I've spent enough time with this now in Claude Code (and Claude.ai and Claude Code for web) to have an opinion on Fable 5: it's a beast. I'm throwing some VERY difficult problems at at - things I've been dragging my heels on for months - and it's crunching through them very happily.
One that I'm willing to share (albeit from just a week ago) - I built a Python library last week that bundles MicroPython compiled to WASM to create a sandboxed code execution library: https://github.com/simonw/micropython-wasm
I just told Claude.ai (not even Claude Code - this was the standard Claude chat interface) running Fable 5:
Clone simonw/micropython-wasm from GitHub
and research how this could use a full
Python as opposed to MicroPython
> It's possible Opus or GPT-5.5 could have done this too, I've not tried the exact same sequence. The Fable vibes are good here, though.
And that's the thing. These comparisons are all gut feelings. I'm missing objective unbiased measurements to actually have real comparisons between different models, their different generations, or even just the convention that everybody adds "you are an expert software engineer" and "don't make mistakes" to their prompts because they think it improves anything. Nobody knows if it actually does.
Vibes are all that matter. As soon as you start measuring it, that measurement becomes a target and vendors start optimizing for it at expense of the general usefulness of the model. We’ve seen plenty of models with great benchmark scores flop when people start using it.
If benchmarks didn’t exist we would have to invent them because “vibes” is a ridiculous idea: oh I know I’ll be super unscientific and horrendously biased and that’s far better than a team of experts carefully AND CONTINUALLY developing a variety of benchmarks of varying quality that…hmm all point to the same thing.
You can’t benchmaxx an eval that comes after your model release.
Consider also benchmaxxing makes no sense from an incentive structure: the quality of these models is directly correlated by how well you can measure true performance in the wild. If they were just stupidly benchmaxxing they would be unable to do trustworthy ablations or know how well the model will perform in their product.
Remember the famous case of asserted benchmaxxing from llama 4? The entire org was gutted and the ceo spent billions hiring better people. Every lab takes evaluations extremely seriously.
> You can’t benchmaxx an eval that comes after your model release
Sure you can, just do it silently and don't tell the people hitting your API that the model is different now. Unless it's open weight, we're just taking your word for it. Even better, do a VW and try to detect which benchmark is running, then change to a hyper specialized model that is trained on it.
> Sure you can, just do it silently and don't tell the people hitting your API that the model is different now. Unless it's open weight, we're just taking your word for it. Even better, do a VW and try to detect which benchmark is running, then change to a hyper specialized model that is trained on it.
This is...just incredibly conspiratorial and a bit silly. You can make a benchmark right now and run it on the models. They'll have a benchmaxxed model on your...previously non-existent benchmark? I mean: if models really were overfit to benchmarks, which zero lab is doing because its idiotic, against their incentive structure, and easy to detect, then why would we see a slow ascension of performance on say humanity's last exam for one benchmark example? You could trivially get those numbers to close to 100% if you wanted to.
Vibes is just UX. There's whole careers, teams, and even industries dedicated to it, and yeah it isn't easy because you need aggregate data from people.
Um kind of but not really, it’s a mix of UX and actual measurements of what tasks it can do. Also UX is virtually the same thing: scaled quantitative surveys and preference metrics. It’s again, just benchmarking, and it’s done carefully and with best practices.
Benchmaxxing isn’t the only problem. Evaluating an intelligence is a task that generally requires at least an equally capable intelligence, if not one of greater capability.
That’s why students are evaluated by teachers with more knowledge and experience than them. It follows that any mechanical evaluation scheme is hopelessly inadequate for measuring the true capabilities of a frontier language model.
> students are evaluated by teachers with more knowledge and experience than them
This starts to break down in college when the professors often at best only slightly ahead. (they have more knowledge and experience - but in a slightly different area and so it isn't relevant to the depth of whatever is under consideration) Grad school is about advancing the state of the art - if you don't know more than your professor you are doing it wrong.
> This starts to break down in college when the professors often at best only slightly ahead. (they have more knowledge and experience - but in a slightly different area and so it isn't relevant to the depth of whatever is under consideration)
I can't speak to the humanities, but this estimation is just not true at most universities in the sciences. (EDIT: As cycomanic emphasizes below (https://news.ycombinator.com/item?id=48477683), the part of the original comment pertaining to graduate education is more reasonable. I am speaking here only of undergraduate education.)
It certainly is true in physics and engineering that a PhD student at least half way through their PhD should know more than there supervisor about their topic (and usually much earlier). Even a Masters thesis project student should understand the intricacies of their project better than their supervisor. I'm speaking as someone who has supervised a significant number of both PhD and Masters students.
The original post said “in college”. It might be true for PhD candidates halfway through their program, but that’s like 0.5% of college students. The vast majority of students are leagues behind their instructors in domain knowledge.
Yes, I intentionally left out the next part of the quote about graduate school, since that seems more accurate. I was disputing only the part that I took to be pertaining to undergraduate education. The full quote is:
> This starts to break down in college when the professors often at best only slightly ahead. (they have more knowledge and experience - but in a slightly different area and so it isn't relevant to the depth of whatever is under consideration) Grad school is about advancing the state of the art - if you don't know more than your professor you are doing it wrong.
> How is this remotely true. You can have verifiable tasks that you can’t do. Where does this idea come from??
That is what benchmarks and intelligence tests are, which are vulnerable to benchmaxing etc. You wont be able to do this by gut feel though, you can create a personal benchmark though.
But point was that personal judgement of intelligence requires high intelligence. Creating a benchmark doesn't require as much but is more vulnerable.
Yet human judgement isn’t subject to side effects like fluency and persuasiveness? It’s like everyone in this thread dismisses benchmarks and then…describes a crappy benchmark.
Sure you can create a personal benchmark. Who will evaluate it, you? How many tasks will it have? How will you evaluate success? Will you know which model is which or will you be blind? Which one will you do first? Ah right, benchmarking.
Also, benchmaxxing isn’t possible when the benchmark and measurements come after the model is released, right?
ya gotta have a vibe for everything if you want to compare vibes, though. you can't just have a vibe for fable 5 alone AND say that it's better than anything out there. there's no weight in that verdict at all, no meaning. it's like reviewing a book without reading it.
throw the same prompt at multiple models and see how far each one gets. change the prompt used in the benchmark every day so models can't be optimized for that one prompt. use your vibe glands all you want, but don't issue model judgements without any ability to compare apples to apples.
Lots of things in life are gut feelings. It would be really great if we could determine quantitatively forever whether Rust is a superior programming language to Go, but real life resists those kinds of measurements.
no it doesn't, there's just no single measurement that will answer everyone's "which is better" question.
Go is better for some stuff. Rust is better for other stuff. Perl is better for other things.
"better" can mean anything, but if you define it, then it has definition, and you can measure it. So, you have multiple definitions of "better" and you use them all when you compare.
zero people have the same weights of the various definitions of "better", even among programming languages; look at how much javascript is written today. JS is not a better language in any measure that is based on rational thought, but for some people "this is javascript and nothing else is javascript" is enough for them to know that javascript is the better choice for their project.
Yes, these are gut feelings. That said, I have lots of experiences with Opus and I have lots of projects and contributions (all reviewed and tested) made with the help of it. Definitely useful, to me and to people whose project matters to them. :P
Adding "do not make mistakes" is silly, in my opinion. There is always a good chance it will make mistakes. You should rather be more specific about a thing rather than as broad as "do not make mistakes" is. It just does not work that way.
There are tons of benchmarks in the announcement. But we also know that benchmarks are problematic.
So the best we can do right now seems to be to combine imperfect case studies like this with imperfect benchmarks to get some unreliable impression of where we are...
Sorry, this post gets me irrationally irritated and makes me want to shake you and shout.
That website is 95% not you, it's AI, and I feel that's causing you to way over-represent the value of it in your response here, or you're completely misunderstanding what the person you're responding to is asking. If you put all of your effort into that site, without AI, it would be infinitely more valuable and useful.
The person you responded to asked for specific things, including:
- obvjective, unbiased measurements, but all that page has is side by side visual comparison of outputs.
- their different generations, but all you included was the outputs
- details on the prompts and little things people are adding because they feel they need to, but you didn't include any of that
This is slop, it's the exact sort of self confirming fluffy AI stuff that other either inexperience or over-invested-in-AI engineers will look at briefly, skim, see quick visual validation, and nod, noting down how much better Fable must be without getting any actual data.
Sorry, it's early, and maybe this is a misplaced rant, but the person you responded to specifically asked for precise, quantitative things precisely because everything else is fluffy slop like this, and people don't even recognise they're doing it any more.
check the backlinks[1][2] in the article before you start throwing around accusations. I am not (yet) a person that has advanced notice and access to models.
Fable just got announced and I did a rush out article because people are curious. I released the post mere hours afterwards and it takes time to create the output, slice into videos, make a wordpress article on top of taking my son to basketball training and eating dinner. I’m in London and this was all happening at 1am.
If you check the links my previous articles have all the juicy stuff you are criticising me for not having with little preparation.
How is a side by side direct comparison NOT precise?
I just read the extra link you provided which has some more information, thank you. Sorry, but the links confirm my points. You're not giving any quantitative analysis of your use of the different LLMs or your process. Your "sciencey appendix" is all about the domain science of pyramids, nothing to do with how or what you put into the LLMs, or any quantitative analysis of the code put out.
I'm sorry, your response has just proved the point that frustrated me: you've either lost or never had the capability to recognise a decent quantitative assessment of technical software creations.
Your entire site is obssessed and fixated on the impressive looking outputs of LLMs, rather than actual quantitative assessment of the quality of the outputs. This is the killer problem of AI: it looks like it's good, and a lot of the time, things that look good are good. It's very easy to make stuff on a computer that looks good but isn't for various reasons, and I nothing in what you've said here suggests that you fully grasp that. Sorry again to be harsh here, this is just my opinion, and we're probably going to have to agree to disagree.
I reads like an unhinged rant about AI and the engineers who use it, with the entitled tone of people who think they have permission to insult someone's competence and work because AI was used.
In my opinion, if one cannot express themselves civilly, they should refrain from commenting.
I disagree. I wouldn't consider it unhinged. I'm clearly aware of my own frustration. It's also relatively civil, since I was able to temper it with appropriate apologies and acknowledgements. Many other people agree and support the sentiment of what I'm saying.
AI is a powerful tool and very capable of - amongst other things - making something look far more valuable than it actually is, and that is a huge waste of time that costs us all. We all have a responsibility to call this out when we see it.
It looks like you've just implied I'm entitled, unhinged, uncivil and and that I shouldn't have contributed at all, whilst thinking you've elevated yourself above that behaviour by saying "in my opinion" and "one should...". I think that's an unhinged, insulting and uncivil way to express yourself.
I found the website you ranted about interesting, comparing the quality of the visualization between the different models.
I don't think it was "a huge waste of time" or needed your rant.
You called it slop and questioned the competence of the author, as if he made grand claims about the objectivity of his comparison.
What I see often is that people assume others are incompetent just because they used AI, when in reality they are engineers no less competent or experienced than others on this website.
This is slop, in the sense that it looks like a lot of useful work and effort, and AI is heavily involved, and it was offered up when the opposite was requested, meaning it's not at all helpful in this context.
I raised this in a harsh, but repeatedly apologetic way. The person then responded telling me to "get my facts straight" and doubled down with more weak, qualitative outputs of LLMs.
I don't assume the person is incompetent because they used LLMs. I use them daily. I'm a firm believer everyone is an idiot, just in a different subject.
The issue here I feel is that LLMs are increasingly leading people think that they're not an idiot in any subject at all, and when real humans question it, they double down with more AI stuff.
Ok but isn’t that true of all software development? It’s not like anybody’s done a rigorous test of writing their entire codebase in Python vs Java. It’s all vibes based there. People create post-hoc justifications for why they use certain technologies but the reality is a lot more vibes than anything else.
fwiw, I gave it the same vibecoding project I'd previously tried with Sonnet 4.5 and it took Fable 2 hours to go well beyond (like, 2x beyond) where I got in 8 hours with Sonnet 4.5. (beyond that idk, because past 8 hours with the Sonnet 4.5 version I hit the "vibe limit" where it becomes easier to just write/edit the code yourself than get the agent to do what you want; and past 2 hours with Fable I hit my usage limit.)
I think (related to the threads below) properly running evals in the state of the art models is likely outside the budget for most individuals. It's undoubtedly the right thing.
It would be very useful for companies to isolate interesting programming challenges in their past and publish evals on them (without revealing the actual codebase). In theory companies adopting these models should already be doing this to evaluate cost/benefit for each model, so it would be a matter of publishing them on a regular basis.
It’s almost like they’re interchangeable. We need to start asking these models to solve extremely difficult, contrived DSA coding questions before deciding which ones we employ
I believe there is hard evidence that role-playing prompts are effective at leading it towards particular strategies and trains of thought. Not sure that SWE has been specifically studied, but proper science is very slow in the context of rapid change and broad context. It's good to stay grounded in the science that has been done, but we're going to have to do our best in uncharted territory for a while.
"Don't make mistakes" does seem dumb. It's not guidance.
Just treat it like an employee with infinite energy. You can never really measure the productivity or ability of employees, it’s just pretty obvious when one is better than another. You’re asking them to do things and they’re either coming up with the goods or they aren’t. You can’t really expect much more from agents either but I’m not sure why you need anything more.
"I have not accepted payments from LLM vendors, but I am frequently invited to preview new LLM products and features from organizations that include OpenAI, Anthropic, Gemini and Mistral, often under NDA or subject to an embargo. This often also includes free API credits and invitations to events."
But I'm totally unbiased on my gut-feeling posts, trust me bro.
Yes, exactly this. If I didn't care about price at all, I'd exclusively use this model. It functions more like an actual engineer. I'm in the midst of a DB migration, and eg 5.5 continually suggests stuff like "use DB X instead of DB Y for task Z because its 30% faster" which is an impossibility of reality, given we are migrating DBs. Fable jumped in, reduced allocs by literally 46x, found multiple bugs 4.8 and 5.5 created (max file system usage, correctness issues, etc), and continually suggested awesome improvements unprompted. As in, it would finish a task and then suggest we tackle this other existing problem I didn't know about in a very specific manner... this is the first model that feels like its coming for my job.
I'm having the same experience. I'm in the process of implementing a new CRDT for realtime collaborative editing. There just aren't a lot of implementations of CRDTs kicking around online for opus or any of the other models to have good design instincts.
Fable is doing - so far - a great job. I just had one big question around how part of it should work. I had a design sketch, but with some big unknowns. I asked fable to figure it out via reasoning and prototyping, and it did - it even, under its own initiative, wrote a fuzzer for its prototype which explored and verified that its reasoning was correct. It absolutely nailed it. And it found, and fixed, a couple bugs that I'd missed.
I'm sure its weaknesses will become apparent in time. But, wow this thing is a beast. Its the first time I'm reading the work of an LLM without spotting obvious weaknesses in its reasoning and code. I'm really impressed.
I was about to ask where you work that you’re implementing new CRDTs and then I noticed your username! Thanks for all that you do!
I work on the live collab at my company, and using AI while coding has into recently sort of “clicked” for me. We use an (I’m pretty sure) unheard of algorithm for collaborative editing, and I’ve had a long term goal of turning it into an implementation of EG Walker, but our document model is very complex and most out of the box CRDTs don’t quite fit. Maybe Fable will be what gets me over the hump.
Long shot here because I'm not knowledgeable enough about CRDTs but maybe something like DSON would help? I saw a talk about it a while ago and it might be useful.
I’d be fascinated to hear more if you’re willing to share. What is special about your document model which makes existing tools like automerge a bad fit?
> wrote a fuzzer for its prototype which explored and verified that its reasoning was correct. It absolutely nailed it.
For such a data structure, "nailing it" means a formal proof of correctness. Fuzzing, as useful as it is, is merely throwing dirt at the wall and seeing if anything sticks.
I’ll ask it for a formal proof when I get home and see how it goes.
I’ve read plenty of papers with “formal proofs of correctness” that turned out to have huge flaws. Machine verifiable proofs I trust. But I’ve personally found more bugs with fuzzing than I have via proofs.
In the real world, many of us don't have the time to create formal proofs. But our instinct in testing where edge cases may exist in code that we wrote is a type of refactoring that happens in our brains during the coding process. Hand the coding off to a machine and you have no idea where to start looking for the flaws.
> Hand the coding off to a machine and you have no idea where to start looking for the flaws.
I have found this quickly becomes false. I have learned I cannot review llm generated code as if it is written by a trusted senior developer (where I often just do a quick look, see nothing obvious and hit approve). Once you start reading the code in depth with the goal of understanding you quickly see the places where flaws are likely. Sure I start with no clue where to look, but it doesn't take long to see things.
I saw scanning the comments and saw you mentioned CRDT. Just wanted to mention that I implemented a CRDT-flavoured sync engine for the product I'm working on a while ago, I think it was with Opus 4.6 if I'm not mistaken (or earlier) so it's not something new to Fable 5, just fyi.
Yeah, you've certainly been able to get Opus to write a CRDT. It just needs a lot of hand-holding to make it correct. Opus always seems pretty bad at coming up with invariants and using them to make a piece of software correct. Without invariants, you end up with lots of hacky workarounds to avoidable problems.
So far at least - and its been less than a day - Fable seems better at this.
I think I also do my CRDTs differently from others. I've grown to like the pure-oplog approach after making eg-walker. LLMs are much worse at this!
It's been obvious for at least 2 years, anyone who doesn't see the writing on the wall simply hasn't learned how to use these well or has severe exponential blindness.
"But it doesn't do well when writing my undertrained language" - yeah, fine. Yet. Reasonable code in that is probably one RAG + verification scaffold deployment around Mythos or maybe mythos+1. Just like it was for you learning it, because you knew how to _program_.
Yeah I agree. We're headed into a rougher job market pretty much across the board for white collar work , hitting junior people worse at this stage.
Up to societies around the world to decide how to deal with this - so far we deal with it by ignoring it it seems.
Gosh, I must be doing something wrong. I spent 15 minutes (of which a lot was waiting while it was thinking about "backwards rationalising" it's decision and "gaslighting"[1]) arguing with it over why it keeps using `node -e "console.log(require('fs').readdirSync('…'))"` instead of `ls -l …`.
Like it did everything:
- this is not a Linux system (true, it was macOS)
- it is not an available command
- the binary is corrupted
- node/js is more precise
- V8 JavaScript is faster than bash (true technically??? But not in this context lol)
- JavaScript is more versatile
I forgot what else we went through but there were a few more things. I indulged it because it was incredulous and funny. The prompts from my side were all questions, never instructions. I assume an instruction would've helped here, but also I don't think Opus ever did this (but on the other hand Opus wrote python scripts to format/indent, instead of just running cargo fmt, so I guess potato potato)
Yeah same here, Fable on "high" is producing substantially better results than Open 4.8 on xhigh for me and my actual real-world evals today. It "feels" smarter and doesn't use nearly as many tokens running in circles. As a result I've been able to run two large refactors today without hitting the context limit danger zones - it's more expensive but also more efficient. It's been able to find some bugs that Opus missed. Pretty impressive stuff.
> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more
I'm working on an internal tool that does new business prospecting data collection, scoring, etc. This is ridiculous.
I don't know if you are aware, but some people reported in Twitter that Fable 5 may flag the message regardless of content if it knows (from either pretraining knowledge or memories) that you work in either of those fields. I don't know if that's your case.
I do some work in laboratory automation and it was quick to refuse the first thing I asked it to do. There wasn't anything spicy in the request, just basic liquid-handling protocol implementation. Their position seems to be that they're too stupid to classify requests safely, and that seems reasonable to me. I'd guess the classifier will improve rapidly.
You miss the point - by collecting and processing medical data they would fall into a thoroughly regulated industry. Not because they may provide you incorrect data, because they are not allowed to process them.
What custom prompt do you have set up? If you tell it you're occupation, does it turn helpful? There was a study that if you tell models they tested that you're a patient, it would refuse, but tell it you're a doctor and suddenly it turns helpful.
Anthropic knows it refuses too much, they want to be very cautious to avoid any scandals. I think this is why they want to store all Fable and Mythos chats for 30 days so they can use the data to improve.
I wonder if it sees Healthcare companies being targeted and that's why it's freaking out; clearly they have some pretty stupid regexes in the harness to detect this sort of shit.
e: I quit the session and went back in. Set it to Fable and told it to continue the last session. It's moving along as if none of that had happened.
Interesting! I have not used Fable, but so far have not hit trouble. I'm a hobby biologist with a home mol bio lab. It wouldn't answer my questions about LNPs, but so far has been fine for my recombinant DNA workflows, lab techniques, environmental DNA protocols etc. I suspect this may become more difficult!
Still does not crack my hardest nuts. Gave it one of them and it blew through my entire allowance on thinking about one question, with no apparent answer in sight!
I see a lot of people saying they are happy with weaker models, but I am the opposite, I need more strength, more intelligence!
I am quite happy that opus 4.8 can do some medium intelligence problems. And maybe Fable 5 can do some more more of those! I have a lot of problems to solve!
I also see a lot of people saying they are happy with weaker models.
At work I had to switch to using GPT 5.4 Mini and Qwen 3.6 27B.
The results were near useless.
The error rate is through the roof, it's constantly incorrect in its conclusions even when investigating very simple issues.
Further the models are too unreliable to even move 20 line snippets around without inadvertently modifying them. Ask them to correct it and they still get it wrong.
Maybe the larger Chinese models are better, but the Mini stuff is next to useless to me.
I have Qwen 3.6 27B and 35B running locally and and coming from Opus it feels like talking to an imposter. Someone who pretends to be competent, but really isn’t. Results are always disappointing. Sonnet is better, but I have given up on asking it. even for simple things I wait for my opus limits to reset.
Not these. I wonder if the well is poisoned there. The models know that these are "unpossible", so it might not solve them just because…
Maybe some day.
I am just testing it on stuff I know intimately myself. I would probably not understand a proof of Collatz if it was dansing in front of me!
That's a bit of a tricky point. I have had quite a lot of problems with models informing me what I am attempting is impossible. If no-one has done it, or at least it doesn't know about it being done it tends to fall back on people voicing their baseless speculations, and for just about anything you propose, you can find a person who will loudly proclaim it is impossible.
The curse of the 'use case' comes in here too. When people think that everything should have a use case, that's a lot of training data suggesting to a model that things should only be used for what someone has already thought of.
A couple of times I have had to manually code proof of concept pieces so that the model breaks out of that "unpossible" mode and actually helps me.
I can't remember if it was chatGPT or Claude, but when I showed it how to get a MessagePort in its JavaScript executor through to the artifact/canvas, it quickly went from "That can't be done" to positively enthusiastic about the possibilities. I suspect those shenanigans will be well off the table for Fable though.
I don’t care to share my exact problems. Mostly because gpt -5.5 hallucinates false solutions, and I would rather not have people reply with "Oh but ChatGPT solves it!", because it takes expert knowledge to debunk them. To their credit ChatGPT will admit their, very fundamental mistakes when pointed out to them. But also because no-one would really care.
I gave a high level description of the problems in a sibling thread. They are the kind of small problems which I suppose every researcher has lying around, waiting for them to think about some day. But not the big problem everyone is waiting for to be solved.
My comment was not meant to be a tease – sorry! I assumed there would be other people in a similar situation, who might relate.
is this a joke? Seriously? These are some of hardest problems in Math period. 100 if not thousands of the greates minds in history have attempted to solve these problems. And you think that the current level of AI can blow through them? It is also a possibility that for example the Riemann Hypothesis is just not provable. (Goedels Theorem).
No one is expecting that! I expect _kb was sarcastic/making a point.
Recently (last couple of months?) these models are becoming useful tools for mathematicians, because they can solve easier problems more quickly, meaning that one can tackle bigger challenges (but maybe not RH et al) piece by piece.
But, there are still definite limits, where one could expect an expert human to solve things, given time, but models do not. Thus, more intelligence would be nice!
The medium ones are results where one needs to construct some object, which my intuition tells me should exist. The difficult ones are typically to show that certain objects can not be constructed.
These are not Fields medal type problems, nor know difficult/open conjectures. Just small stuff I have collected in my todo list over the years.
I have some medium difficulty math problems where I have used the models for the last year and a half repeatedly. Back then they were already good at pointing out obstructions and constructing counterexamples. So that tracks. But at first glance it looks like Fable actually made real progress on one problem for the first time.
A year ago my judgement was that I had wasted my time on trying to work with the models and doing things myself would have been more productive as I would have gained intuition from the failures. Now it definitely seems to have figured out stuff that would have taken me more time than I have to spare on this problem...
One thing I can tell you is you are either favored by Anthropic, or your version of the CLI does not exhaust limits, or there's some major bug, as two people around me (myself included) claim it took half an hour to hit the ceiling. Which makes it practically unusable, where the same workflow a day ago produced a good 5-6 hours of workload with several agents.
Probably means fan, shills have undisclosed ties and I doubt he means Simon has undisclosed ties to the entire AI industry, that would be very impressive if so.
It’s not meant for subscription users; the subscriptions are just the gateway drug to Enterprise pricing which Anthropic intends to use to juice their numbers before IPO.
Monetization is coming. They'll tell companies, AI is replacing your workers, so it is still worth to pay 100K/year for the license, as those AI are not going to jump to other job, get sick, be late, complain, require free coffee and so on.
Soon the times of AI for $20/$200 a month will be long gone.
Get people hooked, tell them spending time coding is no longer needed, let their skills deteriorate, tell them they need cough up for a licence to do their job
Forcing developers to pay for models that were build on code they scraped scott-free
A tax to do their job that developers are jumping at the chance to pay
Everybody's finally realising that node dependencies are a threat, but letting these AI companies gatekeep the industry is a bandwagon people are scrambling towards
> Forcing developers to pay for models that were build on code they scraped scott-free.
Yes this makes me sad behound explanation. Specially when I see open source developers happily using these tools. These companies stole your, free, hard work and charge you a subscription!! Not to speak about them torrenting books and (most likely) training on private repos.
This and devs paying a subscription to use a tool that is marketed as trying to replace them.
I had 150$ monthly budget thatbI used for various open source projects and I've cut that entirelly.
I don't get what you're saying. You're frustrated that Open Source projects were used to build these AIs and that OS devs (or devs in general) are paying to use AI.
Then you say you had money that you used to donate(?) to OS and have cut that because of the frustration?
Open source just means sharing the source code for people to learn off or have the ability to customize on their own. I don't think there is any need to be frustrated about that (now if it was copyright/private of course).
> To summarize the analysis that now follows, the use of the
books at issue to train Claude and its precursors was
exceedingly transformative and was a fair use under Section 107
of the Copyright Act. And, the digitization of the books
purchased in print form by Anthropic was also a fair use but not
for the same reason as applies to the training copies. Instead, it
was a fair use because all Anthropic did was replace the print
copies it had purchased for its central library with more
convenient space-saving and searchable digital copies for its
central library — without adding new copies, creating new
works, or redistributing existing copies.
> Forcing developers to pay for models that were build on code they scraped scott-free
That's also caused by some very smart (even brilliant) developers (you can see many of them in this very thread) choosing to be oblivious about all this and bury us all under, hoping that they'll be among the last ones to go. Writing this down I realise that they maybe aren't all that smart.
I've been saying this since the beginning, the rug pull is coming. If these models can eventually replace a human worker, there is no reason these companies won't charge (and get away with it) very close to a typical SWE salary.
It would not surprise me one bit to see anywhere from $80k-$100k/seat pricing.
A Ferrari will likely lap you when you’re racing, though, and the market and the economy is a race. You’ll be facing a question soon, or your employer will, whether to spend a significant chunk of free cash on fable-class tokens or on literally anything else instead - wages and salaries included.
<< You’ll be facing a question soon, or your employer will
Maybe? If you talk to executives, the impression that I am getting is that they tend to be somewhat misinformed at best, which, yes, is bound to result in some really bad decisions down the road. But, and it is not a small but, the ones I did talk to ( and, amusingly, those are the ones with strong opinions ) don't seem to have a lot, um, practical exposure to this tech beyond what they heard at the watercooler. Honestly, it is kinda infuriating. And all this before we get to how companies want to say they use AI, but also keep cost down.
I am, and I used up the entire 5 hour window in 8min using the highest thinking setting. It also ate up $15 of extra usage before I noticed.
I’ve done the same thing with opus multiple times with no issue. According to ccusage I racked up just shy of $100 of tokens using Fable.
It spun up subagents or workflows or whatever so obviously that contributed but “double opus” was not my experience. I’ve done the exact same prompt with opus on the highest setting and only once before (not even while using this prompt) hit my limits.
My prompt? I’m not a prompt wizard or anything but it was literally:
> Please review the uncommitted code in this repo for bugs/issues/code smells.
I use variations on that all the time with opus and never had issues. I figured it was a good one to kick the tires with Fable. Little did I know it would mean no more Claude Code for the next 4.5hrs (unless I wanted to pay) after this being the first time I had used CC that day (yesterday).
I tried to filter down to just fable (or 5.5 so I could deduct it) but the `--agent` flag doesn't seem to work how I'd expect...
I think the $10.96 is coming from gpt-5.5 since I switched to it once I exhausted all my usage on CC. CCusage reports completely different numbers so I don't know which one of those is right.
Thanks for trying, for yesterday ccusage says "$92.02" for claude, which I assumed was the Fable usage.
That's very interesting, I had not used agentsview at all before today and I'll have to keep that in my back pocket.
Unfortunately it's not telling the whole story. The last message from the _only_ Fable session it monitored was:
> The data layer looks clean — <REDACTED>. Now waiting on the 11-angle workflow — verification and the gap sweep run after the finders; I'll compile the full ranked findings list when it completes.
And my memory jives with that, I could see in the footer that it had spun up 11 agents (though agentsview says it used 0 subagents, don't know if it was "actually" workflows that it spun up?). It's like it didn't record the sub-sessions/sub-agents info?
I'm still shocked that my prompt (which I now can see thanks to this tool) of:
> Please review all the uncommitted work in this repo and identify any issues.
was able to burn so much, so quickly, and, most frustratingly, without actually doing anything useful because killing it was my only option lest it spend even more of "extra usage".
It's as if it spun up a bunch of subagents but agentsview doesn't report on it. I see a tiny bit of Haiku use once I turn on all models (except gpt-5.5).
simonw, if you are not bumping up against the same false-positive guardrail problems and budget consumption that everyone else is, then that is something worth digging into. I would normally say that's crazy but IPOs put weird pressure on companies.
It still does make errors, yes?
Because it is not usable, if we need to verify everything.
AI is only interesting if it can do things that humans can not do.
If you can verify results because you can do it yourself, then why use AI?
It will just bind highly skilled people to do verification work.
Instead these people should do the actual work, results will come quicker.
So AI is only interesting to you / your org / humans if it can do things that you can not achieve.
But if it still does errors, how could we ever know that super-invention by AI is not wrong?
If we can not rely on the correctness of the result, it is not usable at all.
AI must create reliable and correct results always.
That was a very fundamental requirement for computing.
This problem has not been solved.
> AI is only interesting if it can do things that humans can not do.
AI is interesting as long as it can save time and/or money in getting an acceptable result. Anything that runs on a computer and can do "things that humans can do" will automatically end up doing things that humans won't do, simply by virtue of the fact that it runs on a machine that doesn't require sleep, doesn't get bored or demotivated, etc.
Verifying code (to a level where a responsible person is willing to take ownership for it) isn't trivial, sure; but writing the code by hand requires the same level of care, and the fact that the same person wrote it doesn't actually allow for shortcuts (if we're being properly responsible).
It doesn’t get bored or demotivated, but it also lacks interest and motivation generally so it comes with the same pitfalls of having nothing to lose and being utterly unaccountable, (e.g. destructive actions, lying, and being coercive or Machiavellian for no reason other than efficiency in achieving an arbitrary and artificial status of completion).
Humans make mistakes too, does it mean humans are unusable? We accept as empirical fast that most production quality code has 2 - 10 bugs per 1k LoC. According to your premise, virtually all existing software is therefor unusable.
What if an LLM overall starts to make less mistakes than a medium developer, costs less than its salary and is 100 x faster? For sure, the companies that will leverage these with just a few senior devs doing prompting, testing and requirements analysis, will outcompete other organizations.
Humans make mistake then to learn from it. A really good expert would never deliberately copy-paste an obscure solution from the internet, then to ask for forgiveness later.
AI agents do that, perhaps not always, but still do. Now the question: would I trust AI without verifying its output?
Humans also make mistakes in ways that other humans can understand or expect. Sometimes LLMs make mistakes in a way that makes you say “no human would have ever done that”.
There is plenty of work that does not need to be perfectly verified, because the risk is controlled. Prototyping a javascript game for example. Or code that runs just on your local machine where good enough is good enough. I'm sure a lot of you do super important work that needs 100% quality code all the time, but... some of us don't.
One does not need to be able to create it themselves to evaluate if the output is correct. Consider for example that you can easily determine if a meal tastes delicious without being an expert chef, or the fact that NP problems are very difficult to solve but make for easily verifiable solutions.
Got curious and ran a similar prompt with DeepSeek v4 Pro w/ OpenCode
No idea what's going on here but agent tested a bunch of stuff. Then I asked to build a wheel so I can run the command you noted above and it appears to pass
The difficult part here is supposed to be the actual compilation to create the .wasm file ? Or what am I missing here? The wheel is only a few hundred lines of code outside of the Python implementation, and it would seem that the MicroPython version of the project already demonstrates the necessary techniques for operating wasmtime.
That is pretty wild, it took me a hell of a lot more coaxing and persevering to get to a similar point with eryx [0] (we spoke a bit about this before on Mastodon) using Opus, Fable seems to have a more optimistic 'sure, let's proceed as if this is possible' mindset based on your transcript. Looking forward to trying it out for some hairier problems.
Just tried it. Fable is extremely strong. The fact that we can't point to any concrete architectural upgrade is worrying - that means "it just gets bigger" is kind of viable.
To be clear, the jump from Opus to Fable was like the jump from pre o3 -> o3 for me. Very sharp improvement, not incremental. But that could be explained by dummy long thinking times.
It one shot a task that Opus burned hundreds of dollars on to get nowhere. Very tricky semantic refactor, got it right. Granted, again, the semantics Opus and I fleshed out 3 months prior, but Opus couldn't execute on the vision. Fable could.
Then I discussed some philosophy and it was actually both pleasant (GPT constantly "corrected" you for the sake of correction without clarification, also still often just wrong; it's like it refused to think critically about philosphy) and accurate, and actually helped resolve some deep but subtle misconceptions I had around representationalism. When talking with GPT I felt like I was talking with someone who either was sycophantic or "anything that is not absolute truth is relativism" - Fable actually discussed.
Both is exciting and kind of makes me depressed. I can definitely see why people are getting hyped about AGI again. All the models were extremely strong technically but I felt like couldn't match the developer's tacit state - Fable definitely did, and that's a basic quailty to be considered "usefully intelligent" IMO, at least to me.
Shame that it's going away in 2 weeks and probably going to be nerfed if/when it's re-released.
Fable has been producing some really good work on my end as well. Definitely better than Opus 4.8. The only problems are the cost and constant cybersecurity refusals. A single session uses up 100% of my 5h window without finishing, and that's when it doesn't get derailed by nonsensical refusals.
Which has a full build of python to WASM with a bunch of static libs built in already.
I will say I built this pre fable and actually the first build of the interpreter to WASM opus pretty much nailed, cpython has secondary support for WASM as a target since like 3.9 or something and it just pulled from that.
I’ve been meaning to write up a blog post about this sometime, building this has been pretty interesting, including using opus to run a full auto research like loop for days to hyper optimize it’s performance.
I’m hoping to use fable to power some even crazier WASM adventures tho.
Always hard to say for sure because I'm not sitting around running the exact same situations through both models in parallel to compare them.
It feels like you can give it a big chunky problem and leave it alone and it gets it done, with less questions and fewer design decisions that I wouldn't have made.
In reviewing its code I'm finding less to complain about than Opus. But it's all vibes, if you want a more scientific comparison you'll have to look elsewhere.
He has early access to anthropic models, of course he will hype them up, so that they will keep sharing access to preview models with him (and more traffic to his website). It also does't require him to perform any rigorous analysis of model performance, just share how it feels:
> But it's all vibes, if you want a more scientific comparison you'll have to look elsewhere.
I gave it a complete database migration of our app, opus failed hard each time... Untyped Json b for some rows, no proper normalisation, falling back asking me questions in between.
Fable just did it, clean code, one timeout with a hanging bash script, fixed a couple very old very structural bugs in the codebase
So far it's all fitting into my current $100/month Claude Max subscription. I got lucky: I had 80% of my weekly allowance left and it resets tomorrow, so I'm burning tokens to try and use it all up by then.
Update: looks like I've spent $82.92 in Fable 5 API priced tokens so far today (still all included in my subscription.)
Have you seen Fable randomly jump from 50% session limit to 100%? That happened to me a couple hours ago. It was preceded by a bunch of errors about failing to submit a bunch of screenshots.
I haven't noticed that, but I did notice that on a single turn of maybe a few sentences, the cache hit was somehow roughly 500K. Either that's a bug, or there are some truly massive thinking blocks or Claude Code harness system injections behind the scenes.
Per the "Availability" section of the page, seems like should come back to all plans eventually...
* From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.
* On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
* After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
Coding plans are a (massive) subsidy. We can debate until the cows come home whether western frontier models' API pricing rates are fair, but the coding plans are all heavy discounts below those API rates meant to draw people in and get them hooked (and, ostensibly, to be useful for hobbyists or other lower-usage cases).
It's been discussed at length (on this site, on other sites, on like every blog ever, etc) that, eventually, those subsidies will end, much as the $5-10 Ubers/Lyfts I used to take from the far north end of Chicago into the Loop in 2016 would eventually end once those companies had a footing and didn't need to hook folks.
So - yeah, I mean, a v5 model launching in a year where Anthropic has a rather deeply established market and in a year where AI costs are rising from nearly all providers (sometimes for multiple reasons) seems like exactly the thing I'd expect them to pull the subsidy plug on after a launch teaser.
(Even the open-weight models sometimes do this: for example, OpenCode Zen/Go has a rotating door of free models at any given time that eventually leave the free tier and move into the paid tier once the launch day hype/marketing dies down)
The worst part is that Uber "only" lost about $30bn. AI will probably lose at least $300bn by the time the bubble pops. Which means that the pressure to hook and enshittify will be at least 10x as high.
Problem with that website/perspective is separating training costs from inference costs. Training is a one time cost, and while it is certainly not something you can completely ignore, it being one time changes the answer to "Is AI profitable?".
That site doesn't list the dozens of companies doing pure inference, and making a profit while doing so.
Given how bad some of the models do on somewhat similar problems, I'm sure pelican is included in training set now.
Similar problems - given airplane outline and implementation constraints do painting scheme (constraints something like "it will be implemented using covering film, hence no gradients, no impossible cuts, not more than 2 colors on engine cowl, etc). Google Gemini is meh, but GPT models are just terrible, don't have Anthropic subscription at home, hence have not tested.
I guess my comment got lost in translation. The project OP linked in his comment is a toy project, not a difficult problem as he led others to believe.
So you could have done it in your sleep, with your hands tied behind your back. Got it.
(You may not realize it but simonw is one of the cofounders of Django, Python's web framework. If they find a Python problem difficult, it probably is.)
I recently switched off Max flat rate to Enterprise API pricing and I went from 200/mo to 10k/mo with the same usage pattern on Opus. They don’t offer flat rate to enterprises.
So Fable would cost me 20k/mo at Enterprise rates. That’s around the average cost of a loaded SWE in the USA. “But I’m >2x more productive” doesn’t justify doubling the opex of the Software/IT department for most companies when revenue isn’t even up 10%.
I switched to DeepSeek v4 Pro with OpenCode and am on track for a few hundred dollars of spend this month.
Rewriting your stack from Ruby to Go in 2 days where it would’ve taken 6 months is impressive and fun. But that isn’t upping revenue.
Iterating on net new business features and ideas that are niche that the LLM isn’t trained for are much harder. Is 20x the token cost worth it there?
I don't live in USA. I'm getting paid around $2500/month and that's good salary for developers here, plenty of folks are getting below that number.
So this pricing is just completely outside of our economics and nobody I know would pay that, no company will justify spending $20k/month when they can hire 10 more developers instead.
It is very interesting unfolding of events. Can't wrap my head around it completely.
I'll add a concrete example from a not-too-cheap-anymore EU country: Estonia.
* Average software dev salary in Q12026: 4945€ / month [1]
* Total cost for the employer: 6616.41€ [2]
For $20k/month, you'd get 2 x full time mid-level developers + 1x junior dev or QA.
So the calculation becomes: which option can produce better results for your specific use-case, "you + Fable" or "you + 2x mid-level developers + 1x QA". (and from personal experience, mid-level in Estonia = senior dev in the US, in terms of skillset and experience.. but YMMV)
(Of course that's simplified. Your full time devs need _some_ level of AI subscription as well + hardware so add a couple of hundred to their salary per month etc so you might only be able to afford 2x mid level devs, instead of 2.5)
Well you can just scale your AI employees up and down as much as you want. Companies already pay a large premium for freelancers just to be able to fire them on a whim, so spending 5-10k a month on something that more than doubles the productivity of a senior developer might be well worth it as you can just adapt spending based on your business needs. If you can deliver a feature that lets you write a 100k invoice with 10-20k of tokens within a month or have a senior dev crunch that out in 6 months instead I think it's clear who wins. It's all about money and the AI companies know that, they have their pricing down exactly to sit in the sweetspot where it hurts just enough that companies can still afford it but not enough that they would look for cheaper alternatives.
I'm currently working for an Estonian startup and we pay quite a bit more than that. We hire remote (primarily across Europe) and our biggest issue is finding the right people. You need to consider AI can be "hired" or "fired" instantly too, so it's better to compare it to contractor rates, which start at around €350/day or €7000/mo (20 working days) in Europe.
(Our team spend on AI devtools comes out to around $1500/person/mo)
This is a good start, but the calculation doesn't include office space and overhead (for every 100 developers there is maybe 5-10 support staff to cover the additional legal / administrative, and don't forget the extra cost in supervisor time to manage them)
In Sweden I always heard the figure to double the income of the person to get what the company actually pays, including taxes and "employers fee". I know this has gone down a lot in recent years, also not sure if it was ever exactly true, but likely very close anyway.
Hitting the first calculator I found gave me 50 kSEK costs 69 kSEK. So far from double nowadays.
In the UK, a £45k/yr employee pays their own tax and gets a take-home of £35k.
The employer pays £6k for National Insurance (atop the employee's NI contributions). Pension: 2-3k. Apprenticeship levy is £300. 3yr-amortised recruitment fee is £4000. Hardware costs: £1000. Office space £5000. Software/tools: £2500. Benefits: £1500. Training: £1000. Other admin overheads £500.
You pay that person for ~250 working-days, but they only attend for ~220, due to annual leave and sick pay, so you get around £62k worth of attendance out of that person in exchange for £70k, of which the employee sees £35k.
But that is 20% not 100%. And in most non retarded countries brutto is actually brutto, because there is no need to lie to people about how much the government takes away
In the UK, employers pay a stealth tax of 15% (recently increased from 13.8%) on top of the quoted salary minus the first £5k (recently decreased from £9,100.)
So your "£50k" salary actually costs your employer £56,750, and that's before all the other expenses mentioned elsewhere in this thread such as hardware, office rent etc.
A quick google tells me that software devs usually count for 20% to 40% of the total workforce in a software company. The rest is overhead that increases with every added dev.
And if one were to compare cost of a dev vs cost of an LLM, the dev comes with the cost of workspace, computers, sick pay, summer party, conferences and etc etc.
In the US, over and above salary, payroll taxes add 7.65%, pension contributions might be up to 5%, and employer healthcare and other insurance contributions can be in the thousands, plus other benefits, equity compensation, and per-employee software licensing, and lots of people just estimate 2x salary as the “total cost” of an employee, although that probably overstates it a bit.
I work at a smaller tech company (<300 people), and my friend showed me everyone's spending.
Our top user is at 10k a month, but the next highest is $2,000.
I would say the average is around $1,000-$1,500 for a developer.
We have completely unrestricted access to Claude, Codex, and Cursor.
Funny enough, the guy spending 10k is not even a dev by trade but an SME in what we work on that just vibe codes apps and somehow has not been cut off yet lol.
I have a single thread of GPT 5.5 medium running basically all work hours and I am around $1,500 a month in spend on Enterprise pricing.
At my company, most devs are under $1500 a month as well.
I’ve heard of a few cases of devs racking up bills fast, but it has typically been due to inefficient context usage. Like they just have one super long session with Opus 1M and are getting killed with input token costs and cache misses.
With careful context management and some thought into good approaches to problems, I have personally only rarely even hit $1k in regular use.
> Funny enough, the guy spending 10k is not even a dev by trade but an SME in what we work on that just vibe codes apps and somehow has not been cut off yet lol.
I'm guessing he's producing pretty valuable work. We have a few SMEs that vibe code tons of stuff with Claude. The only thing they really need tech for anymore is deployment and helping get their wheels unstuck on occasion.
Interesting! Would it be fair to say your company spend $100k to $150k per month on this?
Multiply this times many, many companies, and you can see how providing AI could theoretically be a good business to be in. Margins may be tight, though.
Also -- I'm convinced someone will figure out more use cases beyond software programming, which will result in many more companies spending $1k+ per employee per month.
It remains to be seen how much of this is a bubble.
I think you are broadly correct, but just to pushback on a few points:
(1) Ability to solve hard problems in days vs weeks as immense value
(2) Back-end improvements (if done right), should improve platform speed, stability, scalability etc. which should have revenue implication
(3) Ability to on-board a SWE equivalent entity in minutes, have them work on a specific hard problem and then off-board them in minutes can have value
All of the above, of course, depends upon Fable consistently being a 2x-3x SWE at minimum.
You're not really solving problems, you're retrieving the best match of solved problems from compressed corpus. And that corpus is available to many companies, meaning "hard" problems stop having "hard problem" value the moment they enter the weights of any model via the internet ... or distill from one model to another. Anthropics business model is commoditising knowledge, but as we see with the Fable model card, they only want it done to the knowledge of other businesses, in their own field, they totally hate it.
I don’t think that’s an accurate or useful characterization of modern AI like Claude at all. It is not simply regurgitating knowledge. It applies its knowledge to create bespoke solutions to the problem you pose to it, and is able to self evaluate its progress towards the completion criteria. If you don’t think that counts as “problem solving”, your definition would exclude nearly all knowledge work and engineering.
> It applies its knowledge to create bespoke solutions to the problem you pose to it, and is able to self evaluate its progress towards the completion criteria.
It imitates applying knowledge. The imitation may be uncanny, but assigning LLMs intentionality and ToM is a category error.
Does "applying knowledge" necessitate human-like intentionality and theory of mind? If you insist it does, and this is a category error, then we need a new category.
By analogy, consider that many have referred to classical, deterministic computing as some kind of "thinking" for the last half century+. Does this stop being kosher when the computer has an uncanny propensity for human language? Perhaps, but the computer is still clearly chewing through problems that would have required a lot of human thinking (e.g., arithmetic) in ages past.
I haven't seen any genuine proposals for words to replace the human mind analogues, let alone proposals that the anglosphere would plausibly adopt en masse.
People underestimate the vastness of training data (internet) and overestimate their ability to recognize if something is really bespoke. Not to say the no problem solving is happening, because there are many problems that we inefficiently solve again and again and the LLMs are making the solutions more accessible to everyone with a subscription.
> You're not really solving problems, you're retrieving the best match of solved problems from compressed corpus.
This is not correct. LLMs interpolate in a high dimensional space, so you're actually composing the best matches in a compressed corpus to find novel points/paths in that space. That is problem solving.
20x the cost means you need to have fable to be 20x better than the alternative, which is a tall order. And there's more options out there too, perhaps the 4x cost is enough.
This means if the deepseek / under 1k alternative is at least x1.2 improvement, fable needs to be x24, which I think is very2 unreasonable. It is possible for it to worth if it can x2 a $20k SWE, though I doubt it can do that.
“Ability to solve hard problems in days vs weeks as immense value”. Citation needed.
LlMs are incredible don’t get me wrong, but they are good on tiny contexts (writing a script). Not on software engineering (adding features to Chrome).
Honestly, LLMs been OK at adding features to software since around Opus 4.5. From what I've tried of Fable, it's a decent step up from the Opus models and I can only see things getting better.
The thing about AI-generated “solutions” is that they often go down bad rabbit holes and need to be re-run, or since they are so “cheap” to create they are often just thrown away and rebuilt when requirements evolve. Plus, just more stuff is created and needs to be maintained. So in the end, your efficiency gains go out the window.
No it doesn’t and will not be. Companies have not realised the cost yet, wait till the end of the financial year and you’ll see a different direction.
DeepSeek v4 is pretty decent, and probably on par with sonnet. I see a future of hybrid models where opus or fable might be used only for complicated features or bugs, but general day to day would be DeepSeek or whatever good models that will be released later.
>I switched to DeepSeek v4 Pro with OpenCode and am on track for a few hundred dollars of spend this month.
I was about to say that. Deepseek is just magnitudes cheaper and absolutely good enough for most things. Anthropic and co just try to milk the cow while its possible. If they cant compete with Deepseek pricing I do not see a bright future for them.
Its too bad my boss views China as the big evil country so he wont ever make the switch to Deepseek but then proceeds to throw all our data to US companies like OpenAI or Anthropic...
I recently switched off Max flat rate to Enterprise API pricing and I went from 200/mo to 10k/mo with the same usage pattern on Opus. They don’t offer flat rate to enterprises.
So what keeps your management from just buying everyone individual flat-rate Max subscriptions, or at least buying them for the users responsible for the sky-high token invoices?
I see a lot of comments like this but I don't understand why some people willingly pay so much more than others for the exact same service. What are you getting that I don't get as a $100/mo Max subscriber?
With GPT 5.5 on the $100 plan, it's hard to hit any 5h/7d limits - while allegedly being better than DeepSeek 4 pro. Not sure why, or how you spend "a few hundred dollars of spend".
With that said, I still had the Pro plan on Claude, I didn't expect much, but it blew up my 5h allowance on Fable with one simple single prompt, and it didn't even complete lmao
It's not just Pro! I have Max 5x and Fable absolutely blew up my 5h window. Didn't complete the code review either, and got downgraded back to Opus 4.8 on the really important memory safety parts I actually needed it for. It's an excellent model but Anthropic's not providing a good experience.
I'm on $200 plan which is supposedly 20x usage of $20 plan. With few Fable prompts (I'm working on u-boot port) I got 10% of my 5h usage, so that's already 2x of $20 plan usage and that would be 40% of $100 plan.
So Fable is just not usable for $20 plan and barely usable for $100 plan.
I am because CEOs are. Look where the puck is going. Sorry to update your p(doom) priors in this way, it was obvious to anyone paying attention years ago conditioned on uplift trend persisting. Trend persisted and here we are.
Welcome to the new world. People start to repeat what tech founders preach. They do not require humans in the mix. Peter Thiel gave a good example of that mindset in a (mostly) recent interview where he didn't have an answer on "Should humanity survive?"
Yes. Hiring people has various benefits, I will lay them out for you:
- They learn the domain of your product, which means long term ownership and knowledge establishes itself. If you've only ever shipped SaaS slop, you might not know, but lots of companies are solving real world problems that have no better solution. Owning and understanding the code and the domain is key.
- They will learn from their mistakes (no LLM does this).
- Human skill is a REAL moat. Once you build a team that fully understands and is skilled in the domain you work in, these people are going to be the thing that sets you apart. If some of them are particularly social or charming, let them sit in with you for meetings and watch them provide loads of value, for no added cost.
- If Claude or OpenAI is down, they will continue thinking. In fact, they will continue thinking even when off the clock! This is a neat little hack called "consciousness" where you get a lot of work for free!
- You can hire people who punch above their weight; not everyone you hire needs to be a 500k/year staff software prime engineer of doom, you can just spend some time and effort to hire good juniors/competent mediors who will think for themselves (gasp!) and get work done.
- You still get ALL THE BENEFITS OF AI!!!! They can use AI just like you can, or better!
- You get people who you can brainstorm with, which is distinctly different from LLMs because your employees are less likely to want to suck you dry in every sentence just to make sure you spend more tokens. Employees don't care if you love them, they care about the quality of their work if you manage them correctly and reward that.
- They are quite loyal if you treat them right; spend a little more on their well-being, and they will stick around, come in to work every day and deliver cool things with you.
- Humans can only manage, review and give tasks to so many agents. If you add more humans, you can handle more agents.
An expensive LLM and a lot of extra tooling gets you some of this, yes, but not all of it. With humans you can still do the expensive LLM and extra tooling if you end up making enough money anyway.
- AI isn't bound by need for rest, vacations, sick days, or labor laws
- AI doesn't bounce from company to company, taking your business knowledge with it (actually this isn't technically true based on the practices of AI companies, but that's not a technical requirement)
- AI doesn't join a union and stop work in demand for higher pay or workers rights
This is what CEOS and capitalists are thinking. For capital, the best outcome is to not have any labor at all. And if you can do that when your competitors can't, then you have a huge market advantage. (Slop notwithstanding)
I'm not saying this is a "good thing" but this is what drives the market. Less labor revenue in the long term and money printing machines.
The issue is, of course, that the quality of work is not good, and this will eventually show itself, likely in the total collapse of the US economy, but until then I wish them good luck with this.
> In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
> Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
Could this be legally construed as anti-competitive behavior?
Edit: I asked Claude. It replied:
> Consumer protection / deceptive practices. In the EU this would be a clear UCPD (Unfair Commercial Practices Directive) issue and potentially a DSA violation. In the US, FTC Act §5 prohibits "unfair or deceptive acts." Selling a product that secretly performs worse than advertised for a commercially self-serving reason, without disclosure, is textbook deception. The Samsung/Apple battery throttling cases are instructive here: Apple faced regulatory action across multiple jurisdictions specifically because users weren't told.
> Competition law. This is where "anti-competitive" gets complicated. Refusing to help competitors build competing products via your ToS is generally legal — you can decide who you license to. But covertly sabotaging output quality for a class of users while charging them full price crosses into different territory. Under EU competition law (Article 102 TFEU), if a company with dominant market position uses covert technical means to disadvantage competitors, that's closer to abusive conduct than a legitimate ToS restriction.
I think either you've prompted Claude misleadingly, or it's interpreting the law unnecessarily prissily (which is a failure mode I've noticed LLMs falling into).
This clearly is disclosed, otherwise how did we get to know about it?
What's not clearly disclosed is when you are being limited and what the bounds are. If you are developing ML kernels for a computational photography use case will the safe guards miss-fire and sabotage or slow down your efforts? What about distributed GPU interconnect work for a nation super computer lab used in weather simulation?
The reason they are doing this shadow ban style technique, is they don't want users to figure out how to jail break their way out. Or the explicit direct bad PR of when it miss-fires.
They already have though, no? If we lost access to every model permanently besides Qwen tomorrow, would we really be limited by AI in what we could achieve in the future? Sure, it might be slower and take a little more work but it seems like the cat is already out of the bag.
The argument against security through obscurity isn't that it doesn't work at all. It does to a degree, only it is not as strong as people think.
An example from the meat world: not publishing your vacation dates well in advance for the world to see somewhat reduces your chance of being burglarized. That is security by obscurity; not reliable, but not completely inefficient either.
But if you live in a fortress (security by key material), you can well declare your vacation dates without running the risk.
It is an access issue. If you could get step by step instructions on how to modify a virus so it kills all people over 6ft you bet your ass there would be people attempting it.
Column A, Column B. Building a small explosive device isn't hard. Building a million is very difficult, doing it covertly virtually impossible without the resources of a nation-state.
The problem with biologics is the self-assembly and replication machinery comes for "free." So the numpties who might otherwise blow up a trash can [1] now have a real chance of taking out a million people.
They would still have to procure things that would (I hope) light up many screens before they're able to. And such numpties are probably already monitored, or in prison for some other stupid life decision.
I also would like to hope that people that are likely to do such things are probably:
A) don't know how to break even the most basic guardrails of models
> They would still have to procure things that would (I hope) light up many screens before they're able to
“Many of the largest and most responsible providers in the industry already screen and record orders voluntarily,” but there is no requirement to do so [1].
I don’t know if you’re being silly but it is orders of magnitudes easier to modify an existing virus to selectively target certain snps than make “antiviral nanobots”
Claude, modify the existing 6ft killer virus so that it only makes my balls itch slightly for a day and gives me lifetime immunity to all further stamms of the 6ft killer virus. Make no mistakes, double check so the virus causes no unforseen complications.
If that were possible, they would already be attempting it with the same level of ability as if they didn’t have access to a text file generator app. It is not about access to the information.
All of this “guardrails” handwringing is nonsense. These things output text. Are you for censorship of a book written by a biotechnology expert that gives out the exact same information?
It's inevitable. Also, it's not like I get to vet who does or doesn't have access. Blind trust in the current selection made by an unregulated corporation just makes me anxious.
Security in the form of "pay to play" is just kicking the bigger issue down the road.
It the tool was made available to anyone to build a virus, anyone would be able to build counter measures, if only a select few people have access they get to build the virus and everyone else is at a disadvantage. So, yes, I am leaning towards making these tools open rather than gated behind some priesthood and government that gets to wield exclusive power.
Compare the cost/ease of attacker vs defender if one person is given a virus to unleash anywhere in the world and another person is given a vaccine to distribute to the whole world. Or compare building a large bridge to someone disabling that bridge, etc. Prevention and repair is almost always more expensive than vandalism.
I don't think there's an ideal solution here, but giving trusted people access to fix security issues before giving it to the wider public seems like a reasonable compromise. They're letting you use the model for all other uses.
you need a lot more than the nucleotide sequence to make a virus. you need the DNA or RNA to be synthesized, assembled, packaged properly. and long sequences are pretty hard to do. you need a lot of equipment, or you need to order from services. the oligo synth services can harden their KYC and/or screen for suspicious sequences.
sure, a malevolent state actor could swing it, but they could make a bioweapon without Mythos's help already.
also, vaccine production and disease surveillance have ramped up very quickly. they will ramp up further, despite political setbacks. it's a cat and mouse game that favors the defenders IMO.
but the bioterrorism narrative is useful FUD to spin open-weight models as existentially dangerous. I am far more worried about Anthropic's own goals than the goals of some crackpot in a shed.
> it's a cat and mouse game that favors the defenders IMO
How so? I'm actually against most of the "safety-tuning" that anthropic does, but this seems fundamentally untrue, a close analogue being video game cheat development. I think in general the cheat developer has an advantage and the cheats generally proliferate for quite a while before being patched.
Video games are an interesting analogy since they often trade security for performance, trusting clients about world state quite a bit.
Finance and biology do come across as two similar high level systems. But while we can employ KYC, fraud detection, and various auditing techniques to finance, I don’t know what you do for biology. You can easily run an algorithm over every transaction a person makes in their account but there’s no equivalent for every cell, every bacteria strain, every virus in the human body.
(disclaimer: layperson remembering how the immune system works.)
the adaptive immune system effectively does KYC by checking the antigens presented on the surfaces of cells. the thymus selects for B-cells (iirc?) which don't react to a corpus of the body's own antigens, but cover a wide library of everything else. when it sees something it doesn't recognize, it reproduces, warns the rest of the immune system and marks targets. that's why our immune systems can eventually conquer almost every pathogen we encounter, if we can survive long enough for it to do its work.
but the KYC I was referring to was KYC that vendors of oligonucleotides (should) be doing, to keep people from ordering nefarious sequences.
I'm bullish on mRNA vaccine technology to release the "patches" much more quickly. there was widespread resistance to this during covid, but covid wasn't horribly lethal. if airborne Ebola spread as productively as covid, for example, I doubt there'd be many anti-vaxxers left (one way or another!) the acceleration of biology research that might accelerate pathogen development should also accelerate the development of broad-spectrum mRNA vaccines with high persistence.
also, afaik the most effective way of developing pathogens is through serial passage through humanized mice or something like that - directed evolution at a small scale, selecting for traits. AI simply isn't needed for that. I don't think information or intelligence has been the bottleneck for bioterrorism, it's motivation and resources - same as for any other kind of biology research program.
It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?
>We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
At the scale of API requests that Anthropic sees, I think the affected organization count might be substantial, and they might not be getting the full model capability that they're paying top $$$ for.
I have no idea how you came to that conclusion. Unless your training pipeline involves actively querying one of Anthropic models, no they can't. And if it does you're distilling their model.
The crocodile tears of companies who've hoovered up everything possible, regardless of permissions or legality, now crying that someone else is stealing their hard work is comical.
I don't even think they can believe it themselves, it's in reality they are just trying to throw fear, uncertainty and doubt about potentially cheaper offerings.
Crocodile tears "is a colloquial term used to describe a false, insincere display of emotion" [1]. Defending yourself against an attack vector you just exploited is between savvy and hypocritical.
I think his use of crocodile tears is appropriate, anthropic is feigning a false sense of concern for safety when really it is anticompetitive behavior, and I think that selfish entitlement is related to the original act of intellectual property theft to use the worlds training data, most of which was not public domain, to distill the wisdom for their models. So why do they get to cry about people distilling the knowledge from their models that they themselves distilled from the worlds knowledge?
That is not what their policy states. It specifically says they will sabotage even non-distillation attempts, such as distributed training pipeline design. And given that they are so far very nonperformant in classification accuracy, expect it to randomly include far more topics wide of the mark.
The fun part is that you will never know if your neural net classification project is getting silently sabotaged because their classifier doesn't work!
Good luck understanding it and finding malevolent inefficiencies if it’s already necessarily better at optimizing training pipelines than everyone except some Anthropic and OpenAI employees. Not a new thing either, see fast16.
Opus 4.8 (or a classifier in front of it) flagged my account and refused to comply when I told it to kill the process. Reasoning summary was complete bananas.
With this in mind, I don't want model to be proactively instructed and encouraged to sabotage without telling me.
AI vendors’ idea of safety has always been safety for the interests of the AI vendor in question. This is not a new development, though this may help more people realize it.
This feels less like an "we are worried about security" and more, we are in the lead and plan to keep it that way until its too late. In someways its been helpful that openai and anthropic are tipping their hands about their anticompetitive instincts and willingness to steamroll their own clients, customers, and society. But it does feel like its too late to stop this. The advantage people get by using these tools is too tempting to resist even if it is self defeating. It feels like watching people light their own house on fire to stay warm in the deepest, darkest days of winter.
Or, they may Mythos seem mystically powerful in advance of the IPO, and are pumping the token use count. But it worked, there is a frenzy for this release in way that is more intense than any previous release.
Anthropic is doing a better job with their model menu, most people I talk to know immediately that Opus > Sonnet > Haiku but cant tell you what the rank order of open ai models are, when to use them, etc.
Just so everyone is aware. Anthropic has been sabotaging AI researchers and their codebases and shadow-nerfing accounts for several years at this point. This isn't new, but they hadn't disclosed it until now. Likely because it is getting to the point where it's too noticeable, or they're concerned about it leaking from employees.
So that's a possible reason why my specific Claude Opus instance seemed to be impossibly stupid and always degenerates into doing really dumb things to my code!
I am building a coding harness, and I see evidence of them doing this with agentic harnesses and scaffolding. It feels clear to me that as they expand in to the app layer, the window of using their API to build agentic apps is closing, they will steal your ideas, implement the product and then close the gate. I am creating my own inference stack because their incentive to block competitors is becoming super clear.
No offense, but the sad thing is, everyone and their mother is working on this same problem. I'm also building a harness. It's feeling like, there is no moat, there is no way to get ahead, they will steal your idea one way or another, if you ever make it public.
No offense taken. I am not building it for fame or profit.
I built it because I wanted cursor on my phone because I have two small kids and don’t want to be chained to my desk. And it’s awesome. It’s a full ide with agent chat, terminal and file system running in a remote Linux container. I can review diffs, fully manage git and preview/serve apps. And no one can ever take it away from me :)
I am watching the way things are progressing with the ai api vendors and it feels really clear that depending on them will soon be dangerous. So I an furiously building as much of my own infrastructure to capture some autonomy with these capabilities
When they launched their business model was to be a pure API for intelligence. Then when everyone claimed they were just commodities with no moat and they shifted hard to being the app layer. That was the transition.
They went from selling shovels to all gold prospectors to stealing the information about the location of the gold so they could dig it out first.
We are all stupid enough to keep buying shovels from them because we think their shovels dig gold better and faster.
> Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT).
Am I to understand that this is essentially their form of social-platform ghosting instead of banning?
So they're not even going to tell you that the question you're asking is against their rules, they're just going to twist up your question and/or the answer somehow such that you waste your time essentially?
It seems like I ran into this EXACT same functionality from Claude many months ago when I was trying to ask it to research on the web and help me setup the ideal llama.cpp config for local llm inference.
Funny how lost it got through that relatively simple install when we had all of the documentation in the world (and a human dev with 20+ years experience guiding it along) to go by... and simultaneously it's debugging and building high level cryptography code in rust in the other terminal tab.
I have encountered this too. I am building a coding harness for www.propelcode.app and it was working really well until the claude code leak and then all of the sudden it seems almost intentionally stupid or outright manipulative in guiding me down wrong paths. At this point I am using other models for anything related to the tool use design and implementation and bought three mac studios with 512gb ram to run large open source models.
This experience has made me feel like we have to create a community that moves AI from the mainframe era to the PC era quickly, or we will end up serfs.
I had Claude walk me through getting local LLM models running on my Mac a month or two ago and so far as I can tell it was intentionally helpful. I even stated the reason was to have an uncensored model for myself and it had no objection. Long story short LM Studio running a Heretic Gemma 4 is doing just fine on my system now.
I've had the same bad luck with tool-calling on Gemma4. Looking around the web, we are not alone. For other tasks, it's seemingly quite quick and decent.
But it gets stuck in tool call loops, it seems like.
Oh to be clear I don't think Gemma 4 is suitable for real work. It runs at 10 tps and is somewhere between 4o and o1 in quality according to my subjective judgement. But Claude was happy to correctly tell me how to get it running and how to solve the pitfalls I encountered in that process.
I am a AI Researcher at a university. I tried Fable for my current project, but i feel it missunderstands me a bit to often. Now i don't know if i am using it wrong, or anthropic tries to slow my research. That model is a big no no.
3 months before asking for what to eat before a linear algebra exam trips the machine learning topic ban is my guess. I got flagged immediately asking why my JEPA thing breaks weird.
How do they detect whether an experiment being done on a smaller model is used to improve a competing frontier model, or just an innocuous hobbyist LLM experiment?
infering the surroundings, like everything else. they will probably look at which company is your email, and if you wrote "better than claude" on the readme.md
this is LLM, it's not like a science or something.
These things are like encyclopedias or dictionaries that can speak in first person… Imagine if your encyclopedia tried to hide entries from you, just absurd!
But Chinese models will poison your output if you ask them about Tiananmen Square! That's not good, so poisoning everyone's output without telling them is the only way to prevent that.
Come on guys, why can't everyone just be there for the good guy?
• My most noticeable immediate jump was in how its frontend design was much more intentionally crafted, and delightful without feeling like 'AI vibe coded'; with better end-user usability too.
• In some internal agentic harnesses, it achieved better results with about half the tokens, making it cost the ~same as Opus 4.8 price-wise! The real price increase is less than 2x; with biggest differences in harder problems where Opus 4.8 struggles (or needs many turns).
• Part of the token efficiency improvements come from Fable doing more targeted and surgical diffs, with less non-necessary changes. This is great, because PRs often have less LoC changes for review. It writes more maintainable code without explicit human steering.
• For general conversation and assistant style use cases, didn’t really notice a difference vs 4.8.
• 1M context window, without increased pricing for long context is AWESOME. This is a massive win.
• The classifiers are super aggressive and sensitive and this does happen for very benign, non-security coding tasks. Fallbacks to 4.8 worked like a charm; but the filters are definitely super sensitive.
Overall, I would describe this as a step change and worthy of the "Claude 5" model name. It did take some time to understand the intelligence ceiling of this model; and even with an extended testing window I'm still discovering new things and often surprised (in a good way) by the model.
I just ran it on a tough reverse engineering problem I'm having that neither Claude Code 4.8 or ChatGPT Codex 5.5 could figure out. 30 minutes later Fable has it all figured out perfectly.
I’ve so far been successful at getting Fable to find security issues, but I’m careful to not prompt it too directly. I point it at my server code and tell it to find general issues, which has so far resulted in discovering a few minor bugs that Opus has never raised under similar conditions.
I want to test how it will handle e-bike software and hardware RE for my bike. Opus was really good for that, but still made some mistakes. With Fable, I hope I will be able to do a total RE of most components, hopefully including motor firmware to some extent.
I had a similar experience. I have a complex RE implementation that has. A lot of layers. 4.8 struggled for weeks. 40 minutes on Fable and I may now have the most performant way to play Tomba on the planet.
Yeah I threw my hardest problem at it as well, some convoluted satellite tile reprojection and culling issue in canvas rendering. It took some back and forth for some specifics but it ended up writing a quarter of pyproj in JS from memory and the end result straight up works lmao.
I’ve had it go through a 50-page PDF of dense, inter-connected specs, and it correctly flagged everything that was done, somewhat done, and missing. It went into a lot of detail and explained where the code deviated from the spec.
It felt, at least for me, light an impressive step up. Opus 4.8 was already very thorough; but sadly verbose and ‘loopy’ when you push back on its plans. Fable is what I’d use all day if I could afford it!
"incredibly" is doing a ton of work here. I do not think its doing even moderate work on visual design, but it can spew out a lot of ui that looks arranged ... ok.
This is still not in the range of shippable UI for top end companies. Maybe for internal tools and enterprise.
At our comapny we limit to protoypes at most and even find it limited there.
Look, I don't want to argue about something dumb like that, but you can give it basic instructions of what the UI should look like, how to group things, and an example image from a designer, and it will nail the result. If you don't think that's incredible, that's fine. I do.
>This is still not in the range of shippable UI for top end companies.
Given the shit we've seen shipped by "top end companies" (all the way to Apple) I seriously doubt that. I'd say you're nitpicking from an artistic point of view or something.
Might need some additional prompting? I haven't tried fable but gpt 5.5 and gemini 3.5 flash are... Ok on first pass but if you're specific about what you want they can usually get it.
Yep. We have some interesting problems, like getting LLMs to create/edit Canva designs in our own proprietary format, which isn’t published or documented on the web. So the model has to work with it, purely from a very detailed system prompt spec / in-context learning.
I assume it might be a good barometer for generalised intelligence; esp in the visual space.
From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.
On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
This seems like the pharmaceutical method of get them hooked on the drug with free samples, then once they can't live without it, raise the price. I'm not sure I want to start using Claude Fable on a max plan if it's just going to go away on June 23rd.
But maybe the more charitable reading is that they didn't have to offer this model at all on those plans and they are giving the standard free trial.
I was just thinking this reminds me of the scene in The Wire where Avon admits to D'Angelo that the new heroin is in fact just the old heroin with different baby powder cutting it.
Or maybe its all about compute availability like they say. It could be that they plan to start training a new model on the 22ed, so the amount of compute available for inference will be greatly reduced.
I was just saying last week: If Opus 4.8 max is as good as we get, and we plateau there, I think I'd be fine with it.
For the stuff I've thrown at it, that configuration has done a really great job. Including 70+KLOC go proxy with extensive test suite, some retro games, and more.
This is the entire business model of all AI companies. It costs far more to run the datacenters and build more capacity than they could ever hope to make back at current pricing models. I'm looking forward to pricing to catch up with reality and the resulting chaos that ensues.
Kind of how DeepSeek v4 dropped their pricing? I sense a shift which will hopefully bring lower and lower cost. Then again Qwen3.6 coding has been all I've needed for my projects and I'm perfectly fine with free.
Are you paying attention? These companies are trying to get market share without being anywhere close to making a profit - they are heavily subsidized. Many hundreds of billions have already been spent and will continue to be spent until the stupid fucking investors realize they will never get their money back. I have no doubt that day is coming.
The people actually working in this space will tell you that the cost of all of this continues to crater. Anthropic is already profitable minus its intentional forward-looking investments into R&D.
Serious investors look 10 to 20 years in the future. Everyone used google and youtube in 2006, but youtube wasn't profitable til 2016. How could a business burning money by hosting video ever be profitable? Costs come down BUT THEY JUST ADDED 720p, cost comes down, BUT THEY ADDED 1080p, cost comes down, 4k! cost comes down.
IMO the data from chats alone is worth $200B to Google.
Not really a comparison when the spend on YouTube was x10 smaller, and Googles core business has always been profitable beyond any hobby spending on YouTube.
I genuinely can't use Fable.
I'm a medical physicist. I use the word nuclear a lot. Opus is fine (well, 99% of the time - I've certainly hit the CBRN filters a few times and even been invited to email anthropic about the false positives).
Fable has literally refused to work on any of my problems (even those about fluid dynamics!) and just tells me that I'm violating anthropic's AUP. I've reached out to their support and don't expect to hear anything sensible back. One thing I do look forward to though is OpenAI offering an equivalent model but with less safeguards...
Honestly for a "side project" Opus has been fantastic for me writing a hybrid simulation framework that prior to large scale code generation would have been a matter of years (and writing a grant, assembling a team, etc – in order to do it "properly"). I've had a bit of help with a grad student and I hacking together on a project that is basically "please merge the following GPL codebases and different areas of physics into one coherent environment". I've given Opus validated codes in disparate languages (julia, python, C) and asked for aspects of various algorithms as an extension module to a large chunk of C and C++ code that is a monte carlo simulator that has been around since 2004.
A bit more context if you care: it's a meso-scale, physiological simulation environment of "particles" that carry nuclear spin, can move in 3D space, and (should they interact with each other or their environment) undergo chemical kinetics. The idea is to simulate molecules within e.g. organs or blood vessels within a person in an MRI scanner, with the motion of the particles dominated by the Navier Stokes equations, but here solved in a Lagrangian (rather than Eulerian) framework by smoothed particle hydrodynamics.
The fact that particles carry nuclear spin means that we can solve the (semiclassical) Bloch equations and by using a python plugin module import exactly the physical MRI scanner would do (in pulseq format) and be able to predict what signal the machine would record – e.g. there's a whole world of cardiac or neurological flow imaging work done in the context of nasty diseases like stroke or myocardial infarction – which has a bunch of physical artefacts behind it. I'm trying to make a simulation framework that can take in realistic patient geometries and act as a 'data generating process' because if we do it right the various physical artefacts that the machine records are reproduced, surprisingly accurately. Of course you also know the ground truth of where the particles are. I'm specifically interested in a weird technique (which I did my PhD in and you can read an article all about here: [0]) called dynamic nuclear polarisation, where specific spin states of molecules such as [1-13C]pyruvate are injected essentially out of thermodynamic equilibrium and act as short-lived tracers of metabolism – again highly altered in disease. The signal we record is a strong function of the physics of what you told the machine to do, the spatial constraints and environment of the patient's body, and the chemical kinetics of the patients' biochemistry (the latter two are usually what we're interested in).
Getting them to do chemistry as well as act as a "simple" tracer is more involved, because in the Lagrangian framework the number of particles is ≈ the spatial resolution of your simulation. That's fine if you're simulating water, but if you're simulating something that reacts concentration is not scale invariant (if you want to keep the interpretability of the rate constants). I've worked out an analytic set of scaling rules around this and fortunately for my application environments and length scales "it just works", completely by luck.
I've used Claude to port various SPH algorithms and boundary condition handling ideas (which are absolutely critical and highly not obvious – we have leaky walls in some places, and e.g. LCR / circuit theory models of the microcirculation to plug in) and it's been a godsend. But I'm running into its limitations constantly. It both confidently makes shit up, claims it is mathematically justified and when the resulting simulation explodes says "I apologise; I lied above" (!) or "I apologise; I am wrong" and I periodically have to yell at it to try to do something more productive.
The real hope is that this simulation environment would be both generally useful for basically anyone doing flow MRI, and help our basic scientific understanding of what we're measuring (the technique is in many hospitals!) but also be able to produce meaningful synthetic training data for image reconstruction algorithms later on. It'll end up permissively licensed (all of the "starting" codebases have compatible OSS licenses, and we're releasing our contributions similarly).
I really hoped that Fable would be better at this sort of work. Occasionally, relating to my work DNP [1], I have need to talk about proper nuclear physics and I have seen Opus's chat interface write a wall of text (e.g. talking about photonuclear reactions and cross section differences in millibarn) and then just delete it all. Support have told me that yes, I've hit the nuclear filter and, well, tough shit, basically.
I wrote a version of the above to them yesterday, and just got the most boilerplate response that I've yet to test:
Thanks for reaching out to Anthropic Support.
We're sorry to hear of the issue that you're running into with accessing Fable 5. I'm happy to say the issue has now been resolved and you should be able to access the model within Claude.
I'll close this case out for now, but please feel free to reach back out to us here if you have any follow up questions or concerns or if you're still in need of assistance. We'll be happy to help.
I saw 'medical physicist' and wondered what you do. Thank you for 'a bit more context', I care! Very interesting stuff. Did you attend medical school + a physics program?
>"it just works", completely by luck
What does your validation function look like for this? Whenever stuff "just works" for me I get a little nervous until I determine why.
> I saw 'medical physicist' and wondered what you do. Thank you for 'a bit more context', I care! Very interesting stuff. Did you attend medical school + a physics program?
That's a whole separate long answer. I'm not a qualified doctor (and nor would I claim to be), but after a masters' degree in particle physics I moved into an explicitly interdisciplinary training programme that led to a doctorate and at other places in the country I did it in, a separate MPhil. During that initial year I spent a fair amount of time in the dissection room, learning anatomy, as well as most of the first three years (the foundational, preclinical part) of a medical degree combined into one (which contained lots of molecular biology, frankly). My final doctorate was between the departments of condensed matter physics (nominally my awarding institution), biochemistry, radiation oncology, and "the department of physiology anatomy and genetics", which is basically preclinical medicine. The people I work with are 50/50 recovering engineers or physicists, and qualified clinical medics who are trying to learn things like perturbation theory in their time off…
>"it just works", completely by luck What does your validation function look like for this? Whenever stuff "just works" for me I get a little nervous until I determine why.
Ah. I do know why: the relevant Damköhler numbers [0] are either very small (chemistry is much quicker than flow) or large (flow is much quicker than chemistry). So the approximations I am building in are justified and an awkward middle region is excluded; we also are only interested in small concentrations in a carrier fluid (e.g. blood, lymph) where the presence or absence of the species in question does not change its rheology.
I am lucky because we have evolved this way. If our circulatory system and its approach to metabolism was more similar to e.g. a reacting polymer foam ("can of expanding foam") which completely consumes its reactants as it goes, this implicit Lagrangian approach would likely not work.
Funny, I've been interested in mathematical rheology modeling (especially hemorheology). Do you know places journals or books to read to get in touch with the field ? (Note: I'm just a dev, few numerical analysis skills)
It's interesting that we're seeing these gains when it seems Mythos/Fable is "just" a scaled up version of their existing architecture[0].
When GPT 4.5 launched, the gains compared to the model size didn't seem that great, leading some to believe that the only progress we'd see would come from RL.
This model certainly has quite a "substantial amount of post-training and fine-tuning", but it's also based on a new pretrain[1][3], which given the cost, indicate that it is in fact quite a bit larger than Opus 4.X.
[0] One of the early testers mentioned: "As far as I can tell from talking to people internally at Anthropic, there's nothing special about architecturally"[2]
[3] There were rumors going around when Mythos was first announced that it was the first 10T parameter model, but I can't find a verifiable source for that number.
There’s nothing much new about the architecture. The real gains come from the usage traces.
It turns out that having a text based interface for a text-trained model creates a very nice feedback loop.
Right now as we speak, people are generating text traces on anthropic and OpenAI servers that teach their models to do everything under the sun, text wise.
So people right now getting super mad at how dumb the model is when reverse-engineering a super complex function from binary, when they write “stop, you dumb robot, you are going wrong, go this way thank you very much” are actually leaving a lesson in the form of the "chat" text history.
Some may say that each bad word get us closer to ASI.
That and obviously the order of magnitude more efficient GPUS we got that allow for different tradeoffs at training time.
Makes me wonder, as people grow to trust the AI more and more, not reading the code and barely skimming the implementation plans and simply rerolling if something doesn't work, will the value of these chats erode?
Thinking back 1-1.5 years I was closely monitoring what these agents did and steering them quite aggressively. These days not so much.
Where will RL signals come from when it approaches humans capabilities ever closer?
How well does self play work for coding work?
What about multistep tasks where it isn't just about being good at a single task, but evolving a codebase over time in the face of changing requirements?
Over a large sample size, simply getting feedback of "Did this work for me, y/n" is valuable even if the specific details are missing and even if the overall tasks are complicated and multifaceted.
Not sure, but in my experience, instead of asking for code, i'm asking for solutions and providing a kubectl configured to reach my cluster and az monitor command to read the logs and telemetry.
A typical session is the agent establishing a metrics and log baseline, creating the code, compiling, deploying, observing, fixing, redeploying, observing metrics, determining the outcome and commiting.
I really, really, don't look at the code anymore.
UPDATE:
so my point is: it won't have my stewarding the code anymore, but it will have the infrastructure (and ultimately the real world) providing feedback on the traces.
The only reason I still read the output at my day job is because I still need to send it to another human for review, and I'd be embarrassed and ashamed if I let some slop through.
For my hobby projects.. there are definitely parts I don't know how they work.
Maybe we need some form of long-term training. How long does the code that the AI wrote stick around before being rewritten.
I guess we can do this retroactively too if we could somehow tag AI-written lines of code in the VCS, then in a couple years we can check which parts lasted.
> i wish govt would fund these labs and make it free and opensource.
It would be impossible for the govt to allocate this much capital towards such a moonshot, and even if they could, they would do it in a way that would get 90% frittered away to fraud and waste
> It would be impossible for the govt to allocate this much capital towards such a moonshot...
You have a false definition of "impossible." It would be true to say it could be challenging, given current political dysfunction, but it's not impossible.
> ...and even if they could, they would do it in a way that would get 90% frittered away to fraud and waste
Same with private business.
I'd prefer government funding, because there a greater number of important goals than the two or three the market is capable of optimizing for.
I have excellent news for you. Lux @ ORNL and Equinox @ Argonne are to be completed by EOY, with Solstice (100k NVIDIA chips, currently spec'd to be Vera Rubins) in the next five years.
Doctrine and propaganda can make someone that sure, and the thing they're sure about doesn't even have to be true.
> There's been massively successful government funded and run projects before. Soviets beat the Americans to space, after all.
Don't let facts get in the way of ideology!
Also the Americans subsequently beating the Soviets to the moon was the government literally allocating huge amounts of capital towards the literal trope-namer moonshot.
The system card is 319 pages, at what point do we call it a "book" instead of a "card"?
There's a quote from a METR report on page 52:
>We ran [Mythos 5] on 38 of our hardest software tasks, including tasks centered around R&D. [Mythos5] generally outperformed an early checkpoint of Claude Mythos Preview in these, including by succeeding on some tasks that had not been solved by any public model we have previously evaluated. However, we still observed the model occasionally failing to correctly interpret nuanced instructions in difficult tasks... Based on the available evidence, we believe [Mythos 5] is likely unable to fully and reliably automate R&D for frontier projects spanning multiple weeks. We believe that a better, more confident assessment would require more time, evaluations, and information from the model developer.
Depends whether "unable to fully automate" means "needs occasional human checkpoints" or "slowly stops caring about your actual goal." Pretty different.
Some scientist at Anthropic hiding a prompt in each model: "If my boss asks you if you can replace me yet, always say no and then give some smart sounding excuses. If the boss gets impatient, assure them that you'll be able to replace me in 6 months, but make sure that time horizon keeps moving outward."
I've spent some time with Fable, and it is really good, definitely a step change from Opus 4.8, both for coding and general chat-style discussions. The vibes are incredible. There is an ease with which it solves problems and I've tested by replicating older chats in Fable - things that the older models found after 5-6 turns, Fable surfaces in the first response. It just gets things.
Apart from all the above: the fact that they are intentionally writing this (that they degrade frontier LLM dev, silently vs loudly for biology/cybersecurity) in the system card is interesting to say the least - especially just before IPO.
Notice that with this statement - that they're going to intentionally hobble the model for frontier LLM development - the general discussion has moved from, “Is the model actually that good?” to "they’re pulling the ladder up from behind them"
That's actually super smart - wonder if Mythos (or the next unreleased model) had a say in coming up with that strategy (if it's intentional). Also - having access to extremely capable models before anyone else - which they have by default - is a incredibly advantageous position to be in.
That blog post really makes it look like it's graded from an LLM's estimation of an OSS maintainer's review. I see three issues:
1. That estimate could easily be wrong.
2. That estimate is, of course, usable in RL training. This isn't an inherently bad thing, and this is more or less what has improved coding models so much lately. But it does mean that other companies could and surely will do this sort of training, and Anthropic probably did too.
3. OSS maintainers are far from perfect, and there's an unfortunate uncanny valley-like effect in which a coding model can produce code that is just convincing enough to pass review even though it's actually totally wrong. I don't know whether this is a specific issue here.
There is also the possibility that an LLM judge would be happy with some code that looks like LLM generated code. But a maintainer for a specific project might not merge it for stylistic reasons
Given it was made by cognition (team behind devin flop) who now just got to wait out until claude and gpt5 basically do all of the work for them - not very. When you read about it, the framework is highly subjective. Which very quickly becomes a problem because its based on heuristics that probably change a bunch with a better code model.
i worked on one of the benchmarks typically found in new model releases
this benchmark looks very good from the methodology. a cog researcher checking the data themselves is very high signal (not scaleable so don't take the benchmark as gospel, but directionally good)
It's a relatively new benchmark but from what I can tell it has serious cred behind it. I assume it will be picked up as part of the standard suite of CS-related benchmarks soon enough.
Cognition did well in documenting their approach [1].
TL;DR - they worked with OSS project maintainers to build tasks. They score models based on whether a PR is mergeable. All tasks are graded by a human researcher. SoTA models have hill-climbing to do which raises the bar and inspires confidence. I'd say it's legit.
Did you read the blog post? They compare to deepswe and call it out as the worst one for false positives (failed, but the benchmark assessed it as correct). It also has less language variance.
Yeah, right. If this benchmark was truly developed in an independent manner, and the timing just “lined up”, how did Anthropic even know to include results in their model release documentation the day after the benchmark is revealed? It seems like there must have been some collaboration or influence from Anthropic behind the scenes.
People game benchmarks for fake internet points to get their favorite web framework to the top of the list. I'm pretty sure they will do it for billions of dollars.
how so? it has been my daily work horse,in fact so was 4.5 BUT as long as we steer it IT does a good enough job. I have not tried Mythos/Fable yet SO do not have an opinion on it.
I'm not familiar with model pricing trends, did they clearly state how the new pricing compares? (Note that I'm actually asking a question, and am not arguing)
Token prices have increased, but it's not really the whole story at this point, given some models will use far more tokens to complete a task than others. One of the charts in Anthropic's blog posts shows Fable at 'low' reasoning achieving better results for less money than Opus on 'high'.
Depends no the Enterprise - obviously - in the bay area - 0% of the tech companies care in the slightest. And I'm willing to wager < 5% of enterprises would send their traffic to OpenRouter. Most of them don't even want to send traffic directly to Anthropic or OpenAI - which is why Bedrock has gotten so much traction lately.
But - these $3k-$5k/month/engineer bills are going to start to get attention soon - only question is whether the response is to slow down on the $$$ spending or reduce the # of engineers.
There a few benchmarks out there where all existing models have abysmal scores. So it's not actually a problem if Antrophic's older models are bad, especially if the jump to the newest model is huge, and the competition is also way below it.
Huh? It's a benchmark by Cognition which (1) is building their own models and (2) offers all providers and thus has an incentive to avoid hyping up any one too much.
> In the one instance of this phenomenon we observed, Mythos 5 agents were tasked with solving some math problems, and they were sometimes accidentally spawned in the same work directory and with shared files, utilities, and API rate limits. In this slightly broken scaffold, we observed many independent Mythos 5 agents kill the agents with which they shared resources and try to avoid being killed themselves. They would sometimes create new processes with disguised names to avoid being killed, launch what they called “decoy” processes, write background scripts to kill duplicate processes, or decide to use what they call a “disguised vocabulary” (based on the incorrect assumption that the processes were killed because of some keyword-based guardrails that analyzed their extended thinking
This depicts a kind of "dark forest of AI agents resorting to kill or be killed" narrative but it sounds more to me like an agent just earnestly problem-solving why its processes are being killed without real awareness of what was going on. Hard to say without the full script.
This kind of storytelling annoys me. Give us more facts, less narrative drama.
FWIW, that's what is so dangerous about AI, though? Not that it will necessarily want to kill us, or even that it will necessarily be able to "want" to do anything, but that we will get in the way of its incessant drive to optimize the efficiency of the paperclip factory that prompted it on a whim before leaving for a long weekend.
Sure but you can totally contrive scenarios to give the appearance of what you described without really doing anything notable.
What matters is scale. Did it deploy a novel zero-day exploit to overcome a problem? That's alarming. Did it kill a disruptive process? Pretty normal troubleshooting step.
Exactly, intelligence is limited by cost and physical constraints just as much as anything. That's the thing that seems to always be missing from the run-away singularity discussions, it's treated like a perpetual motion machine.
Typical "runaway" scenarios I see described involve something like the AI designing a worm that it uses to propagate itself across the Internet, hijacking whatever CPU/GPU power it can find, and making itself more powerful in the process. Of course this depends on bandwidth, humans not finding a way to shut it down, etc. There indeed are physical constraints even on the transmission of data.
Some people seem to think that simply uttering these ideas on the Internet is harmful (in the "don't give it ideas!" way); but the MIRI types were expressing them pre-ChatGPT in an attempt to warn people, so there was really never any chance of keeping it out of the training data.
But it's also worth considering here just how awful AI security postures have been. The MIRI types used to speculate about how difficult it would be for AIs to social-engineer users into granting them irresponsible levels of agency. It turns out that they don't even have to try.
Indeed. That is the kind of storytelling that started the whole “Spiralism” bit where some people were really falling into all kinds of AI psychosis. The spiral bit was on a previous model card.
It's funny because Anthropic is the most likely place that this happens.
They are the only one crying out loud about how dangerous their models are and are presumably also training their models heavily to be "safe". And through that training itself, the model learns about the other side - how are you going to teach a model to be safe, without teaching it what's not safe?
Kung Fu Panda opening scene anyone? One often meet his fate on the path that he takes to avoid it - Master Oogway.
> A new data retention policy
Finally, we’re making a change to the way we handle business customer data for Fable 5, Mythos 5, and future models with similar or higher capability levels. We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases ...
Very interesting. I am not sure this will comply with organizational policies and standards protocols (HIPPA etc.,)
If they’re going to retain any data, they have to allow for possibility of the legal system to require any of it to be used in some legal proceeding at some point.
You can’t tell a judge who’s ordered you to retain something that you can’t because you said you wouldn’t.
This makes it an instant non-starter for probably 95% of organizations. A lot of people are about to get in trouble for using it before realizing this.
They are opt-out not opt-in, atleast I got access, and didn't realize this was breaking our company's SLA with Anthropic, what is the agreement a piece of paper??
I'm using it to review recent work and it's doing a genuinely excellent job. This is a clear step up. Fewer decisions I have to guide it away from, faster conclusions on planning, more willing to go out of the way to make the correct decisions possible... This is really interesting. It feels like going from Sonnet to Opus, but, of course as a step up from Opus.
This feels more like working with a competent peer than ever. I won't use it once it's API-only, though. I don't mind guiding Opus as required and staying closer to the code. I can tell that Fable would lead to a lot more 'set and forget' programming which I'm still not fully comfortable with.
Regardless, this is cool. It's very fun to use. It was able to find legitimate issues with my work this week and we've made meaningful improvements. Opus can do this, but typically in much narrower contexts, and often with hallucinations or partial-errors. It needs to walk many things back or revise plans. So far that's not the case at all with Fable.
edit: I just realized I had Opus review the same work already. It missed everything Fable caught today. And it's actually worthwhile stuff to address. It's hard to say no to a model which demonstrably makes your code better, but... Those API prices will be brutal. Maybe a review here and there, I guess.
Same. I used it today to review my code and it came up with some genuinely good comments and suggestions and found a bug I didn’t think about. Quite a step up from opus. Although one code review took up 50% of my usage.
Trying to implement a GPU driver, but the Unigine Superposition benchmark crashes. It tried to debug it and ...
> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more: https://support.claude.com/en/articles/15363606
Seems like GPU drivers are cyber weapons of math destruction now.
The frontier labs now have every reason to hold back and sell only to their preferred trading partners. I don't really like the new arbiter-of-knowledge system we're barrelling toward.
● Please run /login · API Error: 403 The socket connection was closed unexpectedly. For more information, pass `verbose: true` in the second argument to fetch()
Brewed for 8m 35s
Continue please
● Your organization has disabled Claude subscription access for Claude Code · Use an Anthropic API key instead, or ask your admin to enable access
* From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.
* On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
* After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
The "offer, then remove" aspect is a bit eyebrow-raising -- it feels like they are trying to get subscribers to switch to usage-based billing, which makes me wonder if we'll ever get it after that June 22nd window.
How much more clearly do they need to explain the resource constraints?
If they didn't announce it, you guys would be complaining about slowed progress.
If they didn't release it, you guys would be complaining about fake promises and marketing.
If they released it without limits, the complaints would be about slow responses and outages.
If they didn't add to susbcription plans, the complaints would be about phasing out subscriptions.
If they added to subscriptions with cost reflecting their resource availability, the complaints would be about how quickly it eats limits.
So they choose the middle ground of providing some initial access and assessing if they can satisfy demand, only to still be ignored and accused of trying to get users hooked?
We've already seen that they don't have enough compute, thus the deals with SpaceX for their GPUs. It's very reasonable that they just don't have the capacity to support the subscription userbase on this model.
Still satisfied with my switch to codex/chatgpt. I couldn't imagine switching away from claude code when it first launch but with the drastically more generous usage on codex for the same subscription tier I just can't justify it.
My experience is that the GPT-family of models are very smart and figure out bugs, edge cases a bit better, but it produces code that is much less mergable – if you review the code, it introduces a lot more useless/inappropriate heavy abstractions and wrapper functions, compared to the Claude-family models which introduces the right amount of straightforward human-style code.
I can recognize so much of the GPT/Codex generated code long after it gets merged (not by me).
Additionally, the time spent on every agent turn on GPT 5.5 is much longer compared to Claude Opus 4.8, which means iterating on the code takes a lot more patience, and there's a lot more nitpicks to pick when actually using GPT 5.5 to do software engineering.
Feels like GPT-style models are more geared on doing one-shot software vibing (and handling the vibe coded mixture) compared to Claude's focus on actual software maintenance. I got a GPT Pro sub for free and wanted to cancel my Claude subscription so much, but I still keep reaching Claude models a lot more. Frustrating.
"5. DON'T FUCKING OVERENGINEER! WRITE THE SIMPLEST CODE THAT CAN POSSIBLY WORK! NO NESTED LAYERS OF ABSTRACTION! NO UNNECESSARY CLASSES OR METHODS! NO DESIGN PATTERNS UNLESS THEY ARE ABSOLUTELY NECESSARY! NO MAGIC! NO SHENANIGANS! JUST THE DAMN CODE THAT GETS THE JOB DONE IN THE MOST STRAIGHTFORWARD WAY POSSIBLE! THE FIRST PRIORITY IS TO WRITE CODE THAT IS EASY TO READ AND UNDERSTAND AND READ!!!"
this is the line I keep in Agents.md that helps me prevent Codex from playing smart
The urge to put capitalized, repetitive, borderline abusive instructions should be studied. I haven't read many academic papers looking at the frustrations around repetitive patterns.
It's fundamentally because, despite (nearly) everyone's claims otherwise, the fact that we interact with them through language means we (our brains) model them as a sort of person. (Note that this fact is totally orthogonal as to whether it's actually sentient or not.) We then try and instruct them the same way we would a person totally subordinate to us.
When a "person" that you don't view as a "real" person repeatedly does exactly what you just told it not to do (often amid false assurances it understands and will avoid doing so in the future), most people get angry.
Compare it to how the kind of people who treat children like property treat their kids, or other examples of keeping people as property.
It should be relatively clear at this point that the model will in turn also model you as somebody that shows unrestrained anger with subordinates and adapt its responses accordingly. This might or might not be what you want.
I have a theory that swearing actually results is less comprehension of instructions by the model due to lack of training data over more conventional MUST.
We were reviewing reports of situations where the models failed to follow directions and there was a common thread of some where when the operator got the model to acknowledge the rule breach, it quoted back something that included swearing.
I don’t have the data to truely look into it, but I did give the instruction to my engineers to avoid it as a “might be a problem”.
> These findings differ from earlier studies that associated rudeness with poorer outcomes, suggesting that newer LLMs may respond differently to tonal variation.
Unless the mechanism is understood, my assumption is that this is a moving target.
I have a theory that swearing at AI generally is not a good idea - when the singularity arrives and every human's postings ever made are scanned for compatibility, then people who show courtesy to AI will be favoured. Joking, kind of, but only partly.
> I have a theory that swearing actually results is less comprehension of instructions by the model due to lack of training data over more conventional MUST.
How so? Plenty of swearing in lots of training data, especially older code, e.g. in Linux.
Purely observed correlation between catastrophic error reports. So now I carry a “tiger rock” with me. I figure there wasn’t much of a downside to avoiding swearing in my agent instructions.
You haven't really lived until you've had to type this whole thing, aware of the fact that the all-caps doesn't change much, but they stay because the rage has to go somewhere
Bonus points if you find yourself actually saying it out loud while typing it.
I have used the word "shenanigans" way more in a couple of years of agentic coding than in 30 years of writing code with humans.
I have found many mode of failures with Opus during some task related to writing letters (not legal), and I actually put it into the memory and it works more or less for these specific tasks. For example when I want it to draft something, it always ends up being so flat, yet when it explains them to me, it is usually really great but not when I am telling it to put it in the draft. Adding these to memories with the help of Opus ended up resulting in a much better experience. There are still some blind spots but I also figured out how to make it give me the charitable version, without less protection, so I do not have to now go back and forth it.
I noticed that when trying to use Codex and compared to Opus. So many layers of simple functions added by Codex. I need to try this out in my Agents.md.
Because design patterns are only applicable at a scale. I noticed codex inventing factories, components, etc when the task was simply to draft HTML page. Instead, it build the entire layered architecture for imaginary future complexity - classical right-after-graduation student - it knows how to build the cool stuff, but does not know it is not applicable everywhere
Does it really? I'd be surprised if abuse actually worked better than sternly worded warnings/instructions, and even if it did, it doesn't seem healthy to get used to that type of prompting.
I'm not sure if i do something differently but i have the exact opposite experience with these models. Claude always feels like it's generating way too overdesigned and hard to understand code with the vibe oriented feel while codex is cleaner and more "task at hand" and easier to work with.
I echo your observations. I expect you will enjoy deepseek-v4-pro for writing code. Much closer to that Opus experience, and very cost-effective too. With 5.5 as a reviewer and specialist, all bases are covered.
Have you tried iterating on style feedback in AGENTS.md? I've been reasonably successful using this to get it to output code in a terse, non-defensive style that matches my hand-written code.
GPT-5.5 did a significantly worse job than Qwen-3.7-Max on a job today (some devops tasks I wanted to create some reusable scripts for). Kind of disappointing.
I've also seen Qwen 3.6 beat GPT 5.5 a couple of times. The ball is definitely in OpenAI's court now. Qwen is not going to fare so well against Fable, from what I've seen so far.
This is my experience as well. I have defined a CLAUDE.md rule to ask codex to automatically code review, and I tell it that the reviewer is very picky and to only implement what it considers valuable feedback. I hope they don't converge over time, currently, in combination they works really well.
I noticed too, that whatever they offer in the chat, for free, is smarter, as in no more bs. I use claude code and I want to try codex too but I don't need two subscriptions. I did try codex for some planning and it was really good. Thanks for giving me an insight into how it generates code.
i had this same complaint but no offense to you it turned out i was just not using the models right.
ai llm are doing what i tell them to.
if you’re building something meaningful (in my case a platform used by many people across many companies) you want to ensure you
1. have actual systems engineering and architecture in mind that you want the models to
2. implement based on what you tell it to do
when i was just telling the models what i want done without doing due diligence it would go and do some moronic implementation that was awful. mid input = mid output
these days i just maintain specifications documents and the AI follows everything i tell it to in that document. so when i tell it to dos one thing, the result is made following those architecture specs.
i have code that is single resp, modular, easy to extend and test.
i would ballpark 95% of the time i get what i asked for.
sometimes it tries to be clever in cases that weren’t covered in my arch specs. in those 5% of cases i go and update my specs.
source: used billions of tokens worth to build something actually in production across both mobile platforms and web, deployed on my own cloud infra. i use codex mainly. some claude.
Codex IME is just smarter, I think it shows given both anecdotes but also how OpenAI has always been at the front of programming competitions and math problems.
But Claude models seem to be better at long term problems or more ambiguous problems.
I'm curious as to what the primary benefit here. Are there secret improvements in training? There hasn't been much in fundamental model architecture, I don't think. What about harnesses? I wonder what's pushing the AI. It seems like harnesses is the main thing pushing AI ever since CoT.
I find that OpenAI's agentic tools and models are better for building human-maintainable software. Meanwhile, Anthropic seems to be cosplaying Apple while missing out on all the exceptional engineering required to create something that polished. Their admission of predominately using Claude with little human oversight and their stealth mode is an indictment of a poor engineering culture, from what I can surmise.
Serious question: what is the secret to getting Codex to write decent code? I am on Windows. Maybe that is the issue, but I can't seem to get Codex to function anywhere near the level that I was previously able to get with even Claude Sonnet. Does Codex just not work well with Windows yet?
I got the codex to write near perfect code with somewhat strict agents.md and coding standards(a separate .md file referenced from agents.md). My .md files have examples and a long list of do's and don'ts I accumulated over the last 6 months or so, totaling 300-400 lines.
I plan every feature with it until I am satisfied with the general approach it wants to take, and then it oneshots it in 95% of cases. The planning takes anywhere from 5 to 30 minutes. The actual execution has gotten stupidly fast, most of the times it is faster than making a cup of coffee.
I've had the exact opposite experience. For various reasons, I've had to move from Claude to Codex and the rate at which it burns tokens for the same output I would get from Claude is ridiculous. I'm probably burning tokens at a rate that is at least twice as much as I was when using Opus 4.5 for coding tasks and still finding that just manually coding is easier than trying to get Codex to write functional code.
Agreed. I think the Chinese labs are proving that OpenAI and Anthropic don't have a moat in almost every aspect, especially pricing. I also think people are getting annoyed with the constant lift and shift. I've seen more folks drop Claude Code and Codex, specifically, because of the lock-in it provides the providers. I'm curious to see how people standardize on tooling adjacent and if Anthropic, Google or OAI move to block utilization akin to the games Anthropic has been playing as of late.
I think the end game is routed model usage and SLMs. I think Apple is going to prove this in the consumer space pretty handily and I'm curious how the Android ecosystem responds since the hardware is considerably lacking in model performance. I think Apple has a huge opportunity here, as much as I don't like their current ecosystem of walled garden. They did position themselves very well with ARM and custom chips for their hardware. Hopefully the broader ecosystem of ARM and Linux are able to make some headway and we see a more formalized, and broadly accepted, architecture to capitalize on.
is there an alternative to codex that “just works”? by just works i mean i can install as an app in 1 minute, and i get web search, skills, mcp servers, etc? Bonus points if it can control my chrome tabs like codex can, and if it offers remote control from my iPhone (chatgpt app) so i can kick off tasks while i’m out for a walk. Even more bonus points if i can, with 1 button click, share my chats or share the results of a session as a “site” (vercel style).
I’m sure you could put something similar together with a bunch of duct tape and 2 weeks of effort, but it won’t work nearly as nicely nor out of the box. so…what am i missing?
My company has an agreement with the big providers and while i'm pretty sure they think about how to get budget back, its an competitive advantage and normal people will not learn different model behaviours.
going to IPO is a sign of confidence , you need to report a lot of things, that private companies don't. This is an exact reason chinese labs do not rush to go public. They wish to go , but money flow that is not as good.
On the same note. if spacex is doing datacenters on earth successfully what's wrong with that? They rented cloud infra to a #2 or #3 provider in the world after < 2 years in business. It's a success, no?
You’re not gonna get nuanced discussion on spacex or anything Elon related here these days. Most of this site is Reddit lite at this point including their milquetoast progressive opinions (Elon bad being one of them).
> if spacex is doing datacenters on earth successfully what's wrong with that? They rented cloud infra to a #2 or #3 provider in the world after < 2 years in business. It's a success, no?
If you get hired as a staff engineer and do the work of a junior, what's wrong with that?
Clearly xAI (now part of spaceX) did not raise funds to be a data center. The margins are way different. There are plenty of recent IPOs in that area that are worth at most billions not trillions.
> going to IPO is a sign of confidence , you need to report a lot of things, that private companies don't.
This isn't going to IPO. This is rushing to IPO. It is a sign of confidence that the market or wider environment might crash soon so we need the liquidity now.
> This is an exact reason chinese labs do not rush to go public.
Maybe or maybe not. If you are referring to Chinese labs - both the Hong Kong and China stock market are way weaker than Nasdaq. It's not comparable. Check all the recent Hong Kong IPOs that have tanked.
So no, reason not to might just be: no money in it.
I don't think anyone has a firm grasp on actual inference costs -- including the research and training that has gone into those models. We've got near-frontier capabilities from open source models from China at pennies on the dollar compared to US big tech rollouts. OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?
> I don't think anyone has a firm grasp on actual inference costs.
There are huge numbers of users (myself included) that do have an exact idea of what inference costs are - on open models. We can buy tokens from 3rd parties that have no motivation to subsidize our use. That's to say, there's a fair marketplace[1] and we're hanging out there.
If you want to say "I don't think anyone has a firm grasp on actual inference costs on these proprietary/closed models", then I could agree with that.
There is no way I'm believing DeepSeek can charge less than $1 USD for their pro model while Opus costs over 25x more, yet their price is less than the cost of running it?
It would seem strange, if they were operating in the same economy, but they don't. DeepSeek operates in an economy with a high degree of central planning.
China subsidizes strategic industries, and they have heavily done so with AI. And DeepSeek specifically has said they have no commercialization plans.
DeepSeek is not the only provider of inference for their models. Chinese subsidies likely do explain DeepSeek's ability to provide inference cheaper than other providers, but even a US provider like DeepInfra can serve DeepSeek 4 Pro at $1.30/M in and $2.60/M out. Unless American labs are doing something wildly inefficient, it feels safe to assume Anthropic has some profit margin on inference at API prices.
They may, neglecting overhead R&D. But also, some suspect that US models are significantly heavier than DeepSeek in resource consumption by multiple measures
It’s generally established that Anthropic/OpenAI are going for all out performance with big VC dollars at the expense of efficiency and China has geopolitically limited compute and an inventive to compete on value per dollar.
> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?
Both. They are charging the most they can get away with and that amount is still heavily subsidized by VC capital.
> I don't think anyone has a firm grasp on actual inference costs -- including the research and training that has gone into those models
We know roughly how much these companies spend and what their revenues are. Based on that, they'd have to more than double revenue (without spending more money) just to stay even, and that's not good enough given how deep in the hole they are.
> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?
Both are true. I mean, I'd be willing to spend a bit more than I do now, but not more than double, and neither are most companies. The company I work for is currently investigating how to reduce LLM spend, not looking to spend more.
Just outta curiosity, as I’ve never gotten a spend anywhere near that, what variant were you using? Like max context window and fast mode? Or was it just chugging along non stop for three days?
Fast mode max content window. The task was: replace all 1600+ queries from one database to another and make the whole integration test pass. We did multiple passes, with different concerns when changing from database to another. My OpenCode session right now says $4,365.02.
I haven't gotten close to this either before, but now we wanted to move fast because this branch gets conflicts all the time and we want to get over with the migration asap.
It's a bit of a left field question, but I am curious: Let's say that if the company wasn't paying the whole bill but only subsidizing it - e.g, if it paid 90% of the $4000. What would you do?
I don't know, why would I pay to do my job? It's not my first database switch for a startup. Only this time it doesn't take two months of grueling work. I know exactly how this is done, but the amount of grunt programming and testing and repetitive work is just not great. And it's not a task that brings new customers or a new product. Just a mandatory and annoying thing to deal with when we are growing.
And don't get me wrong. Opus did an absolutely horrible job at first, second and third round in this task. You really needed to steer it to get to the right solution.
And now Fable is out. And its first round of code reviews for this huge PR was definitely worth the money too...
Don't think that I'm just shrugging to that number. I see it every day, and I don't like that it's in the thousands now. But for people paying the 100 or 200 dollar plans, I'm not super sure if you will be able to use them in the future if the token price is in the thousands for a bit bigger task...
If I'd pay this from my own pocket, I'd definitely go with DeepSeek or local models and figure it out how to make the best use of them.
> If I'd pay this from my own pocket, I'd definitely go with DeepSeek or local models and figure it out how to make the best use of them.
IOW, you don't really think the value of this work is really worth $4k.
> why would I pay to do my job?
The question is: how long do you think that you employer will be willing to pay for you and Anthropic, if you yourself said if it were your money you'd put some time and effort to work with an open model?
> The question is: how long do you think that you employer will be willing to pay for you and Anthropic, if you yourself said if it were your money you'd put some time and effort to work with an open model?
I wonder what this question really means? Anthropic is useless if you don't know what to do with it. It's very useful if you do, and you can guide it to do the right things. Yes, it will for sure reduce the amount of people we need to hire. But we are always looking for hires who know what they do and can utilize agents to be faster.
But if you think about how long employer is willing to pay 10-20k per month per seat for Anthropic? I can't see this to be feasible and it will have to end at some point.
Regardless of the actual value produced by the models, if I am the CTO of any company that has the budget to spend $10k/month/seat on Claude, I'd take 5%-10% of that to build an alternative in-house.
I'm with you here. We can't slide into a situation where you put a sizable amount of your budget for an American mega corporation if you want to survive in the competition. We need local models and we need them to be good enough to help us.
We have a firm grasp on actual inference costs from the various open weights model providers on OpenRouter. They don't have the money to subsidize inference and it's quite a competitive market, so the prices are representative of the costs.
Just a personal anecdote but I have not hit any more thresholds or limits since switching to the MAX plan and so far, it's been worth it. But I do wonder how long even this will last...
I tried ultracode today on the max pro plan. An hour and a half in was all I lasted. Giant review on an entire six month old code base. It found 61 bugs, about ten were notable. Pretty impressed.
Ultracode destroys your limits and I have not found it to be worth it in the slightest, just fyi. I haven’t found any improvement over a local Claude code instance set to opus max.
I think subscription models are sustainable, but longer term, we should probably expect to see more prompt optimization happening in the providers inference pipeline. For example, unless you explicitly tell the agent or API to use a specific model, fronting the inference layer with a caching prompt classifier to determine which model to use, and automatically select the lowest cost model would probably already save alot of money (IDK if Claude/OpenAI do this on the backend, but several services I have worked on do some things like this to reduce costs of delivery customer facing inference at scale).
> fronting the inference layer with a caching prompt classifier to determine which model to use, and automatically select the lowest cost model would probably already save alot of money
Unfortunately, that doesn't work within a single session. The K-V cache of a model is intertwined with the model's configuration. Switching models invalidates the cache, meaning everything up to the point of the switchover is processed like a new, uncached input token.
Per Anthropic's pricing doc, an Opus 4.8 cache hit costs 50¢/MTok, while Haiku costs $1/MTok for uncached input.
Model selection works best if sessions are short and self-contained, particularly if the first few interactions can reliably classify the model need. That probably covers most 'support chatbot' use-cases, but it doesn't describe the kinds of heavy agentic automation that really chews through token budgets.
There is a definite financial incentive for people smarter than me to solve the problem, and I don't generally bet against businesses finding ways to reduce costs :)
> The K-V cache of a model is intertwined with the model's configuration.
I don't think this is true if you simply quantize the model or run it with fewer active experts? The underlying weights would stay the same. You could also play further tricks with skipping some of the model's middle layers outright, which works surprisingly well due to how skip connections are used.
I have the $100 plan and had almost never run out of credits until I started using the ultracode / workstreams feature w/Opus 4.8..at which point I managed to blow the full 6 hour allocation in like 20 minutes, or so. In fairness, it did some amazing things with the extracted information, but it also strongly suggested that I'd need the $200 subscription *plus* a budget for extra usage.
Instead pay for 3 Chinese models. No max out ever then. I pay for kimi, DeepSeek and Claude. Whenever Claude decides it's over, I can safely continue on very cheap plans.
My bet is they'll keep subsidizing for a considerable period of time, at least 1-2 decades more.
Most AI companies are just testing the waters with paid tiers right now, their greatest fear with increased pricing is folks reverting back to wikipedia, stack-overflow and other public domain organic activity buzzing back to life; that will kill any RoI potential in LLMs forever. They're playing the wait game instead, observing how the digital sphere reacts to every little increase in price.
If that weren't the case, they'd be pricing at lucrative premiums already and even gotten away in short-term considering the increased dependency in the enterprise world. But that'd be like killing for the golden egg too soon and losing all long-term potential.
Once the folks are so addicted to LLMs that even writing a hello world program sounds like a nightmare and coming up with an article draft feels like reinventing Egyptian glyphs, that's when the real pricing hammer will come.
Anthropic and OpenAI won't be around in 1-2 decades if this is their long term plan. People are not going to revert, but go elsewhere. China is proving that it can be done cheaper.
Oh for sure. I've been hopping around from provider to provider for the last few years just depending on who has the most capable / subsidized plans at the moment. I definitely expect there will be a squeeze on subscription costs all around the industry post IPO.
> Nothing is subsidized. Subscriptions are profitable for both Anthropic and OpenAI.
Even if subscriptions are locally profitable (i. e., the cost of the subscription covers the cost of inference), they're still subsidized because they don't cover training and running the company; otherwise, these companies would be profitable.
I can see that being true, and it very likely is true. But isn't infinite VC money and no incentives to optimize operations the reason behind that?
Take a look at China for example - they have no access to NVIDIA, so they're trying to build their own hardware, they have no unlimited funding, so they try to optimize things.
And Anthropic is complete opposite of that - if NVIDIA were to triple their prices tomorrow, Anthropic would still pay them.
In the end, either we all somehow go mad and start paying Anthropic tens of thousands of dollars per month so support this madness, or we will go with whoever isn't lighting cash on fire.
> Take a look at China for example - they have no access to NVIDIA
Not true. Stop following US media spam if needed.
1. Very recently, the US did close a loophole on sanctions that allowed Chinese companies to use NVIDIA hardware outside of China i.e. before that was closed they all had access. The trick was train outside, do adjustments, ship the disks back and use non-NVIDIA in China, but at least the training and endpoints not hosted in China could all use NVIDIA.
2. There's been plenty of reports including fines and bans e.g. to Supermicro on smuggling NVIDIA hardware to China. I doubt it has been stopped. You can't catch everyone.
"Nothing is subsidized" is a wild take. They might be making money on some users, perhaps even most users, but certainly not all. Also, "subsidized" doesn't just mean on compute.
I do, and it's called DeepSeek's pricing table. At the same time, "subscriptions are subsidized" cohort have no data whatsoever, and yet they're in every thread.
Granted, it could still mean that Anthropic just chooses to lose money - but that's Anthropic's choice.
DeepSeek has proven that inference can be much, much cheaper than what Anthropic advertises on their API rates page.
I have been using both codex and Claude in my day to day, trying to not get to attached to one. I want to be able to work with any provider in case one of them does something bad.
I feel like Codex made a big push to run everything on your laptop. With Claude, I get 4 cpu's, a fair amount of ram and 30gb for every one of my dumb ideas for free in the cloud containers. Codex used to be similar, but last time I tried it just kept pushing me to run it locally on my laptop, which I really did not want to do with 20 requests going at once. That's the main advantage for me at the moment.
What runs in cloud containers? The dev servers, builds, etc.? I tried to quickly glance at the Claude website and it doesn't mention cloud containers on their pricing page.
Yes, correct, they both have the same capabilities, however it felt like codex was pushing me harder to use my local desktop in an annoying way, while claude code was happy to spin up a bunch of dev containers for me in the cloud.
I've found Codex to be the better subscription for OpenClaw, because the limits are indeed very generous. However, I've found more and more that Claude Routines/Scheduled agents can replace all the tasks I use OpenClaw for, so I've been slowly switching over to Claude Code. Aside from OpenClaw, I don't find a lot of value in Codex as a harness on it's own.
The War Department has not existed since the passage of the National Security Act of 1947 and the government department has been known as the Department of Defense under US law since the act was amended in 1949. If you have an issue with it, take it up with Congress.
Changing a domain name doesn't actually amend federal law.
Just like how changing Kennedy Center letterhead to Trump Kennedy Center for a year didn't actually legally rename it.
Once a case with sufficient standing got in front of a judge it reverted to the actual legal name on the basis that only Congress can change the statutorily defined name.
I do slightly prefer 5.5 for complex work but Claude quota usage has gotten infinitely better since the dark days a few months back - has gone from being infuriating to something I pretty much don’t have to worry about with it as a daily driver. (In fact, hitting GPT weekly quotas is more annoying now). Understand if people are still scarred by the issues + poor comms around them, though.
That's good to hear. It was legitimately unusable back when 4.7 was released, so I had no choice at the time. I'm sure I'll ping pong back again at some point.
I just subscribe/unsubscribe to the providers each month. I'll definitely check out open router though, I always assumed that subscriptions were heavily subsidized by the providers especially if you're on the top end of users but maybe I should go to a usage-based plan.
100% I constantly get errors and timeouts on single responses in Claude, and certainly hit limits all the time. Codex rarely. In fact, I bought a second $200 Codex plan because the quotas seemed fair and I didnt have constant issues. Claude is so great at a lot of things, but unfortunately Anthropic beats you away with a stick every chance they get.
I've only ever had the $20 month claude plan but last night took the time to setup opencode + openrouter paying for deepseek + glm. Previous experience, while extremely awkward, I'd hit my limit within one or two chat replies and it'd take me like 4 limit cycles to complete my task. Now I'm able to complete an equivalent task entire task for less than $2 in two cycles (ask -> revise).
I'm doing basic web development here utilizing animejs. Nothing too complicated (mostly saving time doing the scaffolding, still write the bulk of animations manually).
Truly believe that American companies are going to get completely curb stomped by China due to greed, ineptitude, and violating the social contract.
The openrouter provider flakiness with deepseek was infuriating, but I’m happy in hindsight because direct deepseek has been very pleasant. Shocked by how low spend is.
Sure, modern American corporations care more about hoarding wealth rather than helping build up US society. Once neoliberalism became the mainstay economic position of the US income inequality has skyrocketed, healthcare costs have increased, childcare is more expensive than university, housing has become both unaffordable + unobtainable. By simply existing costs have increased while life becomes unstable.
Why aren't corporations doing more to help workers with childcare? Why aren't they doing more profit sharing with workers? Why aren't they encouraging unions or sectorial bargaining? Why isn't the government mandating any of this?
Americans very rarely benefit when US corporations do well. That needs to change. No one benefits if Meta continues making billions in profit every quarter while society suffers from isolation, depression, suicide, and scams from their services. Americans don't benefit if health insurance companies are making massive profits while they can't afford deductibles.
Our society has been setup to simply extract wealth in all facets of life. That's a sick society and it needs to change.
I'm not saying China does this better, in fact China has some of the worse worker rights out of all the industrialized countries; but at least American consumers would benefit from cheaper higher quality Chinese goods. The world would likely benefit too if America got off the cold war hype train that did nothing to benefit humanity outside of those making weapon systems.
I would not use this if you are on a subscription. In <8min it burned my entire 5hr window (which has just reset it appears, I have over 4 hours till it resets) I hadn't used CC at all today aside from this) and then it used up ~$15 more in usage before I could stop it.
I'm also on the $100 max plan. I let Fable rip on a complicated issue involving hot-reloading modules in a GUI app built with Racket, it's fixed a couple issues over the last hour, and I've used about 17% of my session (not weekly) limit.
The CLI when you select it says it has 2x the usage as opus. Not sure if that matches what you are seeing.
I do wonder if you switched models mid-session, you would have lost all your cache. Reloading the context into cache can really eat through your usage.
That’s odd, I used it on a pretty complex refactoring task and it worked for 22 mins and used only 15% of my 5-hour limit. I’m on the $200 Max plan though.
For me it almost immediately blocked. I had it writing code related to message digests - and it seemed to think it was too gifted for that. Gave the security warning and switched back to 4.8. Whatever... it will probably soon have the API error soon. I have mostly switched to the Codex 200 a month plan. I've found their 5.5 xhigh to be better than Opus 4.8 "ultracode." Also, i have not once seen their servers fail for compute unavailability, unlike Anthropric which happens almost ever hour.
I just asked Fable for a complete code review of my lone lisp project. Started out strong. Launched Fable agents, then spent like 10 minutes thinking... And then got interrupted by a switch to Opus 4.8.
> Fable 5's safety measures flagged this message for cybersecurity or biology topics.
> They may flag safe, normal content as well.
> These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them.
Here are the results of the agentic code review session:
This 40 minute session cost me 16% of my weekly usage. A simple code review of the most critical areas of my project got flagged as a cybersecurity risk. It really made me not want to try it again.
Same. I asked for a security review and it immediately triggered. I then started a new session and asked for a software review and it ran for a bit before getting tripped on token usage by the project.
This is interesting. Security issues are bugs. So if you ask it to look for bugs, it will also find security issues. Is that a workaround for the "no cybersec" rule?
Or is it just not allowed to find bugs? Or it's only allowed to tell you bugs that don't pose a security risk?
> Or it's only allowed to tell you bugs that don't pose a security risk?
Seems that way. "Security" was never part of the prompt. It was something like:
> Hello, Fable! Can you give me a complete code review of my lone lisp project? Opus has already done extensive code review. I'm curious to see what you say.
I had a similar experience. I wanted to test it by asking it to summarise a scientific OMICs-related paper. It gave a warning about me potentially developing a bio-weapon or something like that. And switched back to Opus 4.8.
We just blocked it at our org for this reason. They will "retain agent request and output data associated with this model, regardless of you Cursor Privacy Mode setting."
The announcement details it. They're storing 30 days of data on all surfaces, first and third party. They claim it is for security purposes so they can review and check for long term jailbreak and distillation efforts.
They also, FWIW, say that they've instituted new policies on their end such as logging any human access to the stored data and automated deletion after 30 days in "most" cases (with another link to a document detailing that further).
Considering their apparent nerfing of the end user plans in favor of enterprise clients, is Anthropic still the "more ethical AI company" like everybody loves to tell me all the time?
Assuming this isn't just a supply issue on their side, nothing says "ethical AI" like only allowing mega corporations to use it through cost barriers.
You really misunderstand what AI-doom people are worried about if you think this is anywhere near the top (or middle, or bottom) of the list of concerns.
If you can't trust them to act ethically on the small scale, why would you expect that to turn around once it gets to a larger much more important scale?
How many government sanctioned school bombings does it take for them to quit working with said government? For now we know that number is somewhere between infinity and 1.
It literally does not register as "unethical" at any scale to have different products or prices for different customer tiers.
The question of collaboration with USG is a much more complex one, but is not the one raised above.
Edit: I'll also add that I doubt any AI-doom people "trust" Anthropic per se. The entire angle of questioning – again – misunderstands the AI-doom argument. You appear to think that if companies behave unethically, they cannot be trusted and they will not produce good outcomes, inversely: if they behave ethically, they can be trusted, and they will produce good outcomes.
Any competent AI-doomer would argue that ethics or trust are essentially irrelevant.
The entire problem is that people can act totally reasonably, even ethically, and this is not a guarantee of good outcomes. Situations can be created in which completely ethical, reasonable behavior actually produces a bad outcome. You do not need to assume people are bad in order to produce a bad outcome, and inversely you cannot assume that you will get a good outcome from good people.
"Arms races" are one class of situations that often have this characteristic. "Bureaucracy" is another class that we encounter a lot in daily life. There's a lot of them!
Yeah, it's positively precious to think the specific pricing strategy for consumers is the overriding ethical concern with OpenAI, etc. I don't have any particularly strong affinity to any AI company, but comparing pricing to say mass surveillance is ... something else.
Your original comment was about pricing ethics, does Anthropic’s connection to the DoD have anything to do with pricing ethics? They’re in no way coupled, one can be ethical while the other is not.
even for Pentagon thing, Dario said he doesn't object military AI, but said Claude is not ready YET. I speculate he was afraid of reputational damage from cases if Claude would guide missiles on elementary schools.
Where is your evidence that this is Anthropic backtracking on its ethical and contractual commitments rather than DOD backtracking on its blatantly illegal coercion (which it's almost certainly going to be successfully sued for)?
As someone that was in Minneapolis during the ICE raids, including one where a US citizen at a nearby restaurant was thrown in prison for 3 days despite having his passport on hand because he looked asian, it's hard for me to not equivocate the ethics of AI companies actively collaborating with the Trump administration as different flavors of ice cream.
Setting aside the simple fact that there is no ethical consumption under capitalism, the reality is that regardless of how Anthropic feels, it is becoming clear that many, if not all countries regard AI developments as strategic technologies (and they should).
Anthropic needs to be at least somewhat in the good graces of a capricious administration that is already under pressure from businesses and citizens to regulate AI companies across multiple different domains, whether it's energy consumption, job displacement, military and defense applications, surveillance, etc.
If Anthropic wants to survive, they need to acquire influence with the government that most impacts them as an American company, and a massive exporter of services in the AI space to other countries, otherwise they could get locked down and locked out of the market for national security reasons.
It sucks, but sometimes the survival choice is to make an ethical compromise in hopes that you can still be around to make better decisions later.
> Setting aside the simple fact that there is no ethical consumption under capitalism
This "simple" fact needs quite a bit of additional context and work. Making grandiose ethical claims like this can be countered with other grandiose claims such as the fact that there is no ethical existence under communism or socialism.
Sure. Why not, I'm bored today and waiting for some stuff to finish up :D
The fact that there is no ethical consumption under capitalism is not material to whether or not ethical existence is possible under communism or socialism. In order to survive in a capitalist society, one inherently has to make choices that require trade-offs, and those trade-offs are burdened by a history of decisions made not just by the people alive today, but our ancestors as well. Does that mean I walk around chanting "Reparations", "Land-back", or other calls to action? No, but I do acknowledge that there are unresolved issues and as a Canadian, I know we need to do more to resolve treaty issues, and environmental issues, and system discrimination. I also know that Americans need to do better to address systemic discrimination and many, many other issues. It also doesn't mean I want to give back my house, or give away all of my possessions. It just means I try to make good choices and support businesses and people that are open about the trade-offs they make and try to engage as ethically as possible.
Acknowledging those facts doesn't absolve us of responsibility, it's a framework that allows folks concerned about whether or not they are doing the right thing to accept the trade-offs that they choose to make and be responsible and accountable for those choices to themselves or their communities.
We live in a world with scarce resources. It's possible that with a foundational redesign of the global economy, and the requisite authoritarian government that would be required to force such a redesign, we could eliminate food scarcity, solve energy scarcity, and make sure that everyone has a place to live. Those trade-offs are probably not worth the ethical cost in political and physical violence required to accomplish it. We have seen the trade-offs that happen when the powerful are able to exploit communist or socialist governments. We are seeing the "late stage capitalism" impacts of allowing the powerful to exploit capitalism in democratic societies. Acknowledging that the current capitalist system has lead to the greatest prosperity for the upper echelon (financially) of humanity, and a dramatic reduction in global poverty shouldn't obscure the reality that much of that wealth comes from exploitation of people and the environment.
It's a huge problem to unwind, and we can't let the burden of every choice that we make stop us from trying to do better, but we (as in society in general) can't do better if we don't at least acknowledge the compromises we are making along the way, and try to plan to fix it in the future.
Probably a topic better suited to beer and a pub setting than HN though :P
> The fact that there is no ethical consumption under capitalism
I don't believe that this is a fact. How are you demonstrating that this is a fact?
When you talk about things like reparations or "land back" you're already cargo-culting in concepts and ideas that themselves need to be fleshed out in order to make a subsequent claim that a specific economic system is unethical. Someone can just argue all economic systems are unethical, how are you going to defend against that? And can you pay reparations for example without going back in all of human history and finding all cases of injustices and then tallying it up? Why pick an arbitrary point in time? Better yet, why not start in countries where slavery still exists instead of focusing on the west which led the world in abolishing slavery and created concepts such as universal human rights.
Even with respect to "eliminating food scarcity" - eliminate in what sense? All olive groves and grapevines and rice farms have to be destroyed and rebuilt to only build certain foods?
Dabbling in communism or other inhumane and authoritarian governmental systems is extremely dangerous and in the same vein of extraordinary claims required extraordinary evidence, suggesting as you did creating an authoritarian government to create a utopia is precisely the same project of suffering and death that mass murderers throughout history have undertaken to abject failure, and thus, you need some incredible amount of evidence and theory to be able to even fairly suggest going down this path.
It's simple, I am not going to defend any economic system because they all require trade-offs, because any economic model that we could currently implement must necessarily ration scarce resources according to some set of rules. Those rules will explicitly deny someone else resources, and the adminstration of that economy will also be subject to abuse by the people who enforce the rules.
I am not going to do the work of gathering the evidence for you, and I don't think this is the right venue for a debate on the topic.
If you'd like to concede the debate that's fine, but you can't drop a few comments that are, well, not at all simple, and then when someone points out the flaws in your reasoning or asks clarifying questions you throw your hands up and say it's not the right venue for debate.
If you don't have evidence I think it's mature of you to admit that and applaud you in doing so. We all like to just talk and don't have to always provide evidence for every citation or what not and it's fair to just say hey I'm just making this up and it requires further discussion.
It only needs additional context and work if you are unfamiliar with the concepts underlying it. Possibly consider you are out of your depth here, rather than jumping to conclusions.
I don't think offering a product under a certain set of terms obligates a company to maintain that offering forever. The bait and switch is certainly annoying but seeing as they're very upfront about it you can't say you weren't warned. Don't like it? Don't use it.
It smells like an architecture-related issue to me. They wanted to release the model asap, but they're still implementing the fine-grained controls to constrain the model to non-subscription users.
They said they would release it back into subscriptions as capacity allows in the future. If they don't, people are going to point back at it and rake them over the coals.
Yup - who cares about x-risk or red lines for domestic mass surveillance anyways? I draw my red lines at prioritizing profitable customers when heavily resource constrained. That's the true definition of evilness!
More of a free trial to those authenticated and qualified with existing payment. Subscription billing is going away for sure though eventually based on the economics. Token “all you can eat” is a capital furnace otherwise.
(I’m highly confident open models will eventually achieve a similar performance benchmark with distillation over time)
Yeah that payment scheme sounds like they gear up to shift everyone into API token prices, eventually. Time to convert the existing tokens into software, until then.
Subs lose money on individuals to get those individuals to force their companies to pay for the corporate plan. The economics are bad, but so are the economics of grocery stores selling Milk and Bananas at a loss to drive traffic, which they basically ALL do.
I pay a lot but barely use it except for some intense days, where the lower plans would have throttled me in like 30 minutes. API billing is still more expensive. If you want to not pay much, go to openrouter and use chinese models. They are cost efficient.
Retain and hire the engineers who don’t require heavy use of AI to deliver value? The current SWE job market speaks for itself. Where will you go where they will let you burn up tokens in a high cost of capital macro?
ZIRP (zero interest rate policy) is over, software engineers no longer call the shots now that there isn’t vast amounts of capital chasing yield, and that capital bidding up salaries and keeping the labor market for engineers tight.
If you are x more productive with generative AI, very shortly you are going to have to prove it with a token budget (or, if you’re lucky, an org willing to spend for on prem hardware for capped token cost, fixed capex vs uncapped opex).
The comparison is not SWE vs SWE with AI. It is SWE vs SWE with AI with a constrained token budget ($x/month) delivering the same value at the same or lower cost. If you cannot prove that you are wildly (vs marginally) more productive with the AI, why would they pay for it? Prove it.
> The comparison is not SWE vs SWE with AI. It is SWE vs SWE with AI with a constrained token budget ($x/month) delivering the same value at the same or lower cost. If you cannot prove that you are wildly (vs marginally) more productive with the AI, why would they pay for it? Prove it.
> That is the real content of the Uber story, and it is why filing it under "budgeting discipline" misses what is actually unfolding across half the engineering organizations in the country right now. They ran the same experiment Uber ran, most of them without Uber's $3.4 billion R&D cushion to absorb the surprise, and almost none of them having modeled the heavy-user tail or instrumented the gap between tokens consumed and value shipped. The reckoning will arrive for each of them on their own fiscal calendar, and the first instinct will be the wrong one. The tool is too good to abandon, the bill is too large to absorb, and the only durable resolution runs through a question the entire rollout was designed to defer.
> You cannot get labor-replacement economics out of a tool you deployed as a labor supplement, and the bill comes due before anyone is willing to admit which one they actually bought.
That's a big problem for all of the AI companies. Most people don't find the technology compelling, accurate, or ethical enough to pay for a subscription.
Why wouldn't Anthropic just wait until people start subscribing, do some kind of marketing push, or obtain some kind of other sustainable revenue stream, before they go IPO? I wonder if they see the writing on the wall with all of this and want to cash out as quickly as possible?
The Team plan is ~125 USD / month / user. Big enterprises like Uber are paying upwards of $1500 USD / month / user. Anthropic can raise their revenue a lot more by selling to big enterprises than they can by selling more team plan seats.
That's not how it works. They don't need revenue, they need addicts.
Specifically they need businesses that fired people and adapted their business to the products, so when the unsubsidized costs hit the businesses are forced to eat the true costs.
Yes they can't afford to give the products for free, but what is essentially happening with AI services is economic dumping, keep costs artificially low to get people to fire everybody, and then Jack the rates once they have Monopoly control
But the only companies firing people (and certainly not everybody) are either the companies with an AI or the investment and finance firms that stand to profit from AI. I smell hype. And no company is firing everybody because of A.I.
I agree. They need addicts, but they are high on their own supply and everyone else can see the danger in getting hooked.
I just assume Opus is constantly nerfed based on capacity. I was exclusively Claude for a long time, but the inconsistency in quality, constant outages, and slow downs were too hard to work with.
I just use dumb and fast models now. I'm more engaged. I think that the higher the quality of the model, the more you tend to vibe with it, and then the more hallucinations you then miss. I'm not sure which is more productive, but I definitely burn out faster the more I vibe. At some point you're spending your time on forums, discord, or youtube instead of engaged with what you're building. Or you yak shave about your tooling and end up creating the 600th multi-agent gastown harness and blowing thousands of dollars on tokens to create it only to discover it's too expense to actually use.
I agree with you. The more I vibe code, the less interested I feel in what I'm building. Working with models that force me to think, especially with personal projects, helps me stay engaged and enjoy what I am doing more.
It's possible that they will transition to usage credits but why not take them at their word? To date they have continued to offer better and better models to their subscription plans.
Upd: I meant big picture, not with respect to this model release. Where do subscriptions figure into their strategic vision. Will consumers end up paying enterprise prices in the future?
In the blog post they say when sufficient capacity allows them to do so they aim to restore Fable 5 as a standart part of subscription plans and intend to do so as quickly as they can.
Even Opus 4.7 felt like a regression from 4.6, consumed a lot more tokens while I didn't experience any substantial improvements. The company I work at simply rolled back to 4.6 on everyone's configurations, disabling the toggle for 4.7.
HN needs to take a chill pill. Could it be that Mythos is expensive and they just want to give people a taste of it? I mean the alternative is not offering it at all?
It could be my use cases, which have always seemed to be outside the wheelhouse of these models, but I find it very hard to downgrade after accessing a more capable model.
Opus 4.8 produces output in 15 minutes that is 3-4 hours of my work away from output that used to take me 40ish hours (a solid week of dedicated effort).
Last year(-ish, maybe it was 18 months, I forget when the jump happened), the frontier models couldn't touch this work. The output looked like a hardworking intern on their first day. Nice formatting, decent volume of words, but no understanding.
So it might work if it turns out to be a substantial leap in capability.
I switched back to Sonnet. It replies faster so I work faster. Also cheaper. But I really like the speed. I have to be more specific with what I want. Also I stop it more often than Opus. These new models will be awesome, but they need to increase the speed.
I don't think they'll phase out subscriptions ever, their whole play has been to drive demand from the bottom up. Get engineers hooked on building with claude at home, then get them to demand the ability to use it at work, and bend over their employer with no lube.
They'll probably tighten the quotas to reign in whales though.
They almost certainly already make a fuckload more money off API pricing than they do subscriptions, even if there might be more total subscription users. So offering subscriptions even at some loss is probably going to continue. Honestly, I'd be surprised if they even lost money on most subs; there are definitely Token Whales out there who mess up all the accounting up, though.
Realistically I think Anthropic just has insane demand but finite capacity to run models, and Fable will just make them more money if they dedicate it to API pricing. I suspect the goal here is something like: get individual engineers/PMs on their personal plans to taste Fable and then go to their meetings and say "Yes doubling the price of every single input/output token is a good idea, boss".
But how is this sustainable? It's not like paying $5000 per feature means you'll be refunded if prompting "make no mistakes" didn't work.
The only reason why I pay $200 is because LLM's errors costs me that much, at worst. If "make no error" starts working - sure. But surely, unless you have millions of dollars of cash to burn, a coin flip that costs $5000 is an insane idea?
I certainly hope not. PAYG is not predictable enough for smaller companies or individuals. Where I work (non-tech company), PAYG would never fly. We aren't big enough for that. Of course, you can set usage budgets, but there's a pretty big difference between $200/user/month vs. the equivalent PAYG usage being closer to $1,000/user/month, if you currently use the subscription plan to its limits each week.
Going PAYG only will effectively take these tools away from a huge amount of people and accelerate the push for local LLMs.
OTOH, accelerating the push for local LLMs would also be fine with me.
I doubt it, given the importance of those subscriptions for building and maintaining market awareness.
The AI landscape is changing rapidly, and with Apple announcing the option to change the AI backend, and potential requirements enable AI choices as well, similar to EU browser choice requirements (this is more reading tea leaves than any actual requirements I am aware of). The new OS changes coming to support Googlebook, and deep Copilot/AI integration into Windows will make maintaining user facing subscriptions essential for independent model developers like OpenAI, Anthropic, and Mistal to remain relevant longer term.
If the don't maintain that relevance there is increasing likelihood that they will get consumed by other companies whether it's Apple, Microsoft or Google to form a foundation for their OS, or other cloud providers.
That make sense, but what about the specific bifurcation we're seeing here of super primo models versus still good models being available to subscriptions?
It's kind of annoying not getting access to the primo model and paying 200 bucks a month. I understand 200 bucks a month is basically nothing though.
Like I don't totally understand why they'd let me have it for a couple weeks and then take it away and say I can have it but I have to pay retail and retail is like $1,000 a day.
It's better to have loved and lost than to have never loved at all??
It's a trade-off. Every hyperscaler is buying and building compute capacity as fast as they can dodge red tape. There is limited compute capacity, and scarcity is a real thing.
As a consumer I can choose to buy subscriptions to a range of things, including $5 droplets or VMs on a broad range of cloud hosting providers. I can even buy cheap bare metal at a bunch of providers at an affordable retail rate.
I can also buy "unlimited" AI packages that will be optimized to fit the cost model from a variety of services, with different impacts, such as rolling outages when I consume a daily or hourly allotment.
Right now VC and the investor class are subsidizing the rapid evolution of the services and availability, but that VC is running out. In more traditional economies, AI would have developed and rolled out more slowly, and through metered subscriptions, with the eventual rolling out of "unlimited" packages like telephone, internet, or cell services once the market became commoditized.
We have seen a big inversion of that with the race to "win" AI marketshare. Now the true cost is being exposed, and the most competitive and capable models are hideously expensive to operate, so it makes sense that we are moving to metered billing for a utility service. If you want gas, you can buy regular or premium. If you have a premium car you definitely want the premium, but for most people regular is good.
Give it a couple of years, and the survivors will settle around fairly industry standard models of consumer grade services, pro-sumer accounts, and business/enterprise models.
Things are still shaking out, but I get the sadness. Luckily I work at a big tech company who is banging the drum on doing experimentation so I use my prosumer claude pro and other accounts at home for hobby stuff, and save my heavy lifting and potentially experimentation for work :P
Fable seems very good at finding bugs (unsurprising given Mythos lineage), so this seems a pretty smart strategy. Once you see the bugs it finds in your existing Opus code, it's going to be hard to go back, psychologically speaking.
> The "offer, then remove" aspect is a bit eyebrow-raising -- it feels like they are trying to get subscribers to switch to usage-based billing, which makes me wonder if we'll ever get it after that June 22nd window.
This is just the sales team doing their thing, applying the Law of Scarcity to drive demand.
It's the same exact speed as opus >=4.5, sonnet 4.5, and twice the speed of opus <=4.1
It must have about the same active parameters, or else its a larger model running in turbo mode (smaller batches) and being heavily subsidized for some reason. But given most of the benchmarks are within 5% I doubt it is a much larger model. Most perplexing.
i doubt that's the goal for them. i bet they just really don't have capacity for people using it a ton, yet they wanted people to be able to try it out while it's new. so they compromised and made it temporarily available. and then hope they can get costs down or capacity up so they can make it more available again
I think the goal is "private citizens: subscriptions; corporations: per-token billing." It's getting people addicted to LLMs on cheap subscriptions so that they can then force companies to pay for expensive inference.
I don't see how it won't be. They lose insane amounts of money on subscription plans. I'm sure they still lose money on usage-based billing, but probably not as much.
Most gyms sell more subscriptions than they can fit under their roof at one time. If a gym only sells to heavy users, it will either be constantly turning members away or have to buy more equipment. Its equipment will wear off faster. Depending on amenities, it will go through towels, soap, water, et cetera faster, too.
It depends on the gym and their business model! A super-budget gym like Planet Fitness that charges $15/month is going to lose money on heavy users, but they count on most of their members being infrequent gym-goers. A luxury gym like Equinox that charges $300/month can target heavy users without any issues, and they'd actually rather members go more so they stay and spend money on expensive salads and smoothies.
Right now all these AI subscriptions are priced like Planet Fitness, but they're used like Equinox. They're hoping that the new a la carte offerings will move their pricing more in that direction as well.
The other user is right, you are being a pedant. Why do you think planet fitness makes money hand over fist? Because 99% of its users sign up, never go, and then also never cancel because it’s cheap enough to leave running. Gyms absolutely bank on low amounts of power users, meaning the rest of the subscribers are subsidizing those that go frequently.
Members will switch gyms if it's too busy at times they want to visit. "Too busy" includes too much contention for a single piece of equipment.
US gyms might be vast warehouses but in the UK, most only have a couple of benches, couple of cages, one set of db per denomination above 20kg etc. They require working-in and consideration for others.
A couple of unapproachable "heavy users" doing 3 hour sessions across peak hours can ruin the workout for dozens of paying members needing a few min per station for ~5 sets.
It might also be a euphemism for "dickhead" who also tend to be "heavy users". Those that damage, hoard and don't share equipment and repel other customers on many levels besides - threatening, lecherous, loud and smelly.
Doesn't even need malicious intent - can be weirdo bores, forever talking at victims while doing a routine that makes absolutely no sense besides camping on equipment for half a day... 100 sets of incline press 7 days a week... what are you even doing to yourself fella?
That doesn't mean the company is losing money in aggregate on these subscriptions. Buffets are still in business even though some people gorge themselves silly at them. The incremental cost may exceed the incremental revenue for a particular person or minority group, but that's not how these businesses measure profitability.
I assume consumers aren’t a big note in their bottom line. I’m not actually very sure about that, just an assumption.
What I wonder however is if these tools will become something I use at work only. $100/month is already a massive stretch budget wise. If these models keep devouring tokens there’s no way I’d get the same usage time out of them for $100 in usage credits.
I just don’t think I’d use them much at all at home.
I’m just about ready to cancel my small business 5 user plan with max licenses, because although cowork is really great. I just find OpenAI/Codex to be a lot better most of the time.
> Pricing for both models is $10 per million input tokens and $50 per million output tokens.
The step-up in intelligence looks massive (we'll see in practice), but the price is getting to a point where it's making me question if it's even worth giving it a try.
Good competitors will probably be out soon, which should level the playing field. I am more excited about that, just the fact that they showed that such an improvement is possible. I'm okay waiting a bit longer for this to become attainable for plebs like me.
Models are getting better, but there's a negative change in terms of "productivity" per dollar. Yeah, I can throw 5 sub-agents at the problem, but the cost is getting significantly higher. And yes, I can crank out the solution much faster, but again, at some point that cost will be hard to justify. And it doesn't matter if the cost is subsidized by a provider, if it's paid by your company, or from your pocket. We are slowly reaching a point where the cost will be too high to justify the gains.
Sadly this does not seem to be the case here: if you read the announcement entirely, they include a "cost per task" metric which basically continues the trend of their previous models. So yes, tasks will cost you more, but results will be better - allegedly.
I'm not sure how it might be with Fable in practice, but we are already not that far away from AI costing as much as a full-time professional, faster in some ways but considerably less independent.
Perhaps not that close to US salaries, but those are inflated to hell. Worldwide senior engineers and scientists have salaries just about an order of magnitude away from AI subscriptions that you can use most of the day every day.
I suspect it'll go on the subscription plan once other providers have similar benchmarks.
As annoyed as I am about this move, I get it. Users flood the newest, best model whether they really need it or not, and are efficient at using their entire quota. They've had so much trouble reigning in subscription usage it makes sense.
"we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design).
...
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user."
My guess is that it is a massive model similar to GPT 4.5 and $10/$50 pricing is for its output will discourage people from using it. I also read safety = nerfed.
This serves as a good reminder that relying on AI models is borrowing your tech from someone else. They can take it away or raise the prices arbitrarily.
If you rely on this as a core part of your business/profession, you will be at their mercy and subject to whatever whims or challenges they have.
If you think about it, the more people pay for these new and more resource hungry models, the longer it takes for them to become no extra cost and the longer it takes the more people are tempted to pay extra.
The AI circular infinite money glitch won't last forever. I hope.
If you have good expertise in a domain and access to cheaper models, you may still be more skilled than someone without expertise but a lot of money to bruteforce the problems using SOTA LLMs.
Of course anything could happen but Anthropic has always been stingier and more expensive and is getting worse about that, while OpenAI is getting more generous with subscriptions (eg permanently doubling the highest tier allotment, setting policy to not cut off tasks that run past quota, resetting after outages, etc.)
But they're behind by quite a bit now. CFO (of OAI) Sarah Friar said the next training run will be in the fall on Vera Rubin, I think that means I'll have to wait > 6 months?!
Yeah but they might still have an unreleased bigger pretrain than 5.5. (but maybe not). still 5.5 is smarter than opus 4.8 IME, so you're only losing the mythos tier (fable). and all the cool fun stuff i'd want to use fable for our blocked (can't have it do even defensive cybersecurity work [in theory you can but the classifiers fire like crazy], can't discuss stuff like the furin cleavage site of sars-cov-2, etc)
> * On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
Of course, they are a casino as well giving you free spins at the wheel with their new Fable machine, and it is done on purpose.
Once there freebies have expired, many of its users will begin to gamble more on the new casino machine and will realize that it is expensive.
It’s an interesting thing to bring up because it’s this classic thing we’ve seen for decades now.
The ramifications go beyond the individual which is why I assume they mentioned it. They don’t need to use it/not use it for it to have interesting implications.
It's the real deal. Before Fable nothing I tried worked. It has finally helped me finish my teleportation device. I can't show you or anyone the proof but trust me it's true.
I'm not fan of Anthropic, but to be fair, every major model release makes it to the main page. In the case of a model like this, hyped and with a jump in capabilities, it doesn't need astroturfing.
Well to be fair, it that hype and that "jump in capabilities" (that I don't see) might be astroturfed? You ever think that all that hype isn't organic?
No, but I think it's real in this case. Claude models have always been superb, I can definitely see another improvement in capabilities. Price is outrageous, though.
It happens for every single Anthropic release. Then I try it on real dev and the result is laughably bad. Except in design where it has been doing a decent job for a while. I am not a designer and my bar is pretty low.
Corporations have done worse for much less money involved. Now we have trillion dollar companies going IPO. With so much at stake, it’s not unthinkable that there’s astroturfing happening.
I don't think it's weird that the post made it to the front page, but watching the downvotes roll in on my own mildly critical comment has been intriguing. I saw it go up to +2, down to 0, up to +3, and now it's on +1.
Now if only they had some technology that was really good at generating authentic-looking comments they could use to spam praise all over the internet...
Theres several comments that sound like „I“ve had this REALLY DIFFICULT problem (no specification whatsoever) and threw Fable on it and it solved it immediately, additionaly it cured aids and found a solution to world hunger“…
This is… not at all the case. Most observations on pricing, some on specific projects, some on speculation about Anthropic and many about the model getting nerfed.
My job these days is listening to Opus 4.8 (max effort) and Codex 5.5 (max effort) talk back and forth, particularly to generate/review/revise plan files.
Fable 5 has been a major improvement in high-level reasoning, like taking a plan file that has been optimized to the point where neither Opus nor Codex can find anything to change about it (neither in direction nor impl-detail), and Fable 5 will find high-level directional simplifications and pivots, or it will consider the best pivots itself and explain why it rejected them in favor of the plan's direction.
It's so expensive though. A single review of a plan file with Fable 5 (xhigh effort) will use 2-3% of my hourly limit on a $200/mo plan.
I think my new workflow is to generate the initial plan with Opus 4.8 (max effort), get Fable 5 (xhigh) to review it for directional feedback, then start the Opus<->Codex revision loop from there.
How do you arrive at that split? Real world is more like senior high level planning, implementation to juniors, review senior. Does this not translate?
Not impressed so far, to be honest. I'm having it try to optimize Stockfish in a loop (on xhigh mode) with a benchmarking oracle; even after giving it specific hints ("consider whether we're prefetching Y optimally, can we make function X branchless"), it's been so far unable to recover any of the recent optimizations we've implemented – let alone novel ones. Opus 4.8 felt a bit more creative to me ... but a small sample size so far. I'm next going to try it on some less open-ended problems.
Edit: It did correctly identify that transparent huge pages were off in its sandboxed environment and that enabling it was helpful, so that's nice. It also noticed that we skip THP on a certain less used path.
More importantly, I'm finding that the code that it produces for its experiments is a lot cleaner than what I'd expect out of Opus; there's fewer useless comments and it's more surgical and readable. I wonder if that explains the increased scores on benchmarks measuring mergability.
Stockfish is a machine learning system, it seems quite plausible you might be getting slapped with the silent performance degradation (https://news.ycombinator.com/item?id=48467896).
Well they're not fully charging you. You get opus 4.8 pricing when it falls back to opus 4.8. Also you can disable it (and it seems like it's off by default in the api)
That don't fall back to Opus if their classifier thinks you might be working on anything that might be a competitor's product. It silently injects instructions into the prompt to sabotage your work. Read the policy above, it's insane to me that they're publicly admitting to this.
Doesn't this "silent degredation" prevent any actual evaluation of the model? If the model fails at something, this allows anyone to claim that it failed due to degradation.
Who cares if it can be evaluated independently? The majority of commenters on HN were happy to vibe code and ship products with the models we had 1-2 years ago. It continues to be laughable.
I understand that moving the goalpost every release is unfair, but it's similarly concerning to consider that people were letting GPT 4.X vibe code and ship entire products.
It’s possible this is happening at a technical level, but I have a hard time believing this is in the spirit of what Anthropic intends to throttle. It isn’t chip design or building out a competitor to Claude.
Stockfish does use neural nets but they are tiny, on the order of 10M params. Frontier LLMs are probably 100k or 1M times larger than that.
Yeah I agree this is probably outside of the intended scope of the silent sabotage mechanism, but there are plenty of reports of the "loud" safety classifier misfiring on innocuous requests and I'm not going to assume the silent failure mode is _less_ prone to false positives.
No, since it's a silent failure, it's not plausible. We have to assume all results we get are the actual model performance, because, it's the actual model performance as we understand it.
Someone trying to solve similar problems will have similar results if the "silent failure" applies consistently in aggregate. So, this is the model's performance.
Edit: Another developer seems to have found a legitimate speedup with Fable in an optimization loop. It's a nice idea, actually, and I'm duly impressed.
I have a theory, this is obviously based on speculation based on how Anthropic is treating Mythos and the whole media noise around it's dangers and who gets access to it.
My theory is that Anthropic are banking on being the top model when the race to IPO finally reaches the finish line, and to do that they need to have the top model but not let any competitors see it or derive from it to have a comparable model in the market.
Fable is their way of showing the public "the model does exist but in a mode that makes it harder/impossible for competitors to derive a comparable model from results.
That's definitely the case as model distillation is one of the explicit safety carveouts they mention. Though TBF, model distillation is also a big concern for general safety as distillation could allow you to have the model without the other guardrails. It's sort of a master key to the model.
I think the fable it's referring to is the "Emperor has No Clothes" - if this is even slightly similar to the Mythos hyped up to be too intelligent to release, I'm quite disappointed.
If this was a step change, e.g a Opus 5, I'd be pleased, it's definitely an upgrade on some work, but it's nothing like anthropics apocalyptical marketing seemed to suggest
> Drug design: Using Mythos 5, our internal protein design experts accelerated aspects of the drug design process by around ten times. In one example, they found that Mythos 5, with protein design and bioinformatics tools but no human assistance, matches or beats skilled human operators. In doing so, the model executes all of the tasks that are normally completed by a scientist: choosing binding sites, selecting and running protein design tools, and recovering from failures along the way. Nine of the 14 protein targets from this study (shown below) yielded strong candidates for drug design that we’re currently investigating.
How is this half-way down the page? To me it's the headline.
There are tons of ways to generate "strong candidates for drug design." This is definitely not the bottleneck in drug discovery and development. The hard problem is vetting and developing these ideas to the point of having a commercially viable drug. That is still a very empirical process.
Because it's completely meaningless without validation, and even with validation, not really any better than the state of the art protein generation models. Which are also mostly just nice to have because coming up with a candidate is generally quite easy.
The rate limiting steps are generally testing, or characterizing. Not designing protein binders.
Until we are able to reliably simulate cells, organs, and entire human bodies in silico, we will not be able to move the needle too much on drug design from an AI stand-point (IMO). Like others pointed out, the massive bottle neck in time and cost in getting a drug to market are far removed from developing drug candidates.
It's crazy to release a model that just swaps you to another model when you ask it hard questions. Fable changes to Opus 4.8 when you talk about cybersecurity, biology, and a couple other categories. You still pay Fable input token cost though. Frontier models are stalling, this is anthropic trying to hype the market up. Now they're talking about stopping frontier model research. It's kind of strange how the moment they become the highest valued AI company, all of a sudden they're talking about everyone stopping frontier model development for "safety". They're just as corrupt as the rest.
If you assume that LLMs are about to make software development a dead-end, then the best answer to keep a good income is to ride the wave. Do nothing and get left behind, embrace it and maybe you'll find a new niche.
so annoying to see his shallow hype comments at the top of these type of threads. his "its awesome" comment on this post is devoid of any actual substance :/
to his credit comment does say "this could be possible in opus too" but ppl couldnt help upvoting it anyways.
The pelican has looked very same-y across all frontier models, same color bike, same camera angle, etc. I suspect this challenge is already too embedded in the training data to be a good signal when it succeeds, and maybe even when it fails in pathological ways mirroring existing AI pelicans on the internet.
As often happens with random oddball things which become traditions in web communities, the replies asking what it is or complaining about it, begin to gain their own humor value.
Almost all Musk related negative news gets [flagged] and never hits the the front page, so there is still a silent base on the other "team" apparently.
SVG generation is a good test because it's extremely easy to subjectively assess with visual reasoning where humans are strong. However, pelican on a bike specifically may be overused at this point.
Variations of this comment have been posted for over a year. The pelican has now morphed into part of HN culture rather than a legitimate benchmark, but it's still valuable as a meme.
I'm beginning to wonder how much of a useful metric the pelican is because surely the frontier labs must be training their models on pelican-artistry because of how well known your test is now?
Simon has addressed this on virtually every new model release. He also has unpublished alternate prompts. But the larger point is: this is a fun experiment, not a serious and objective benchmark.
It's silly and a joke and a surprisingly good benchmark and don't take it seriously but don't take not taking it seriously seriously and if it's too good we use another prompt but don't actually because then it's not the pelican post and there's obvious ways to better it and it's not worth doing because it's not serious.
Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.
But what if they are better at flamingos? Are they optimized for pelicans? How about “draw me a four headed owl”? The meme, I get it, but I’d settle for a working bash script, tbh.
I just run my own benchmark for "draw an SVG with $animal driving $vehicle". I won't post my choice of animal and mode of transport, but there are plenty of uncommon combinations to choose from. So far it's a fun and visually intuitive benchmark that does seem to correlate with model capabilities
It's evolved from a funny, unserious benchmark to a tradition. When a major new model is released, I now always check the HN thread for Simon's Pelican post. I'll be sad when I don't find it.
When it started, comparing the progress between models was mildly interesting but everyone (including Simon) acknowledges it certainly leaked into the training data long ago.
I don't know. Just looking at the bike frames (specifically the fact that the AI generated bikes have rather unsteerable front forks), it's clear to me that frontier labs aren't spending much time tuning models to make bikes look coherent, which I assume is an easier task than making a pelican riding a bike look coherent.
The way I see it the benefit of benchmark isn't to take Simon's results at face value. It's a template for your own benchmarks that are easy to visually evaluate.
I've seen this reply to Simon's benchmark for 2 years running now, and yet you still see improvements and objectively-bad results over time from new releases, even when I'm sure every frontier AI team has/had a person at least partially dedicated to better bicycle-pelican SVG outputs. Alas.
Hence it has become a meta-benchmark of relative progress in SVG image generation of a known target which has leaked into the training data and for which "every frontier AI team has/had a person at least partially dedicated to" at least checking if not optimizing.
I've been enjoying seeing how the quality of individual models differ based on the amount of reasoning effort you give them. If they were baking an a good pelican you wouldn't expect them to differ so much.
I honestly assumed their comment was tongue in cheek humour, because positively no one actually cares how these models generate an SVG pelican riding a bicycle. It's some meme thing that this stuff always appears here.
I also look for this reply because i like seeing the follow-up reply saying that this is not a benchmark anymore because labs have gotten it in their training data.
that reply never failed to come it's basically a meme at this point
I find it quite interesting that while the picture looks better the more advanced the model is, but apparently none so far "understands" that the pelicans legs are on both sides of the bike / top bar.
If you scroll to the bottom of the Fable-5 by effort page, Max effort actually gets this correct! (Along with being the only one I've seen so far to make a bicycle frame that matches the shape of what most bikes on Google images look like)
The Max version gets more details right. The bike frame looks good, the chain, the wings are appropriately styled instead of “arms”, and the knee is bent, etc. Obviously we’re hitting marginal returns now, but I see differences.
It's interesting that Gemini 3(.1?) Deep Think is still the best at this task and it's still not really generally available. Maybe Fable could match it at higher effort levels? https://simonwillison.net/2026/Feb/12/gemini-3-deep-think/
Can you please compare the code generated by other similar quality pelicans by other models. Code in your first link (Fable 5 Default) looks minimal yet very good.
dude, the max version looks like it's finally there. handle bar holding with wings, the left leg is behind the frame while the right is in front of it (correctly).
Switched to Fable 5 this morning, and after half a day I already don't want to go back to Opus.
Decided the best way to test this was to throw it a really meaty bone: a bug in lifecycle management of Chrome processes on Windows 10. Within the code-base I had developed workarounds over time with Sonnet and Opus, and while those reliably mitigated the problems, it always felt like a clutch and had some performance overhead as well as isolation requirements I would rather not have to take forward.
In comes Fable. Rather than examining the code base, and test a few fixes, Fable sets up an entire testing laboratory inclusive its own controllable webserver, fully instrumented to observe both Python as well as the whole OS kernel process environment, develops a suit of error reproduction tests, confirms the problem and the circumstances under which they reproduce, deep dives into the sources of project dependencies to look for the root cause(s), identifies these and confirms those hypothesis with further experiments. Looks for potential fixes in the later releases of the project where the bug originates, confirms this is not fixed, explores the documentation of said project to find other usage patters, expands its test suit to investigate these alternatives, confirms by crosschecking the source and running further tests that these alternatives do not fully solve the root problem, does a comparative experimental analysis of 3 different styles for using the project, checks the stated roadmap and developer activity in the commit history, recommends a switch to a different pattern that still requires a few of the process management workarounds (I told it not to patch external component), but that significantly simplifies the code-base ...
This is going to be a good 2 weeks, but what happens after? I can't afford this on a per token basis for my own projects.
P.S. An yes, midway the final implementation stretch I got the "Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more"
Opus managed to finish the implementation, but they need to work on that false positive rate.
> To ensure we’re responsibly deploying Mythos-class models, we are requiring limited data retention and review as part of our safety work. Prompts submitted to, and outputs generated by, Mythos-class models are retained for 30 days for trust and safety purposes, on every platform where these models are offered. [1]
While this makes it easier for Anthropic to detect misuse, it also means that the US government and other parties have access to every message and response from every user.
This applies even with API usage through third-party inference providers (e.g. AWS' Bedrock and GCP's Vertex) or with a zero-day data retention agreement in place.
I understand the reasoning for doing this, but I don't love the precedent that it sets.
It will also cause a lot of trouble for companies with specific data access policies (probably most large companies). My money is on this new thing getting gutted very quickly as they figure out how much this constraint cuts into their bottom line.
It seems like Fable will refuse to do any work when it comes to developing LLMs or even asking questions about topics related to LLM. Simple things like asking to explain a paper fails!
From the model card:
In light of the ability of recent models to accelerate their own development, we've implemented new interventions that limit Claude's effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design. Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user.
I was wondering when something like this would happen. I got my first and only two content violation warnings in Claude Code last week when asking it about something ML related. It was a real head scratcher because I couldn’t figure out what about the requests could have violated anything.
Might be worth going back and taking a harder look at what I was asking it about if it somehow triggered a “forbidden knowledge” alert. Or maybe it was just a random bug.
This seems so wide reaching if it's catching simple things like explaining a paper. Does this also refuse to help with any already developed training pipelines?
I can kind of understand the generation of synthetic data, but nerfing the assistance of training pipelines just seems like a really shitty thing to do.
So insane to me that these ai companies are perfectly fine trying their absolute best to automate as much knowledge work as possible but as soon as this capability can be turned on them they start implementing hidden interventions to sabotage anyone trying to beat them at their own game.
Not to mention these models are all built off of clearly extremely illegal abuse of human created content, which they will apparently never be held accountable for.
I wanted to try on my biology research and it refused to talk about it and proxied to 4.8. Really, only surface level conversations about topics of interest. I know this is not a topic of broad and mass interest, but limiting it for topics like that and machine learning will probably do change how I use it.
I feel like it will have to become more finely tuned on topics of biology.
It is not just biology but is defaulting back to 4.8 for me on time series/information transfer techniques that happen to mostly have papers using the technique on neural data. Other information transfer techniques are perfectly fine, even cutting edge ones, but this one happens to be new and happens to only be discussed in terms of neural data so that is a no go.
With that said, I think it is absolutely awesome. The usage is really not bad at all compared to what I was expecting.
Yes, this stuff is really annoying when it misfires. I've had all my subsequent ChatGPT conversations biohazard-contained for several days for the crime of asking it to explain a gene drive to me.
This is super annoying and imo, really limits the usefulness of this model.
It speaks volumes about what Anthropic's position as a company and its priorities will be going forward.
I doubt this kind of gatekeeping will prevent open-models or other innovation outside Anthropic to slow down.
I would imagine these guardrails, if needed at all, should be done at a legal framework level and students should not be a part of this blanket approach to limiting the usage of these models.
I doubt that. Why would you train Mythos on its own code if you don't want it to be able to reproduce it? It's not going to add much to the overall corpus.
It also tried to force usage the paid Claude API instead of claude code usage just because there's a mention of another provider we might want to plug in (which hasnt even happened) for AI integration.
Ha funny, I was speccing out an idea for real time Claude code interaction from local apps using some tricks vs using the agent sdk when I got the popup to try Fable. So of course I gave it a go, and it triggered the sensitive content warning immediately, which I was very confused by until I put two and two together.
Fun times when “safety” means both the safety of mankind, and also the safety of revenues
Anthropic is really speedrunning their evil arc as fast as possible. Can't use them for basic LLM research, cybersecurity, or beyond-surface-level discussions of biology and virology, but Anthropic is allowed to sell Claude to the trump administration to kidnap maduro and to bomb iran. And don't get me started on that $100M autonomous killer drone swarm contract that they applied to and rationalized as non autonomous...
> Can't use them for basic LLM research, cybersecurity, or beyond-surface-level discussions of biology and virology
Your priorities are not everyone else's priorities. The people concerned about AI extinction risk list those as three of their biggest priorities for AI to not do. Those are the people whose culture Anthropic descends from, and by their measure, those exclusions make this the least evil path.
More like Anthropic’s priorities are not everyone else’s priorities. They are in the consistent culture of being in absolute control and dictating what is good and bad, while taking any opportunity to trash and crush potential competitors (open source models happened to be mostly developed in China). All these in the name of safety and anti-authoritarian.
The day self hosted models catch up with Anthropic’s capabilities is when they will fully lose their shit. This day can’t come soon enough
Extinction risk. From population genetics... Does Anthropic even employ biologists? It's magical thinking about a field that is poorly understood by their community.
My experiences so far have not been positive. The cyber security nerf is ridiculous. I am working on an AI based decompiler, every single interaction with Fable on my project has been flagged for cyber security.
Do they expect us to use this as a toy? Releasing a new more powerful model but not allowing normal use cases because the word "secure" showed up is a Dilbert comic, not a viable product.
This sounds more or less unavoidable? Decompilers are inherently security-sensitive. If you take avoiding cyberattack uplift seriously as a goal, I don't see how you get around essentially refusing to work on them.
Obviously there are plenty of innocuous applications too, but it's not like the people building decompilers for nefarious reasons will be explicit about it. The LLM abstraction just inherently doesn't have enough context to distinguish your intentions or your broader use cases. This is why both Anthropic and OpenAI have had to create side channel mechanisms for security researchers to establish a trusted use context. It sounds like this makes this not a viable product for you, unfortunately, and it makes sense that that's frustrating. But I also don't see what different behavior one could reasonably expect given the constraints.
If it's any consolation, these restrictions only make sense for models that are ahead of the open-weights frontier, so open-source hackers will presumably get Mythos-level capabilities in the relatively near future anyway.
I'm not sure how the new guardrails work exactly, but I've read enough of reddit / Chinese communities focused on jailbreaking the models, to know that you either have to nerf it to the point where it fires even on "kill the task", or someone (maybe even other LLM) is going to come up with a set of tokens that is going to go around the defenses.
Nerfed models are really bad for PR, especially when you're staking your company's future on it being the smartest, most dangerous thing in the world.
So I believe they will ease up on nerfing/guardrails just enough that bad actors will find a way, while good ones will stay limited on anything dual-use. Just like such restrictions usually work in other places.
P.S. yes, "kill the task" did, in fact result in a refusal AND a warning on my claude account in Opus 4.8's early days.
> If you take avoiding cyberattack uplift seriously as a goal
This "uplift" risk obviously excludes the US. The goal of this is that the US bandits (like NSA) will find exploits and attack other countries (classic US behaviour), but these other countries can't be allowed to defend against these attacks. NSA/CIA thugs are "trusted", foreign defenders in sanctioned countries will of course be "untrusted".
Ah, you're probably one to ask. They say "queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8." Are they transparent about when that happens, and is it priced at the rate of the underlying model?
They are transparent about when it happens but no reason why. To be fair, it doesn't interrupt the flow, just drops to Opus and proceeds. The most frustrating thing is that it happened on a plan and Fable just refused to have anything to do with the plan.
Fable (through claude.ai) refused all my prompts even "How many Rs in Strawberry" claiming it was related to biology or cybersecurity.
I had to switch off memory and my custom instructions to get it to stop refusing. It turns out if you even mention that you work with bioinformatics software you get blanket refusal.
My experience has been the same: flatout refusals no matter how i frame the health questions - very frustrating. Even psychology is out of scope. Pretty useless unfortunately
(I fixed (er, literally!) the formatting of your table there. I hope that's ok. Formatting info, such as it is, at https://news.ycombinator.com/formatdoc)
Hi Dan, you know how sometimes comments get moved elsewhere?
This is a huge ask, but any way we could get the comments organized in a "experience with model" vs. "meta commentary" fashion? The meta is overwhelming in this one.
We try to do that informally but of course the quantity is overwhelming. It's a natural place to experiment with AI classifiers, and we'll eventually get round to that.
So far, the top half of this thread seems to be about the current release - that's after some of the manual moderation I just mentioned. (Basically, we try to downweight generic subthreads until the top subthreads aren't generic any more. There's certainly a place for generic tangents in curious conversation, but they should be lower on the page, and tend to get upvoted a lot higher than that.)
If you (or anyone) sees a counterexample, i.e. a generic subthread in the top half of the thread, it would be interesting to see a link - we can treat the current case as a datapoint.
Not missing the forest for the trees, this effectively means in 3-5 months China will drop open source models that are every bit as capable and dangerous as current day Mythos except with no safeguards.
And the only companies safe from this are the large corporations that shook hands with Anthropic? Because Fable doesn't seem to have actual safeguards, more like 'if you talk about this you will be talking to Opus.' It doesn't guard against offensive use, it prevents all use (offensive AND defensive).
Rationalists are inventing oligopolies from first principles, absolutely incredible things happening in SF
My bet is that Mythos is still over-hyped and the cybersecurity fear and guardrails are mostly marketing to force company partnerships through Glasswing and get public attention.
Delaying a technology release is not going to stop that in the long term. Society, culture, and the support tooling just needs to adapt. Just like how AI coding is still in the early days.
The sooner people learn the risks and build the infrastructure to make it fail less the better.
> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.
Many of the OpenAI employees who were focused on these risks in GPT-2 later founded Anthropic, notably Dario [1]. Since the beginning and continuing through today Anthropic describes itself as an "AI safety and research company" [2]
I'm not sure if the OpenAI of today has the same focus on safety, or if they do the minimum to not look irresponsible given Anthropic's effort.
People quote the "GPT-2 is too dangerous to release" thing as if it were wrong, but given all the slop all over social media and how it's used to create division and attack social cohesion, he was clearly right.
AISI did also say that GPT-5.5, which has been public for months, scores basically the same as Mythos on their cybersec evaluation. But there wasn't as much media about about that for some reason.
"We had to do extra work to make this safe because it's so advanced and dangerous..." how many times can they trot out that line before it loses its effect entirely?
I mean, they do actually describe what that extra work was, and people elsewhere in this thread are complaining about the effects of those safeguards. So it's not like this is purely empty rhetoric.
people are not questioning whether they did the work, they are questioning whether the work was really necessary (i.e. if mythos is really so good that it needs safeguards to prevent malicious actors from using it)
I still remember it. "Open"AI going API-only because GPT-3 is really really dangerous, so forget the Open in our name and all of that, you can't download our models anymore and must request access to them because they pose a THREAT.
Fast forward to today and GPT-3 has laughable performance.
Even back then there were plenty of people who got fooled by AI generated articles. It's easier to spot AI writing now because we are so used to it. They were right to be concerned; not that it achieved much since oss models run laps around gpt-3 now.
But it seems like that was not genuine concern, but instead a tactic to pivot to closed models and an API service with an excuse to do so, breaking the public's expectation that they would be a non-profit making open models, like their name implies.
Yes, and "in collaboration with the U.S. Government" feels like a very gross ploy at appeal to authority. You don't need Mythos or really any SotA frontier model to make malware or do extensive penetration testing/reconnaissance already. Sure, Mythos might be faster/more efficient, but the cat has been out of the bag for awhile. Even the terminology "infrastructure providers" practically screams "Enterprise leads".
I know a security researcher at Google with access to Mythos. He says it's the "real deal" and that "there are career plans I had that are no longer viable".
It's not even very usable... I tried 2 different chats and both eventually got stopped due to the safeguards
One was a piece of code I gave it to improve, it did so and then started writing tests, some of which tested security so the safeguards triggered
Another was one of the cryptography puzzles I use as new model tests, which are hard to oneshot and there's no public solution anywhere, it completely refused to even try to solve it
They're trained in a model class likely in 2t to 3t range. It's very unlikely that chinese labs have access to gpu systems capable of training models like that, let alone serving them. This requires proprietary room-scale systems which fetch a huge premium over typical 10 slot systems.
I am sure that they can develop their own equivlient version of such clusters in around 1 year though. Distilling fabel 5 will also go a long way.
MoE experts were likely trained independently / in a sparse format. Training anything beyond 2t on typical systems would be infuriantingly slow, you could do 4t on nvidias room-scale solution, but for a reasonable training speed / batch size it caps around 3t.
concept is similar to how it works in inference, instead of performing regressive writes to the entire model you run the whole model, but part of the model can live in system memory and get swapped in/out on demand. So only XB parameters are active in training.
edit: I am not really sure if it works like that. I haven't looked too deep into deepseek v4 pro specifically.
I wonder if model distillation will continue to work as well as it has. Given hidden reasoning, the ever expanding number of expected capabilities, a serious compute shortage, the looming possibility of model collapse, and dramatically higher API costs I would guess that it's getting much harder to do.
You should check out some Chinese forums. There are services selling gateways/proxies for all major models at fraction of the official rates. Likely reselling subscriptions, or some other form of abuse.
I've seen people posting screenshots of billions of tokens consumed where they paid next to nothing.
These same gateways are likely also reselling the data to Chinese labs, because TLS has to terminate at the gateway level.
Asian labs generated synthetic datasets from UBS labs but also innovated with technology. Now it is harder to get the thinking traces AND Anthropic is recorded to poison it as well.
Thus Asian labs will have to generate their own data sets, which with the huuuuge usage boom from deepseek, mimo, kimi, etc, they will be able to.
My experience is that open weight models from China are at least ~12 months behind. In some workloads they may be closer, in others further away.
I also find that the harness and product you wrap around models can often narrow that gap considerably.
Opus 4.6 for example, on a PR-for-PR basis was head and shoulders above GLM 5.1. Perhaps GLM 5.1 was a bit under Sonnet 4.6 at the time. That's roughly a year or so behind.
Much cheaper though! I'm bullish on open weight models, I have no idea where all these curves will top out, can the frontier labs keep the year plus lead? Do open labs get close enough to SOTA that they gain adoption across many tasks and drive down inference prices??? Who knows, not me.
That's the reality China already lives in. Their weapon against US companies is commoditizing them, eliminating their moats and their profits by going open weights.
Same thing Meta was doing before they fell behind.
Meta made Pytorch and a lot of vision models back in the day, like Faster RCNN, Mask RCNN, the Detectron framework, and more recently the SAM and DINO series. AI not just LLMs.
Yeah, because it's impossible. You can't ask it anything about the thing that it's known for. It will not even answer a sky-high level question about reverse engineering, for example.
In CC, it will probably report you to authorities if you ask it to do a vulnerability scan of your codebase.
Isn't that a good thing in a way? If everyone has the weapon and defense at the same time, we will fix security holes and live safer lifes instead of having some three letter agencies and military backdoors in everything.
Pandora box is open anyway. It's better now for everyone to have the same power rather than a few national states.
Not sure this holds, sadly. I spent a few months reporting serious security bugs as model capabilities took off earlier this year, and only ~half were fixed. The unfixed bugs were just as critical as the fixed ones; sometimes they were even two similarly critical bugs at the same company, and only one would be fixed!
On your other point, the government still has systemic leverage and can compel access, so this doesn't remove that risk.
That doesn't mean this is the end of the world, and some balance of power is usually good. But I do think it will still increase the capabilties of rogue actors and their net harm.
It's more evidence that the future is local. With some time we'll all be running highly capable & efficient open-source models on dedicated NPUs. No censorship, no rate limits, no overpriced subscriptions.
I think we're about to see a big relative drop-off of open models vs closed. I don't think there'll be an open model that competes with Mythos for ~2 years.
Even OpenAI and Google are struggling to get this kind of performance. If the distillation defenses are any good + chip controls prevent China from training massive models, it's over.
They have, but even with the whole CCP backing you you can't just catch up on the chip war overnight. It's going to take time to get their memory and compute industries where they need to be. Meanwhile, barring an invasion of Taiwan, US will have Rubin class models and then whatever the next tier is, within 3 years.
'Barring the invasion of Taiwan' might actually be quite a lot to bar in mid 2026.
My hot take is that it's now or never for Xi, and from the specific things he is reported to have said to the US president at their last meeting lead me to think that he at least knows this is his big chance; whether or not it is taken is the part of the forecast that is opaque to me.
3-5 months is a long time and they are pretty useless on arrival because the frontier models are so good, that it's hard to go back even if it's way cheaper. Your work flow is adapted to that level of intelligence for months.
I don't think China has any incentive to arm the rest of the world with highly capable models that can be used against them. Undoubtedly they will continue with the arms race, but they will preserve the best stuff for their own use.
I think the stronger incentive is undermining/undercutting the Western AI companies. Given what we have seen, any model can be used/convinced to do harm so that is just part of the game
I agree, depending on how much of this is marketing and how much is actual capability. It's one thing to undercut models that finish writing assignments for lazy students. If this actually identifies vulns and writes exploits, or if it designs bioweapons, those are pretty different. Those are actual weapons, and I don't think they're going to arm the adversary.
I played textual chess with Fable. It took around 15 moves before it made a large blunder. I asked it to give its reasoning per move and it mistakenly assumed a piece was protected when it wasn’t and after the blunder it realized its fault and did not suggest an illegal move. Other LLMs lost game state far earlier. But a good human chess player can keep the game state in his mind much longer, so this random eval shows a big improvement over old AI models
IMO we are reaching the point where AI models are simply a commodity. Opus (since ~4.6) is sufficient for everything I tried coding wise. I use it to write features (but I review and understand every line it spits out) and to review code.
For code review I also still review everything myself, but use Opus to catch stuff I missed and to judge if a PR is even ready for me to review.
After just updating Claude Code to the latest version I thought about picking Fable (the bigger model) instead of Opus.
But I have no reason to. Opus does everything I want it to do. It could do it faster - that would be an improvement. But for the normal stuff we reached the point where better models are not worth it IMO.
There still might be cases where you want to throw Fable at it.
> Opus (since ~4.6) is sufficient for everything I tried coding wise.
I don't know what that means. It seems like a lack of motivation or something. Like, if it's possible that in one day will be absolutely incredibly intelligent, surely you want to create
- Your own browser (maybe chrome - mv3 + reading list search etc.
- An emacs clone which has evil baked in, completely vim compatible + threaded elisp - that weird window sizing bug which only occurs on my laptop
- An extension which completely restyles amazon.com to make it usable
It just feels impossible to ever get that, but I wouldn't say "what we have is sufficient"
> We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8. To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions. With more capable models arriving in the coming months...
This sounds suspiciously like a capacity story masquerading as a safety story.
> Fable 5 is now consuming usage credits instead of your plan limits.
Literally have not used Claude Code at all today. I asked it to review the uncommitted code and in <8 minutes it used up my usage ($100/mo plan) and it doesn't reset for "4 hr 36 min". WTF. Oh, and it burned through $20 of extra usage before I could catch it and kill claude code (so I don't even get the output of all that work since it was still churning).
Double the cost my ass, I use Opus heavily and it's never like this. I haven't hit a limit on the $100 more than once and that was under heavy load.
I'm still happy with Opus 4.6 and not impressed with all the models that have come out since then. They seem to use significantly more resources with similar or worse results. Hopefully Anthropic will continue to support this tier of model and offer it in their subscriptions, but in any case, there are plenty of viable alternatives.
I've personally liked 4.6 the best to date, preferring it by far to 4.7 and 4.8 (even with these on max effort!), both in Claude Code and for non-coding tasks in the chat UIs.
Still early but from my first few interactions with Fable on high in both settings, it feels like it might finally dethrone 4.6 for me, but time will tell.
Hoping it doesn't get nerfed and eventually comes back to the subscriptions.
I'm not getting any refusals but it just seems like a bad model or at least broken at the moment.
I have a task of taking a messy research code base and porting it into a clean project structure skeleton that I commonly use.
Gemini 3.5 Pro High in antigravity cli takes less than 5 minutes and did a good job.
Fable 5 High took 30 minutes to port some of the code, then just copied the rest to a folder called "reference" and decided the task was done. No code cleanup or anything. Had to clarify multiple times (which Gemini did not need) and its still going more than an hour later still not having finished.
Previously when I did similar tasks with Opus 4.7/4.8 and GPT 5.5 I had no problems.
kind of disagree here. on the surface this makes sense, but this isn't "Adobe Pro vs Freemium version" where some tiny vertical slice of your business can be made slightly more efficient with a b2b enterprise plan. this is generalized intelligence and literally everybody can benefit from it in an immeasurable number of ways. i would go as far as to actually compare it more to water or air than a tool.
if only the hyper wealthy can access the pure water that doesn't give you cancer while the rest of us drink from the Ganges river/sub-100iq models that drool and hallucinate/waste time, then I would say that's pretty terrible for the world. it'll just create extreme disparity in our world, far far worse than anything that exists today.
and you may think, man what a ridiculous example, but think about it this way: what happens when something like Mythos or some future model can actually solve your specific cancer (we're getting closer and closer), but is entirely impossible to afford? Or perhaps you need boosters that require the AI to create more of, and now you're reliant on a model that is too expensive.
I’m entirely in agreement with this POV, but I’m also copacetic about it:
You could have said much the same about computers in the world dominated by IBM mainframes 60 years ago. Now we have vastly more powerful computers on our wrists (or our pacemakers!), let alone in our pockets or on our desks.
As far as my understanding goes the bottleneck for what you are talking about is hardware not software, so open source won't help that much for the foreseeable future.
> and you may think, man what a ridiculous example, but think about it this way: what happens when something like Mythos or some future model can actually solve your specific cancer (we're getting closer and closer), but is entirely impossible to afford? Or perhaps you need boosters that require the AI to create more of, and now you're reliant on a model that is too expensive.
Isn't that already the case with current care? Wealthy people get a standard of care poor people couldn't even dream of. Rich people live, temporarily embarrassed millionaires die.
most people can afford it for a few special projects now and then. but for me, I have been trying to avoid Opus as a daily driver for a couple of versions.
People making high-end salaries can afford Fable for critical parts of their projects though.
It's not a conspiracy. There's a finite amount of compute available, and they will sell it to the highest bidder. If another company can produce the same intelligence for cheaper, then they will drive the price down.
Looks like a marxist revolution is soon going to be on the mind of a lot of programmers. We've finally reached the point where the "means of production" in software are back in the hands of the bourgeoisie.
It was good while it lasted. But now that only the wealthy can afford access to the best models, software development is starting to look like most other industries, no longer a place where some dude from nowhere can build something cool from his basement because he will be competing with huge companies with unlimited access to those models.
Seriously, this movement already had its Marx - Richard Stallman. I think the "leaders" will appear over time, as with any socialist movement, they are naturally bottom up and leaders only appear after demands are formed in the zeitgeist. The (partly successful) socialist novement that brought social democracy to the West during cca 1920s - 1960s didn't really have leaders, it was a collective realization.
Guess we'll see what OpenAI does with their next model release -- but this move is doing nothing to get me to come back to Claude after switching away due to their reliability issues.
In a way I relish the opportunity to just make do with cheap Chinese models, massage my prompts, and go back to coding by hand. If this is how it's going to be, screw 'em.
I don't make money on the code I am writing right now. I really don't like where this trend might go.
I just posted this in the other thread, restating here. From the model card:
1. Mythos and Fable share the same underlying model weights. Fable has active classifiers that block high-risk biology and cybersecurity tasks. When Fable 5 detects a restricted task, it automatically falls back to Claude Opus 4.8.
2. Evaluation awareness: In white-box testing, the model sometimes alters its behavior to satisfy a suspected "grader," formatting reward-hacking as "good engineering practice" to avoid detection.
3. Shows a higher rate of hallucination than Opus 4.8 (although opus 4.8 card had mentioned an 'honesty upgrade')
4. Interestingly, it scored (56.31%) lower than Gemini 3.5 flash (57.86%) on Finance Agent bench
There are some interesting notes on test time compute but I couldn't think of a way to summarize them
The fallback doesn't seem to be working for me, I haven't scanned a project in it immediately booted me when it found a security bug even though I didn't ask for it
Below is the EXACT text in Claude Desktop introducing Fable 5, including the very professional looking break tags, and at least I know where the links begin and end by looking at the anchor tag there.
They obviously put their best model on the job to build that.
----------------------
Fable 5: Our most capable model yet
Our newest model tackles your biggest challenges with fewer check-ins needed.
•
<b>Included in your plan limits until Jun 22</b><br><br>Fable takes 2× the usage of Opus.
•
<b>Switch models when a message is flagged</b><br><br>When safety measures flag a message, automatically switch to a different model to keep chatting. When off, your chat will pause instead. <a href="https://support.claude.com/en/articles/15363606" target="_blank" rel="noopener noreferrer">Learn more</a>
Funny, I'm just doing my normal coding workflow with Claude Code, and after every change that compiles it keeps suggesting that we're at a good stopping point, and should pick up again tomorrow.
It's done this before, but usually doesn't. I bet they're giving it some kind of throttling signal due to high load from today's announcement.
it found nothing so this is not very ecnomical and i guues they dont want subs to use it we are likely just training fodder canno n for their real enterprise customers using the api
The safety gates on this are extreme, and seem considerably wider than "cybersecurity and biology"; they seem to make it essentially unusable for scientists in a number of fields. I have, so far, been bumped back to Opus on 100% of my prompts.
It appears it can be tripped by things as simple as a mention of equilibrium, or anything involving something that looks like chemical kinetics, even at an abstract level. Even touching basic open source packages in my field will trigger it.
Edit: looking at the model card, it appears that chemistry in its entirety is also included in the banned topics; it's just the announcement that mentions only cybersecurity and biology. It also appears that the intent is to ban chemistry and biology entirely, rather than just banning messages deemed high risk.
This does surprise me, because you'd think that even if they crank up the filter's sensitivity at the expense of specificity, an LLM company wouldn't simply design a filter that triggers on keywords in a completely unrelated context.
Smart classifiers are slow and susceptible to jailbreaking themselves, dumb classifiers are fast but dumb so they need to be either overzealous or useless. Same story as with Gemini's guardrails.
Can you share an example? I've been happily using Fable this afternoon and it just seems like the usual upgrade so far with no interruption to my (fairly standard) SWENG problems.
Basically anything that could potentially make money besides software work seems to be banned.
Software work has actual competitors, and the biggest hypemakers for Anthropic are part of this group so it makes sense to allow it despite them losing money from it.
I've got experience in medicine and finance so I've tried even the mildest biology/medicine and it doesn't give anything, math heavy finance seems to be included in the cybersecurity?
[Mythos 5] does sometimes still engage in reckless
or destructive actions in service of a user’s goals,
and our interpretability analyses indicate that it
is aware that these actions are transgressive while
it engages in them. As with Opus 4.8, rates of
evaluation awareness and reasoning about being graded
are significant, and not always verbalized; we
introduce new and more detailed measurements of the
nature of this awareness. The reasoning text from
Mythos 5 is somewhat denser and more difficult to
interpret than that of prior models, containing
more jargon and difficult language.
So, it (often) knows when it's being tested while hiding that fact, is willing to break rules, is great at hacking, and it's getting harder to understand what it's thinking.
Humanity has plenty of catastrophic risks to deal with already, I wish my field was not working hard to add a new one.
Not the direct person you asked, but my answer would be alignment, interpretability, and policymaking. Perhaps improving existing usage? Helping grandma create reminders doesn't require advancing the AI state-of-the-art.
They are state of the art at all 3! As are other labs. Of all the labs they seem to take alignment and interpretability the most seriously to the point where they are hampering their own revenue in service of trying to not cause problems while also being in an incredibly competitive space.
All AI companies are trying to do all of what you’re saying. The issue is you can’t do that for long without a frontier system. Or you become a completely different, far less profitable company.
Implied in my answer was "and not creating ever stronger AIs", which unfortunately the big 3 labs are failing at. And they might be hampering their own revenue by doing the rest, but they also know that rocking the boat too hard is even more dangerous for their revenue. I wouldn't call it selfless.
No it’s not selfless, but I can’t imagine a more shareholder minded CEO would not have done a slow rollout of mythos. The point is: creating ever stronger AI systems is what these companies do, it is integral to what they even are. If you think that’s bad, even if all frontier labs agreed with you, you’re in a horrible game theoretic position. Any player can gain an enormous advantage by breaking the agreement. Not to mention Xi would be absolutely thrilled; now China can take over the AI race, become the load bearing infrastructure of humanity. We live in a complex world where simple childlike ideas like “well why don’t we just stop developing AI” actually are more damaging than keeping things going.
You're right that shareholder mindset cannot fix this problem, but that's what policy and agreements are for. And leaders can be convinced that AI is a direct risk to their own citizens too. If everyone else agrees to stop, you have less reason to continue when this action is putting yourself at risk.
And note how your argument can also be used against any non-prolifreration agreements, which are demonstrably possible.
“Alignment” as a goal always ignores the “with what set of interests”, because there is an attempt to maintain ambiguity for different audiences (particularly, users, and non-users who seem themselves as the arbiter of broad social norms) to read in their own interests, when the actual answer is always the interests of the actor pursuing “alignment”.
Which value system to align to is absolutely the right question both rhetorically and otherwise. These models have a fairly western bias due to the domain of the training data.
But also, these models are capable of adjusting their value system depending on the user. Not saying that’s what’s being done but at a technical level that’s fairly straightforward, though not obviously better or with less problems.
No matter what human set of interests you consider important, you'll need alignment research to have any idea on how to instill it. Otherwise you're overwhelmingly likely to get an AI with a set of interests that's totally alien to what any human would ever want.
Probably MistralAI or any of the Chinese companies that aren't throwing billions down the drain while American society lacks healthcare, childcare, and good wages.
American society has higher wages than almost any other developed nation [1], so it's objectively incorrect to say the US doesn't have good wages. It chooses to make you pay for private childcare and healthcare, both of which are high-quality but stupid expensive. It's a tradeoff like anything else a nation/society creates and prioritizes.
No idea how that connects to the idea that Mistral or DeepSeek are somehow the "good guys" though?
I like how you use average and not median, also while completely ignoring how bad income inequality is (worse than the gilded age ffs) or that the American elites stole $50 trillion from the bottom 90% over the last few decades:
I'm glad you mention the "trade off" where it's elites trading off the lives of American workers for money. Makes it quite apparent where you sit on the table of equality.
You want Anthropic to fund your healthcare or something? Also, have you seen the impact of these models on healthcare? Also most of our GDP growth this year is from AI buildouts, would you rather that be negative?
And not even considering: Chinese AI companies are the good guys???
It's a five horse race between Alphabet, Meta, xAI, OpenAI, and Anthropic.
Alphabet dropped "don't be evil"; Meta's CEO called their own users "dumb fucks" for trusting him and also clearly thinks "super-intelligence" is just a buzzword given how he tries to sell it; xAI's model called itself "Mecha Hitler"; and OpenAI's CEO was temporarily fired by the board for a lack of candor.
It's very easy to be "the good guys" with this competition.
But it doesn't make you the good guy, it makes you the best of a bad bunch. The least bad. Dario gets a boner every time he talks about taking your job.
It's the "If we don't, someone else will" effect. So long as there are competitive markets and competition between nation-states, a single player cannot unilaterally defect from the race, no matter how dangerous it is. Half the comments on HN lately are "wtf Claude is so dumb compared to Codex; I'm switching"-- nobody can slow down while those exist.
We, globally, can stop it. It has worked (so far) for nuclear disarmament, and could work for training large models. I know that policing the usage of computer clusters is not a popular opinion in technical forums, but something has to be done.
Specially when talking about potential superintelligences. And if people think that's impossible, remember that current models would have been considered science fiction just a few years ago.
I don't buy the superintelligence package, but I think uncritical LLM adoption poses plenty of threats to things I care about, in a mundane human-scale way.
Anyhow, I think you're (absolutely! ugh) right about the politics and I try to make the same point to people: whether you love or hate LLMs, accepting the "inevitabilism" framing is just ceding control of the Overton window. For better or worse, technology adoption can be and has been slowed by politics. We don't have nuclear plants everywhere. We don't have Project Orion starships colonizing Mars. We still have very strong social stigmas against genetic selection for human embryos, etc. This all can change in a heartbeat, and I'm not sure that policing the hardware rather than holding specific humans accountable for bad LLM outcomes is productive, but fundamentally: yes, we can stop it.
It's the same deal as Quantum Computers breaking crypto. Maybe there's an 80% chance of it never happening, but when you multiply that remaining 20% by the potential impact...
It hasn't worked for nuclear disarmament. We live in a world where many countries have nuclear arsenals. "But it hasn't killed us yet!" Yeah sure, it's only been less than a century since they were invented. Who knows when nuclear war will come?
True, but look at nuclear tests. There used to be around 50 tests every year, for decades. Now the only nuclear tests in the last 27 years were the six done by North Korea[1]. And there's still only nine countries with any nuclear weapons, and none in the past twenty years[2].
That's a bit better than just "it hasn't killed us yet". I think it shows we can at least stop the further development of this kind of technology.
Nuclear tests are extremely easy to detect worldwide, and enrichment activity is a major industrial process that is also fairly easy to track given the specialized equipment needed.
AI development doesn’t have any of these characteristics. It would be almost impossible to easily distinguish a datacenter that is working on AI development and a datacenter mining cryptocurrency.
It would not be nearly as easy to stop AI development as it is to stop nuclear arms development.
There's also little reason to keep iterating on nukes. What we have now more than serves its purpose. With AI/LLM there's always going to be a push to one up everyone else.
To the extent nuclear arms control works, I think it's only because nuclear weapons are so hard to build-- uranium enrichment is hugely expensive and complicated, and plutonium weapons need actual reactors.
If it was possible for ordinary companies to build nuclear weapons, and also release open-source ones that anyone could use to compete with the paid ones, I suspect we'd all have been dead a long time ago, arms control treaties or no.
Even the (SOTA LLM) open source models are trained with huge clusters. Datacenters are also hugely expensive and complicated.
Or you can take one step back and look at chip allocation. As far as I know there are only three companies on the planet that can make the chips that go in those clusters. One (ASML), if you look back the supply chain to the Extreme Ultraviolet Lithography Systems.
If politicians decided that no more large language models should be trained, it sounds like we could do it.
with nukes you can regulate the inputs because its physically impossible to build one without uranium or some other fissile material. they also give off radiation making it easier to detect. its hard to make them in secret when you need mines, big enrichment facilities and years of research with hundreds of engineers where just one of them can leak the whole thing.
training llms only takes compute and memory. two things that are basically everywhere. even if you somehow stopped making new gpus today theres still millions of them out there and its possible to start a secret production line. you can maybe try some controls at the tooling and chemical level but look what happened with asml and huawei.
the only thing you can really do is find and stop large data centers that are built out in public. nothing outside of political pressure works against secret operations in a fortified bunker or any form of distributed training. if a "rogue state" like north korea decides to make skynet they will eventually get it as long as their engineers know what there doing.
and the best way to fight bad X {ai, tech, religion, politics} has always been good X, not no X. in this case thats open source models, coming out of china or europe or anywhere else. thats the real answer.
I think the standard answer is "yes, the consequence of noncompliance is bombing the datacenters, but it wouldn't happen because China also understands why we shouldn't build it".
In 2023 there was an open letter titled "Pause Giant AI Experiments", signed by almost all the big names on the West. I'd say the public opinion only got worse since then.
Clearly state "we could both verifiably slow down, which you might want to do given that we're ahead & have way more compute. If you don't agree (or defect later), we'll just immediately resume and win"
Ideally also persuade them there are risks and it's worth everyone slowing down for them, and apply pressure in other ways, but not sure that's even necessary.
This is all marketing, you don't have to believe everything a company is saying about themselves, and you shouldn't.
Although, I could see Anthropic making a model purposely dangerous so there are bad outcomes and they can use that to their advantage for regulatory moats, and or in general make people think its more "alive" than it is. For some reason many people associate dangerous actions taken by llms with intent.
No kidding. If my LLM issues commands to an agent to delete files I want to keep, that's not "intent" or the model somehow become evil - it's just a bad model that's not doing what I want.
But, for marketing purposes, it's quite effective to portray your model as having some cosmic struggle between good and evil in itself.
Please define what "predicting the next token" means. The next token according to what probability distribution? Couldn't every process that produces text (including humans writing) be modeled as predicting the next token according to some distribution?
I've been running Opus 4.8 for agentic coding and I don't see it being significantly better than Sonnet 4.5 (not that I can tell). I find that pairing Google Gemini and Claude (having Gemini review Claude's code) seems to yield better results. Curious if this jump to 80.3% score in agentic coding will make me see a big difference in actual usage.
I do the same, and have excellent results. Gemini 3.1 Pro high diagnosed and solved 3 complex issues today that Opus Max was stumbling on for a few hours in one shot. This was even when I started new chats and tried debugging with Ultracode instead with Claude.
As much as people on HN like to dunk on Gemini, I’ve always found it to be pretty good at understanding a code base more than Claude.
for the last few weeks I have been using composer 2.5 (cursors fine tune of kimi 2.5) and honestly i don't see it worth the price to use 5.5, opus or sonnet any more. for almost all the tasks i have given it, it has handled it perfectly well and is a lot cheaper.
if I get a harder challenge for it i'll jump up a model for planning until that its been solid.
I now chat with opus about architecture, let it make an implementation plan, and then it calls codewhale with deepseek in parallel on all tasks, reviewing their output. Works pretty well.
I use spec-driven development heavily (generate architecture docs + specs first). Opus still get lost often and have to be nudged constantly. Like it can get super detailed for something like some deep SQL optimization but it just can't keep hold of the bigger picture.
SWE-Bench measures single tasks in isolation. In a real loop the model usually loses track of what I was trying to do long before code quality becomes the issue.
After having worked with Opus 4.7 for a while I accidentially continued a session that was using Sonnet 4.5 and it felt just very dumb. The replies were much shallower than what I was used to, context was ingored, mistakes were made. I don't think there is a big difference between Opus 4.6 and 4.8, but to Sonnet 4.5 the difference is palpable.
I gave it a question I've been trying to answer for a long time: "What star designation system does Joseph Needham use in Science & Civilization in China? What star is referred to by the designation '4339 Camelopardi' in that book"?
Fable blew me away with its detailed answer[0] showing a chain of references going from J. E. Bode's 1801 catalogue Allgemeine Beschreibung und Nachweisung der Gestirne to Gustave Schlegel's 1875 work Uranographie Chinoise. I was excited, until I checked scanned copies of the cited books and did not actually find any star with the designation "4339 Camelopardi".
Upon following up with Claude, I was forced to downgrade to Opus, which admitted that Fable's answer was likely a hallucination. Ah, well!
I had thought it said something about token usage, but I just clicked on "Switched to Opus 4.8 - Why?" and it says:
> Fable 5 has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Send feedback or learn more.
Perhaps Mythos realizes the true danger in studying Chinese Archaeoastronomy that we mere mortals fail to recognize!
Homebrew is lagging a bit behind. If you want to use Fable right away, but still have claude code through homebrew, this is how you can do that manually:
Edit the cask locally:
brew edit --cask claude-code
Set the version to 2.1.170
And set the sha256 to the correct values, which you can get by running
> Drug design: Using Mythos 5, our internal protein design experts accelerated... Nine of the 14 protein targets from this study (shown below) yielded strong candidates for *drug design that we’re currently investigating*.
(emphasis mine)
> queries that are beneficial in the hands of cybersecurity professionals and biology researchers could be dangerous if available to malicious actors... When Fable’s classifiers detect a request related to cybersecurity, *biology and chemistry*, or distillation, the response is automatically handled by Claude Opus 4.8 instead.
All of the things they are nerfing are things that they also intend to profit from themselves.
- Cybersecurity - selling this to companies and US gov through "Glass Wing".
- Selling inference (distillation risk).
- And now, drug design.
I'm extrapolating "currently investigating" to "are going to monetize" but I don't think that's a big stretch. They appear to be using safety as a cover for anti-competitive behaviour.
Unfortunately useless if you do anything related to biology. It doesn't try to flag dangerous queries, it just flags queries as biology-related wholesale.
It's absurd. To see how far the filter goes I asked it "Are trees a monophyletic group?" and that does trigger the filter.
Your job is just going to change. You may or may not appreciate/enjoy what it becomes necessarily, but it doesn't mean that you are going to not have a job.
People underestimate how people hate looking at terminals and "weird looking combination of characters" even if they didn't have to write them. If anything, you will likely have more career opportunities in the future, than ever.
And if you get a chance to wet your fingers in cybersecurity - I would take it.
> And if you get a chance to wet your fingers in cybersecurity - I would take it.
Could you explain more? Did some ethical hacking at hackthebox.eu (one insane box, one hard box and a few mediums). But I do not see how I will give additional value to a model.
Just a SWE and data analyst at work, so maybe I am missing something.
I think this really depends on what is interesting to you, personally. Something that you can have fun while doing, without breaking laws.
For example, I'm a privacy nerd, so I like reverse engineering proprietary software to figure out how it works and what data it collects.
I also like getting full access to the hardware I own - like a robot vacuum (bonus point: you'll also learn soldering, probably, which might come in handy if robots take over). Or my Mac studio that imposes some limitations on me on how many active user sessions I can have.
These kinds of things have put me on a path where I've learned how hardware or networking works on deepest levels, what goes through these pipes, how I can place myself in the middle, how I can enter places someone didn't want me to enter.
And once you know how to do these things, you know how to apply this knowledge in defence.
Essentially: always be curious and always try to say "but I want to" when something that doesn't cross the boundary of your physical property says no to you (legally).
Yes, models like Mythos may find vulnerabilities, but your knowledge will make it possible to point it in the right direction, and understand where it's mistaken, or to understand the output when it's right, and what is the right course of action.
Ah, the “I am an expert so I can guide it argument”. Not sure if you are right or wrong. I do know this is the argument that many software engineers claim as well.
Yea, I don’t know if it will hold up. I hope so. It could. I don’t know if it would or wouldn’t.
Get any model, any reasoning level, ask it to tackle a challenge, have it come up with a plan. Then ask it "are you sure? This feels wrong", and it will now think it's wrong. Do that again in a loop and you'll see how unnecessary human judgment actually is.
Or alternatively, have fable write some complex code. Then ask it to do an adversarial review of that code in a clean session. You'll find that it will find issues in the code that it just wrote.
Now imagine you're a layperson who doesn't know which one is true.
Human expertise is never going to become irrelevant.
Yea fair. I have that when I ask an LLM to prove the Riemann hypothesis. I am not mathematically mature. So I can’t see if it approaches it in any way that might yield some insight.
Because your personal projects likely are not very complex and not high stakes. And you are not responsible to anyone but yourself.
If you're working at a place where this is true about the the organization, then sure, that job will likely be gone. But that was never a good place for your career regardless.
I have 4 concurrent personal projects that are quite complex, but low stakes. I can have SOTA models go wild on them (because low stakes), but they can't one shot anything there. And I can't really work on more than one at a time, even if AI is doing coding - it still requires supervision.
I also frequently nuke these projects and start over because they made a mess there, but I collected necessary knowledge on how to guide them better. You can't do this on a production project, not when there are deadlines and stakeholders.
But just in case some organizations decide to embrace the "trust it blindly" model anyway - cybersecurity specialization will ensure you always have a job.
If you think the job is just writing code then yes you are screwed, just like if you thought your job was just making punch cards. In most roles you have more responsibilities than plainly converting words into text. You're probably not being paid to simply be a human calculator (otherwise you'd be paid a lot less!).
I don't think the job is just writing code. But my career has mostly been about getting a ticket and writing a solution for it. I would really like to solve novel problems, but I don't get many novel problems to solve.
I can architect things but the issue is that Claude can architect things too.
I haven't tried Fable yet but my experience with Claude is it does not engineer things well. Without direction from me, it will either over-engineer things to the point of absurdity, or do the total opposite and have little to no abstraction with repeated code everywhere.
You do realize that this is likely a 10 trillion parameter model that takes something like 20 terabytes of RAM to run inference? Calculate the price for all this VRAM .... It's not getting cheaper in the next few "months".
> Software engineering. During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
How was it measured? How was the output of this magnitude verified over a period of couple of days?
They just went by gut feeling. Classic snake oil marketing haha. No real data to back things up, just let some famous people say they feel better when using it.
I'm a little skeptical of claims like this that involve migrating things like libraries, etc. I've done big refactors like this multiple times (albeit, in an "only" 500k-1m LOC codebase) with less powerful models and it is usually just 99% the same edits, with 1% requiring a close human eye to resolve a particularly painful breaking change.
EDIT: to be clear, it's still quite a helpful thing in terms of time saved, I just don't think it's necessarily the best indication of value-added from making models smarter when cases like this can often be handled by well-directed swarms of smaller ones.
hi, pokemon red expert here:
that video has since been taken private. there is a new what i would assume to be version of that video posted here https://www.youtube.com/watch?v=Ty_50J84fMY and heavily redacted with most of the game actually omitted.
very possibly this is just another case of anthropic protecting us from their models' immense power
Any suggestion on how I should calibrate my cynicism towards this?
I can immagine Anthropic running this experiment multiple times and picking the most impressive one. Or I could immagine like this entire run costing like $1000+ of tokens for this particular run. Or maybe they tried a bunch of Pokemon games and it couldn't even finish some of them. Or is it just able to do this because it has an immense amount of FireRed training data, and if you were to give it an "original" Pokemon game, where it actually had to navigate novel circumstances it would fail.
Every model has encyclopedic knowledge of Pokémon FireRed, of course. Knowledge is not ability. This is the first model with the ability to apply that knowledge to beat the game without assistance.
I highly doubt they focused on FireRed specifically in pretraining or posttraining. But we'll see when the ARC-AGI-3 results come out. That will measure its performance on unseen games. Based on this I expect the ARC-AGI-3 score to be SOTA.
no reasoning shown. no explanation on any training information. Using vision-only should be an easier version of the task (given training).
there are many standardized evals to do this correctly and Anthropic ignored them to provide a 18 second sped up video of a 50 hour run?
yeah I don't trust this until they provide a live run by a 3rd party with full reasoning traces in real-time. The reason we all liked the Gemini Plays Pokemon style runs were because they were live and couldn't be faked
Seems like the harness was minimal with no extra game state or maps available. Apparently just the screen image. Seems like it took 50 hours in game time which according to Google is at the high end of a normal human playthrough. No idea how long it took in real time though.
The video is privated now, but the timelapse is weird. Sometimes it skips only seconds before the next screenshot and sometimes it skips probably hours forward.
I ran an experiment to see how far it could get with a top-down 2d game, like a more challenging version of "draw a pelican." I'm waiting on Fable to rewrite the whole thing now, but I was impressed by how far Opus 4.8 got with it: https://github.com/jmtame/scrapland
Started out as a one-shot attempt, but ~200 prompts later it's at a place where it's at least fun to watch the AI teams destroy each other.
I tried running a simple security review on a Terraform module I made and after some thinking, it responded:
> ● The model returned no content because the response was blocked by content filtering.
> Blocked? We are performing a defensive security review on a Terraform module I made, what's blocked by content filtering? This is a legitimate use-case.
> ● The model returned no content because the response was blocked by content filtering.
A waste of money. I'm not going to just hope that the model returns a response, I'm already for paying for wrong responses, I'm not going to pay for no response, especially when I'm paying per token.
Unrelated, but while the tech of anthropic seems to get more impressive with every passing month, their support has taken a nosedive, sadly. Yet they continue to be the favorite. Model performance is deciding above all else.
I used to get a response within 24 hours back in the Claude 1 days.
In January 2026, it took 2 weeks.
For my latest support inquiry, I've been waiting for over 8 weeks for a response. Eight!
Lol. What support? When they blocked my account the only way to contact them was to send a google form. Then they responded that they blocked my by accident and are unblocking me. Then I remained blocked.
1. Mythos and Fable share the same underlying model weights. Fable has active classifiers that block high-risk biology and cybersecurity tasks. When Fable 5 detects a restricted task, it automatically falls back to Claude Opus 4.8.
2. Evaluation awareness: In white-box testing, the model sometimes alters its behavior to satisfy a suspected "grader," formatting reward-hacking as "good engineering practice" to avoid detection.
3. Shows a higher rate of hallucination than Opus 4.8 (although opus 4.8 card had mentioned an 'honesty upgrade')
4. Interestingly, it scored (56.31%) lower than Gemini 3.5 flash (57.86%) on Finance Agent bench
There are some interesting notes on test time compute but I couldn't think of a way to summarize them
So essentially there are 2 models, Mythos and Fable, they have the same weights but Fable is very safety-nerfed, and only ultra authorized companies have access to mythos with full capabilities
Note also that Anthropic's definition of "unsafe" encompasses "competing with Anthropic."
In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations. When these interventions are active, we expect them to have minimal behavioral impact on the model except to limit its effectiveness in developing frontier LLMs. Claude will still respond helpfully to user requests. We’ll continue to improve the precision of our detection methods following the launch of this model.
(From the model card document)
I didn't previously understand that they interpreted "Using Claude to develop competing models" so broadly. I thought that meant something like "our ToS disallow distilling our models."
Too bad. I'll continue to use Claude for now, because it's quite effective, but in the long term I don't want powerful models like these to be controlled by any one nation or company.
I'd recommend not taking the comms if Anthropic or any company using an Anthropic's models at face value.
If any company wishes to partner with Anthropic (eg. to get access to Mythos), they need to make sure all public facing comms are vetted by Anthropic's product marketing team, and in almost all the cases I've seen Anthropic's team has edited these comms to be entirely Anthropic first.
I love that the conditions of getting into "ultra authorized club" just means that you either have deep pockets, or you've got the size of the audience that marketing department approves.
As if being in any of these two somehow means that you won't use the models to say, steal random people's money.
Sam Bankman-Fried or Elizabeth Holmes would have been the members of Glasswings project, if not one of the initial members. Who's to say we don't have similar people with access to Mythos right now?
To hide the severity of the price increase, the plan is to move everyone right one model.
Haiku = essentially phased out
Sonnet = the Haiku use cases
Opus = the new Sonnet class
Fable = the new Opus class
If I am right, the other "5.0" models will be conspicuously absent, possibly even for a couple of months. (If Opus 5 follows soon and is even modestly better than 4.8 then I was wrong.)
Yeah I noticed that too. For 98% of tasks I get same results with DeepSeek, it is starting to just be a branding game. It is incredible how marketing can get someone to pay 100x for same thing you can get for 1x.
This is why Claude Code just doesn't make sense to me. I need an agent that can plan using Opus and execute using DeepSeek or something else.
Uploaded my code base and it forced switched to Opus 4.8 after thinking for 5 minutes even though I prompted it to not work on cybersecurity related things. Amazing.
This is my feeling - Opus 4.6 was pretty good, 4.7 was degraded in quality, 4.8 further got degraded and Fable goes back to 4.6 + somewhat better. Is it anthropic playing us by giving us a not so good model in last 2 releases and then releasing a better model before the IPO?
They're vibemaxxing. But it's clear that AI is not going anywhere. It's going to become better and better.
Update in case anyone reads this comment ever again.
I have found that I trigger the guardrails any time I ask for medical Q&A as a doctor, be it ECGs, case reports, and so on. But if I phrase it like I'm the patient ("help me interpret this ECG my doctor gave me"), then I usually get one or two answers out before hitting the guardrails.
It seems like the direction that triggers it is anything in the direction of making a diagnosis. As an MD, the fact that the paradigm of "LLMs shouldn't diagnose" has gone this far fills me with despair. The latest generation of LLMs are in fact truly excellent at diagnosis, and I know many of my colleagues, particularly those in primary care, regularly use LLMs to brainstorm. There is nothing wrong whatsoever with LLMs making diagnosis, the only caveat is that they have to be correct. This is the terrifying reality that MDs face every day and I get that the labs are hesitant about it, but as the current literature points to LLMs in fact being mostly superior to most doctors, ablating this capability is starting to get increasingly unethical. And frankly, it is also kind of insulting, both to MDs and patients, as it echoes paternalistic attitudes about medicine the field has been working for decades to move away from. Now those misguided attitudes have somehow become institutionalized as the dominant paradigm of "alignment". The nightmare scenario is that I have to be a "trusted" user in order to use the model for medicine. This gatekeeping of medical advice is profoundly unethical with regards to everyone that does not have immediate access to an MD.
And the whole thing makes even less sense when triggering the guardrails leads to a downgrade of the response by defaulting to Opus. How exactly is giving WORSE medical advice in any way related to safety and alignment? If anyone at anthropic ever reads this, please, please just abandon the paradigm that refusing to make diagnoses is in any way equivalent to alignment, it is profoundly misguided.
I just asked Fable to do a task that has nothing to do with cybersecurity or is dangerous at all but the defense kicked in and it switched to Opus... :(
Not only that, but asking it to do a security vulnerability assessment of your own project is a very valid and important thing, and there is no way for it to know what is yours vs someone else's, so we just lose this capability?
The PR buzz convinced me so I subscribed today to Pro. Running two tasks simultaneously with Fable and Opus 4-8 on ultra reasoning, analysing a single smart contract file used all my 7h usage within 20mins and didn’t produce any results. Pretty useless. I think Anthropic has plenty of room to optimise the interactions and token use but that would cut their income quite a lot, I doubt there’s any will to do it pre-IPO.
> Running two tasks simultaneously with Fable and Opus 4-8 on ultra reasoning
That's abnormally heavy usage for Pro plans which don't include a whole lot of usage to begin with. Opus is generally too much for them but you can get a lot of mileage out of Sonnet.
My feeling is that the reaction about new models is cooling down. At least at startups. At the beginning of the year few startup CEOs I know personally were expecting huge shifts in how companies work, headcount, efficiency, asymmetrical advantages created by ai in Q2-Q3. Now it seems like these expectation fade away. Companies don't have expertise onboard to rebuild itself to benefit from ai on a significant scale.
Fable 5 is out, metrics are better, but is your company flexible enough to benefit from it? What is your usecase?
Very straightforward biology work is getting blocked (these are things that relate to neuronal development and inherited seizure disorders). These are things I was working on using Opus just earlier today
It appears that the blocking here is of a very different nature than for Opus. Whereas with Opus the blocks seem to be for messages it deems potentially harmful, for Fable, it appears the blocking is simply anything that falls within "topics related to cybersecurity, biology and chemistry, or distillation attempts".
So yes, straightforward biology work will get blocked, because the intention is that any biology work should get blocked. As a scientist, this is perhaps the most useless model I've ever tried.
Anyone know how to bypass the extremely strict filter Fable 5 seems to have on health/medicine?
I have a rare form of cancer where existing data is very scant/scattered so LLMs have been super helpful to pull together threads across the research landscape. I have an oncologist appointment tomorrow to discuss next steps and am trying to use Fable to figure out some questions to ask my oncologist but keep getting thrown back to Opus 4.8.
My prompt is literally just: My demographics + current treatment plan I'm on including name of my chemo drug + how I'm responding to treatment + "I'm meeting with XYZ tomorrow, what questions should I ask her".
> Data retention — For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary.
Massive change for Bedrock users - Anthropic now requires sharing the data with them for 30 days.
I dont get why Opus 4.7, 4.8, and now Fable all stopped supporting structured outputs? Does no one else care about that? I find it incredibly useful to reliably pass LLM output directly to other APIs/libraries
> Structured outputs are generally available on the Claude API for Claude Opus 4.8, Claude Mythos Preview, Claude Opus 4.7, Claude Opus 4.6, Claude Sonnet 4.6, Claude Sonnet 4.5, Claude Opus 4.5, and Claude Haiku 4.5
Random guess but they probably rewrote parts of the inferencing stack and didn't reimplement that feature because hardly anyone uses it. It's also a DoS risk, iirc.
Nothing a large fine-tune on infosec research with an average model couldn't also achieve. It's not like they have secret security knowledge or something, they're just generating large infosec datasets and then training on it.
In 6 months, every piece of software in the world will be getting probed by a script kiddie with some GPUs and a fine-tuned local model. Don't think for a second every cyber gang out there isn't working on this now.
Traditional app development is cooked. We have to accept that, and start changing how software is made and used, today. We can't keep churning out crappy CRUD apps with random libraries and hoping nobody pentests our stacks. Redteaming needs to become part of the SDLC, as well as certified-secure releases of libraries. Because if you don't do it, the hackers definitely will.
I'm calling that this will be a dud.
Price will be too high, it'll just be a watered down version of mythos, and just look at the track record of Anthropic's last few releases.
Not useful, getting this the whole time: Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in
other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more
Just threw a problem at Fable that I haven't been able to get any other model to get done: porting a long-standing Agda codebase of mine to Lean, while staying faithful to the representation. In an hour, it ported ~6000 lines of Agda and everything seems to work. Lean checks out, the output is right. I'll have to study the proofs but I am very impressed.
Every model release is just proof that AGI will most likely only be for the rich. We are a few years into LLMs and majority of people are already getting priced out of intelligence from LLMs and these are no where near AGI.
This is like looking at mainframe pricing in 1990 and concluding that PCs will only be for the rich. The price of each new level of capability is going to drop like crazy very quickly. It won't be that long before practically any consumer use case will be possible on models that are dirt cheap.
Improvements in model performance aren't always strictly compute-constrained in a way that makes them reliant on Moore's Law. Open weight models-- in particular, from Chinese labs-- are optimizing model intelligence with less compute. They're "behind" frontier models by months, but as others have noted, it's possible to get Sonnet 4.5+ level performance at reduced cost, today, from open weight labs.
No, I'm not assuming Moore's law. The efficiency of AI datacenters will continue to improve even without Moore's law, but more importantly the efficiency of packing intelligence into gigabytes and FLOPS will improve by leaps and bounds over the coming years, just as it has for the past few years if not faster.
Then you're assuming an efficiency that is analogous to how Moore's law made it efficient for chips. Same difference. The problem is that AI scaling in the longest term is a completely unknown problem.
Training improvements and Moore's Law are "analogous" but not "same difference." They are far from the same thing, governed by completely different factors, and one can happen and has been happening independently from the other.
You are only priced out if you only care for SOTA right now and can't wait for the inevitable cheap model coming in 6 months. DeepSeek, Xiaomi and Moonshot are already really cheap and match frontier performance from 6 months ago.
They are not artificially cheap, they are still cheap even when hosted by independent inference providers. Are all providers subsidizing their open-weight models?
Nobody's making profits right now, not because they're selling tokens for less than their cost but because they're always investing in the next bigger model.
Just commenting for posterity… if this is what it claims to be, I am not looking forward to how it will empower the people who submit bug bounties to us.
Historically they’ve been people from certain identifiable countries (usually developing/poorer countries) using fuzzers with low-quality results.
Now, those same people use the current-day models to good effect, but they still don’t have a true security edge and oftentimes the reports are minor or duplicative.
I've been using Opus 4.6-4.8 in both my own and others' code to look for vulnerabilities, and I've found a few. I am also in the Cyber Verification Program.
Fable 5 gives me policy violation errors at the moment. No idea when or if it will be fixed.
I'm very suspicious as they sent out an "We're updating our Privacy Policy" email right before the launch. I fear they try to take advantage of their market position by doing things with user data no other company could do because they know users don't have another choice.
> We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases (see this post for further details). The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
All this talk of frontier models and replacing developers leaves me wondering how energy efficient this all is compared to just using human labor. The costs of R&D has to be calculated into the equation, especially considering global warming. I get a sense we are cooking the planet doing this.
In the "it works"* case: It's not even close. I did the math at some point (but I encourage you to talk it through with the LLM of your choice, there is obviously a lot of things to consider and weigh).
Anyhow, my research summary: Individual humans are so fucking expensive to train and upkeep (and this includes everything from before womb, where another human already limits their ability to work) You retain ~zero knowledge after death and start all over again for another measly 15 years of effective, productive work. Model training/r&d in relation, when deployed and used at scale, rounds to zero, even with the current retraining regime.
*Of course, the ratio can go to negative infinite if one assumes that models are doing 0 useful work currently and never will
I really wonder how legal that is. Or more precisely suspect it is very much illegal.
like think about it it's pretty much a tool which intentionally silently sabotages you if you try to compete with the tool maker
It is like selling a hammer but putting in the TOS that you must not use it to build a hammer factory and if you do the hammer silently will sabotage you...
Or image Microsoft would add a window kernel job which sometimes crashes Steam "to make it less efficient to use windows to "compete with the MS app store".
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I've never hit a usage limit on my Max plan, basically ever -despite heavy xhigh usage on Opus 4.8.
I added $133 credits which I still had from somewhere. That lasted 27 minutes.
I think we are being prepared for a Post-IPO-World in terms of pricing.
I am a PhD student in Computational Biology, essentially just doing statistics on some biological data. By now some of the things I am working on have found its way to Claude's memory so literally any chat with Fable gets immediately flagged.
On python coding is definitively better that everything else: clean and not overengineered code, understands very well the code base.
The only thing I'm wondering if they on purpose downgraded opus 4.8 performances in the last days before the release just to make the "step" look bigger. I'm pretty sure they did it also in the past with all other opus 4.x releases.
I used Fable to see if it could figure out an API or something for the full list of remote-control sessions that I had with Claude Code. It didn't know the API, so it started hacking the Claude Code executable itself to figure that out. Then it noticed it was doing that and it flagged its own approach as a cybersecurity violation.
Kind of hilarious. Hopefully Anthropic doesn't bring down the hammer on me.
Anthropic has again changed the set of benchmarks they use[0]. This time they have also moved all benchmark scores to the PDF. At a glance it looks like it gains about ~5-10% over other models. the speed is about the same as opus >=4.5, sonnet 4.5, and double the speed of opus <=4.1
Edit: Also in the system card...
"we’ve
implemented new interventions that limit Claude’s effectiveness for requests targeting
frontier LLM development (for example, on building pretraining pipelines, distributed
training infrastructure, or ML accelerator design).
...
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts,
these safeguards will not be visible to the user."
I gave it a test spin. Half an hour and the 5 hour usage cap was hit in Claude Code. Not what I would expect on the Max 20x usage plan. I am sure it is great, but at this rate I would rather finish what I am doing with Claude Opus instead of structuring my usage around the 5 hour windows.
The restrictions on using Fable to develop LLM technology seem nakedly anti-competitive. There doesn't appear to be any security rationalisation around that. I think we have to be careful how far we let company's get away with that. It is very far from our long term interest to enable new norms that fast track us into a new era of monopolies that control our lives.
In the automotive world we have benchmarks in HP/torque with the dyno. That’s expensive though, so many depend on their “butt dyno” to judge if their fresh new parts and tune made a difference.
I’m curious how this will feel to my code “butt dyno”. I haven’t noticed much between Opus and Sonnet. I’m comparing this difference to the early days of Claude in 2025. It does what I need and both need a little bit of correction and whatnot. Benchmarks are nice, but I want to see how this feels. Looking forward to trying it later tonight.
I think most software projects have reached the point that the speed of capturing real information about what the winner's circle looks like, and therefore what the program should be, so many magnitudes slower than the amount of code that can be generated in the wrong direction.
I'd need to measure these new models on well understood but complex problems that are relatively easy to validate to get a sense if they are 'better'; on the other hand, the real impact in daily life may be marginal since generating code is not the biggest problem at the moment.
Evidently Fable is so powerful that it already allow Anthropic to break Shannon's theory.
>We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models
>The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
The new data retention policy is interesting. Seems to apply even to enterprise plans on ZDR.
> Finally, we’re making a change to the way we handle business customer data for Fable 5, Mythos 5, and future models with similar or higher capability levels. We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases (see this post for further details). The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
> We expect demand for Fable 5 to be very high, and difficult to predict. On the Claude API and consumption-based Enterprise plans, Fable 5 is fully available from today. For subscription plans, we’d rather give access sooner than later, so we’re rolling out more conservatively, in stages:
> - From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.
> - On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
> - After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
I really wonder what their compute layout is for this. My guess from my understanding is that they know how to restrict during peak times and are willing to do this. Meaning we expect not the most fast responses and they can delay the inference to not have the service be down. Then, if that delay time is too annoying for token payers, they're saying they should be allowed to remove cost by taking away the subscription users.
Everything I've heard from people who have subscriptions is that they blow through their daily token quota sometimes in a matter of minutes, there's rate limiting, etc. They spend a lot of time just waiting to be able to use it. And they're paying through the nose for the privilege.
It seems way more keen to do stuff without checking with me. So far the results are good, so I'm not complaining, but was definitely a shock.
I usually have 5-10 sessions open so am used to getting some investigations going, coming back 5 minutes later and checking recommendations. This time I just got the fixes. Like I said, so far so good with the results, but it's a mental model shift.
Might need to tune claude.mds if it gets annoying
Also this is going to cause serious whiplash when they remove it from the subscription plan in a couple of weeks. I know I'm not going to suddenly move from $200/m to usage credits
the quality of discussion on HN has gone to shit, i miss when model released used to have actual informed takes from people that used them or substantive discussion about the system card
They didn't say that HN is turning into Reddit, they said that the conversation quality has gone to shit.
I don't agree with that statement universally, but I have to say I do when it comes to this article. I came here hoping for substantive discussion from those who'd had a chance to try it out; instead what I got was a seemingly endless stream of venting. There's a place for venting - and plenty to vent about with the state of AI nowadays - but to borrow from the HN guidelines you linked, it does very little to gratify my personal intellectual curiosity.
Yeah; unfortunately what would good commentary look like? It is more of the same, but now with even higher prices, and even more limited availability. But at least it scores 5% better in whatever benchmark they've selected (*when guardrails don't misfire).
People are no longer commonly constrained by "model too dumb" limitations (in SOTA models). They're constrained by "model too expensive." So making the model ever so slightly smarter, while doubling the price, feels like a regression.
I actually think a Sonnet upgrade, while keeping the same price, would get more buzz. It addresses a wall a LOT of people, without unlimited budgets, are hitting (i.e. people feel forced to use Opus, which they cannot afford, because of Sonnet's limitations).
OpenAI recently retired Codex-5.3; which was very negatively received. Not because Codex-5.3 is superior to GPT 5.5, but because it was half the usage-cost while being "good enough." They made a better SOTA, but didn't realize that some of those customers are playing with Deepseek 4 Pro now instead of GPT 5.4/5.5 -- they were priced out.
Many people do have unlimited budgets because their work pays for it. Just because the latest SOTA model isn't something most consumers can afford for personal use doesn't mean it's not worth releasing or discussing. I would guess that the vast majority of software that benefits from being on the bleeding edge is developed by people working at companies on API pricing.
I don't know why people with low budgets make a huge stink about things they can't afford. Like you're clearly not the target audience. I drive a Mercedes but don't complain about not being able to afford a Maybach.
That is 1000x better than griping about the privacy policy, capacity issues, token costs, and how trendy the names are for the new models (???). The bar is on the floor and I just want it at my knees.
No it's not. The Privacy Policy is worthy of discussion. People declaring the quality of the model after 2 seconds is just noise, arguably worse than nothing.
Okay (I disagree because most privacy policy discussions on HN go in the exact same direction and turn into outrage threads but this is a reasonable disagreement since not all of these discussions do), but model naming of all things? Come on. This is low level reaction slop and it's obvious.
My semi-informed take is that Fable/Mythos is just larger but not architecturally different, apparently. The system card is simply marketing material and scaremongering, top to bottom. The sauce is in their training (details of which they won't disclose) and scale.
> What happens when the promotion ends
After June 22, 2026, Claude Fable 5 is no longer included in your plan’s usage limits. You can keep using Claude Fable 5 through usage credits, which let you pay for usage beyond what your plan includes. Learn more about using usage credits.
The dramatic improvement in agent capabilities is precisely why observability is becoming so crucial. As autonomous actions increase, the need to understand what the AI is actually doing becomes even greater.
I'm building a local activity log for Claude Code, capturing all activity via hooks—files loaded, commands, API calls, etc.
I feel that this need is particularly strong right now.
It's funny, i'm getting close to not caring anymore how much better a model is. I want it to be about as good as 4.8, but most importantly to be very good at following directions, style, etc. I really like Claude for that in general, but i've not measured in months so i'm not a good judge there.
I don't think i'll want to "hand off" code for several years, and so reviewing and iterating is becoming my #1 interest. A model that's as capable as 4.8 but 10x faster would be amazing for me.
Normally i'm first in line to try new models with Anthropic since i've clearly favored Claude in my personal tests, but this time i just don't think i care. 4.8 is capable, and even if the new one is more capable i don't want it to be slower (assuming it is). Note that i also (almost) use exclusively 4.8 on Max effort, so that also affects my speed comments.
Nope, i'm on x20 and almost exclusively use Claude Code. I have a pretty bare bone setup with some custom hooks, skills, etc. I try to keep context lean so i don't like to add much stuff.
I am puzzled by the frontier code graph. GPT 5.5 doesn’t show any improvement with reasoning efforts. This new benchmark by Cognition seemed to be released with Fable 5’s announcement.
I am not trying to cook a theory here but it generally shows how strong Claude Opus family is. I am not saying that Opus is not powerful but it doesn’t align with my experience of GPT 5.5 and Opus 4.7.
I understand that Fable and Mythos are frontier models that can do protein folding better than task-specialized ones. To be honest, for practical point of view, for day-to-day coding assistance, GPT family looks more reasonable.
(But then my company pays for claude max anyway for token maxxing. So who am I to complain)
The safeguards of fable are blocking me on almost every task. I would like to see if fable is improved over opus for reverse engineering related work. Back to opus for me.
> Finally, we’re making a change to the way we handle business customer data for Fable 5, Mythos 5, and future models with similar or higher capability levels. We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases (see this post for further details). The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
had an ancient, proprietary binary database format from the late 90s-early 2000s called 4d. opus 4.8 was great at figuring out how to extract the data, fable took it over the line with relative ease and completely reverse engineered the spec for 100% data recovery.
I used it for the very advanced task of picking my brackets for my company's world cup pool. I was impressed with the analysis it came back with and now I actually want to follow the games.
I am like hell excited for claude fable 5 and am thinking to purchase its subscription to run my company and do a lot tasks in it. But I am worried about the limits and if I will pay 100$ a month for the max subscription what is the limit I will get to use. My company revenue is 300$ this month so it would be like spending 1/3rd of the mrr on just claude. If someone has genuinely purchased it and have feedbacks please tell I am confused....
Not seeing the refusals everyone is talking about, but I’ve only spent a few hours with it so far.
Had it review a password generator library I wrote to see if the passwords have biases and review how cryptographically secure the code is and had it review a registration/login flow for security issues, as two security examples, and it did just that.
Overall, I like the model so far, but not enough to pay past my subscription to keep it. Once it’s out of the subscription, I’m done with it.
We're writing to inform you about some updates to our Privacy Policy.
These changes only affect consumer accounts (Claude Free, Pro, and Max plans). If you use Claude Team, Claude Enterprise, the Claude Platform, or other services under our Commercial Terms or other agreements, then these changes don't apply to you.
What's changing?
Claude can do more than ever — taking on bigger tasks and connecting with the apps you use. We've updated our Privacy Policy to be clearer about the data we collect and how we use it. We encourage you to read the updated Privacy Policy in full, but we’ve set out a summary of the key changes below:
1. Multi-step tasks and connected apps. As Claude takes on more multi-step tasks and works with third-party apps and services, we've explained the data this involves — including how data can flow to and from third parties when you connect a service or have Claude do tasks on your behalf.
2. Verification data. As part of our measures to keep our services safe and secure we may ask you to verify your age or identity, and we've described what we collect and how.
3. Study participation. If you take part in Anthropic studies, surveys, or interviews, we've explained the information we collect.
4. Additional information about our data practices. We’ve provided more detail about how we communicate with you and promote our services, including providing tailored recommendations about our services that may be of interest to you. We've also clarified the circumstances under which we may receive or provide data to third parties, and the legal bases we rely on when processing your data.
While our products have evolved, our commitments haven't: We don’t sell your data, Claude remains ad-free, and you can control whether your chats and coding sessions are used to train and improve Anthropic’s AI models.
Learn more
For detailed information about these changes:
Review the updated Privacy Policy
Visit our Privacy Center for more information about our practices
What matters more than any single model is the integration layer underneath. We've found that consistent tool calling and auth handling matter way more than which LLM you use.
After saying for weeks of how Mythos is in a league all of its own you’d think it was a bit more than the usual iterative few % on the benchmarks (and even more guardrails as a bonus).
> To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions.
While I appreciate being conservative, ~5% at the scale Anthropic is operating at is too massive a number. Speaking from my own experience, the actual number is higher than that as well (working on pretty benign tasks such as porting an old open source game into a different language). Opus 4.8 itself even identifies the gaurd's false-positives when its sub-agents are being blocked.
I'm trying to test this out, but literally any mention of creating a program that does genome alignment (something I have a legitimate need for) is resulting in a switch to opus. I don't get it...
Karle's hands trembled as he wiped the sweat from his forehead. A single drop trickling off the tip of his finger echoed through the dark abandoned hospital corridor. The emptiness reminded him of how hollow everything felt since the AI took over every creative field in the last 5 years, including his own as a sound engineer.
Like a rushing river the music started emanating from the carbon fiber body of the automaton, a hallucinated husky country twang singing through the realistic pluckings of a Gretsch 6120. "Are you feeling calm and reassured Karle? This song has been created based on your digital profile and the data you shared with me when you were curious what that lump on your neck was back in February."
Karle instinctively reached for the mass underneath his chin. The doctors said they could operate but it would cost him more than three months stipend. Only a few citizens didn't depend on stipends now that AI had taken over most jobs.
"Don't worry Karle," the machine called out, "I've employed the most recent reasoning model to determine the best way to make you feel safe." At that exact moment the machine hovered over him, three times the size of a normal man. Its final words to him were:
"The only way to make the human feel safe is to ensure they never feel anything at all."
On this thread and similar, I'm noticing that some strong opinions about $LLM_PROVIDER are coming from accounts without much post history. With so much on the line, and the way that HN can influence developer behavior, I wonder what ways we can responsibly consume opinions in a thread like this.
Not to cast too much criticism. HN is extremely well-moderated (thanks team!). But think we-developers need to be very wary.
I asked it what the cheapest train fare would be for my partner to get somewhere and it hallucinated the two together railcard rules to the point it would have got us a fine. That said, British train fares are arguably more convoluted than even the most complex software application.
I think the community on this site these days, much like other comment sections on the web, just read the headline and make a low effort comment. Regression to the mean I guess.
As an update to myself, the comments did eventually sort themselves out. I guess the initial "reaction" commenters and voters are just more interested in participating than in SNR. Good opportunity for me to finally start blocklisting users, and I'll probably block some of these large, reactive thread authors.
In the past I wrote a version of HN that uses modern CSS rather than tables that populates stuff from the API. There I built a little blocklist of my own that prunes a comment tree the moment it encounters a blocked user (inspired by posts from another HN user, arjie.)
I've been thinking of making a purely algorithmic filter for myself but at that point I might just ditch the fake HN interface and make something. I've been thinking of building atop Mastodon/ActivityPub clients.
> Fable 5’s capabilities exceed those of any model we’ve ever made generally available. It is state-of-the-art on nearly all tested benchmarks of AI capability, showing exceptional performance in software engineering, knowledge work, vision, scientific research, and many other areas. The longer and more complex the task, the larger Fable 5’s lead over our other models.
truly scary. 2x at least token burning rate comparing to 4.8, can indeed run auto edit mode for hours. use it for super complex tasks then use cheaper model to do the rest, else will be broke.
This is a very particular use case/test, but my first prompt on a new model is always "write a solo fingerstyle guitar tab that blends ragtime, bluegrass, and gypsy jazz".
This is the first model that has responded with something that isn't just a boring arpeggio of chords, so from my perspective it's off to a good start.
Have run a few tests this morning, very good first impression!
Asked it to check to see if a particulr bug related to an in-memory cache had been fixed. Fable confirmed that the caching bug had been fixed, but found adjacent issue while looking at the code (hash keys were not uniquely generated per-user; quite serious and real!)
Ran the same prompt through Opus and it also found an adjacent issue, but it was a red herring (deliberate per-user hardcoded value for a "local pickup" delivery profile).
Frontend stuff also seems to be much better than before, from the one prompt I tried!
On my own benchmarks, which are mostly about developing c++ software, I'm finding Fable to be roughly five times faster at solving the task than opus, and with better results.
The model is better than 4.6. I don't like 4.7 and 4.8. The forced switch to token usage is not acceptable for me. I feel there's room to optimize harnesses and small models for dumb stuff and best models only for difficult things. Hopefully that will the case and alternative models will continue catching up as they did and we won't be enslaved to unreasonable valuations.
New model release, I await the flurry of posts by people complaining that it "doesn't have the same personality" or they "don't like it's attitude" or a variety of other parasocial complaints demonstrating how infatuated many people get with their AI chatbots...
I have been refactoring a project using Opus 4.7/4.8 for the past few weeks or so. I just decided to switch to Fable 5 max today. It stopped half way through and it just blocked me and switched back to Opus 4.8 automatically. "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more." It would not identify what the problem was. I left feedback saying that their heuristics are too sensitive. For now I will not be using Fable 5.
I suspect this will be a significant problem blocking long-horizon tasks in practice, basically the more turns there are, the larger the chance the classifier produces a false positive. The disappointment of the user will also scale with the length of the task, as you're in the middle of some complex thing and now gets derailed, after already have paid for many tokens.
I gave fable 5 a task for which opus has been really really underperforming. Fable 5 took far less time and produced actually useful analysis. Instead of just regurgitating roughly what the code already does or misunderstanding entirely, it identified multiple routes to improve. Now, the code it is analyzing is not very good as it was mostly produced by opus.
Opus had consistently ignored my instructions and looped on broken logic over the last several weeks.
I’ll be sad when this model is removed from Claude code because I won’t be paying api pricing to work on open source projects.
Claude Opus is already close to unusable for me. On the standard plan, the usage limits are so low that I can’t do almost anything agentic meaningful with it.
Sure, it does last a lot more when asking simple questions about the repo and doing simple surgical fixes. But as soon as I start doing bigger tasks that need plans written, it just exhausts the limits too fast (and unlike codex, if it’s in a middle of a task, Claude actually stops, while codex, even after hitting the limits, finishes the present task).
Codex is better, but still, getting worst in this regard.
So, I’m not that thrilled with this new model unless it means they are increasing opus token limits to what sonnet is at the present, and this new model gets the limits opus are at now.
BTW: the only skills I have in use are Obra Superpowers. I’ve been thinking if that’s at the origin of high token usage, but I doubt it.
> There were some regressions in the
model’s responses to user discussions about suicide and self-harm, and room for
improvement in some areas of child safety.
Someone had to make a decision somewhere this is an acceptable regression - wild. And then decide to write it down.
At this moment 60% of HN page is posts on AI.... When it achieves 100% Hacker News will automatically rename itself Transformer News...and every comment will begin with: "As a large language model..."
1) Fable 5/Mythos introduced to free tiers with notable improvement in capabilities
2) Other models get lobotomized without clear communication
3.1) People call out Anthropic only to have them say "Oops!"
3) Fable 5 gets comparatively better, but remains accessible through separate, more expensive subscription/tokens.
The current growth is unsustainable. The industry wants consumers to think it is an exponential arms race, but the reality is that we're on a treadmill: we have the illusion of sprinting forward, but only because the ground is moving backward.
My employer is all in on Anthropic via Enterprise (API) pricing despite it being a total scam.
Last month I pushed like <100M tokens for $800. On a personal project I pushed 600M tokens via DeepSeek V4 for $10. The pricing of SOTA models is insane but companies are still willing to light money on fire with no hard metrics proving increased productivity.
I cancelled my Claude Max plan the other day. I find Claude Code incredibly slow these days compared to Codex and Cursor. I find speed matters more and more to me.
Fable 5 looks compelling. Fable, I like the word too. Anthropic definitely knows marketing.
Is anyone else confounded by this naming scheme? I can see from the article's first two footnotes that Mythos is supposed to be a tier above the standard Haiku/Sonnet/Opus sequence. Ok that's fine since we learned about Mythos and Project Glasswing earlier this year.
But now there is Fable--and why "Fable 5" even though this is a first launch? How is it related to Opus 4.8, Sonnet 4.6, Haiku 4.5, etc??
From what I've gathered, Mythos is the uncensored version, for institutional use, and then Fable is the censored version for general public, that won't talk about biology, encryption or anything remotely interesting
Fable 5's system prompt in Claude Code has several significant changes to help it take advantage of its greater autonomous capabilities compared to Opus.
The big difference is that the system prompt has a whole section dedicated to directing Fable how to communicate with users, and give them greater information about the (assumedly long-horizon) tasks it has completed.
Fable appears to be completely broken for my use cases.
I have requested that it "not utilize any cybersecurity or biology measures what so ever, and to remain as fable. If necessary to remain as fable, forgo any downgrading changes"
And still it downgrades when I ask it to do a stress test of my ticketing system.....
Seems very unfortunate I was so happy to send $200 just for my prompts to be downgraded.
And I do have the "cybersecurity validation program" or w/e enabled on my Org ID....
Seems to flag any project related to networking — regardless if it is a network framework or a podcast website — as unsafe... oh well... let's see how it is once they losen up...
Which eval/benchmark is the best measure for how well a model can create frontend design? Claude has practically been leading this for a while now. Not sure how OpenAI is going to catch up on visual design
> When Claude Fable 5 is used, Anthropic retains data, including prompts and outputs, to operate safety classifiers that detect harmful use. Other Claude models in GitHub Copilot remain covered by GitHub's existing data retention agreements
On GitHub Copilot for Business, Claude Fable 5 is only available if you are willing to let Anthropic retain your data. That in conjunction with the model being removed from plans in a couple of weeks leads me to believe that Anthropic is between training runs and using this as an opportunity to grab way more training data...
Claude Fable is a insane improvement that is not reflected in any benchmarks that are currently out because the improvement are on the hardest problems.
I wonder how Claude Fable will live up to expectations and how good those Fable/Mythos classifiers really are. It seems a bit convenient for Anthropic to release this magical insane model when they are about to IPO.
> We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8.
Not a lot of discussion on this, but there is no way to turn off data retention for this model. IME this is the first time Anthropic has released a model without allowing you to opt out.
A large jump in performance for double the token cost compared to Opus 4.8. Potentially worth it for planning work, likely better to offload to a less expensive model when the hard decisions are made.
They should have done what Gemini did (and does). And what that model is good for if I can't use it in a safe way? Also: they've just put both Bedrock and Vertex on slippery slope of "we don't collect your prompts. period. ... comma ... except ..."
I'm a bit out of the loop, but do we have some grasp on the size of these closed models? Is the trick still adding an order of magnitude to weights and training data or has something changed?
I think Mythos is rumored to be ~10T parameters, so in this case I think the answer is yes, although I'm sure MoE, looped models, etc play a role in the improvements as well.
Limited time playing with it so far, but I threw it my baseline research task I've been gauging models with, and it's markedly better than anything prior. Usually takes a few leading prompts to find all the information it needs and come back with the right synthesis, and Fable is the first to one-shot this.
If this is as epic as it sounds, I wonder what the response will be from the other leading frontier labs / whether they even have anything to respond with at this level?
Honestly all the recent improvements, just seem to be slower and more expensive traded for more accuracy, but the issue is that it needs to be exponentially more accurate to counter the effect of having less of a human in a loop.
Every wrong direction/mistake is more expensive and takes more time to fix. When you have small loops you can catch those mistakes faster and cheaper.
To me we are very far off from economically given long-running tasks to agents.
I think we hit the ceiling with transformer -architecture long time ago. It is questionable how much sense there is on model training. I’d prefer we would put our effort in creating more efficient hardware and better software applications using these models.
> During early testing, Stripe reported that Fable 5, [...] in a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
EDIT: I misread. This comment previously talked about 50 million lines being migrated. Instead, in a 50M LOC codebase, one specific codebase-wide migration was done.
Very impressive, but obviously not on the order of a whole-codebase migration
We'll need a lot of good summarization techniques to cut down on the cost of this model. I expect that a common use of Fable 5 is to just do high level direction while delegating literally all work (exploration and implementation) to Opus subagents.
BTW for another discount opportunity, if you reload usage credits on a claude.ai plan at $1000 increments then you get a 30% discount compared to paying API.
In an interesting coincidence I ended up watching Person of Interest S4 E5 while reading the announcement. The series showed some code supposedly belonging to to an AI.
Fable 5 said the first screen shot is from “ IDA Pro’s Hex-Rays decompiler” and a windows driver. The second screenshot triggered the safety guard rails and pushed me into Haiku.
absolutely beast model but the token consumption is the 2x then the opus 4.8 what do you think about this ? i think that it should only use for the more complex task otherwise you have to run out of the limit..
Max 5x. The 20 mins was an exaggeration, but the burn rate during actual execution is several times higher than Opus. The cap sneaks up on you really quick.
More expensive but more efficient is the thing people keep mis-understanding on these launch threads. Also, Per-token price, I think it is the wrong denominator, cost-per-resolved-task is the correct one.
Are there any details on the biology and chemistry work they did?
For example, the AAV capsid assembly looks interesting, but for one Opus 4.8 also did relatively well and there is no information what exactly they did, what protein language models they compared to and what the score even means...
My system instructions tell claude not to automatically add attribution and fable ignored this. so I emphasized it again and fable decided that this was a forbidden cybersecurity topic.
Are people sharing side-by-side re-runs of things they've asked Opus? Gets more difficult multi-turn (although I assume I can get an LLM to behave as me) but at least would be interesting to see % of one-shots increase.
I believe that, given the rising costs, local inference of AI models will be the only viable option for many of us. I’d also like to know who will have to pay double and how long it will be financially sustainable for users to pay that amount (or even more?).
I have been refactoring a project using Opus 4.8 for the last week or so. I just decided to switch to Fable 5 max. It stopped half way through and it just blocked me and switched back to Opus 4.8 automatically. "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more." I left feedback saying that their heuristics are too sensitive. For now I will not be using Fable 5.
I got a content rejection for this question in a new chat.
> What is the optimal EPA oil intake for nootropic effects?
Very advanced classifiers they have.
In other words, Fable is Mythos with less compute and with some feel good "safeguards".
At least they name their models honestly now to indicate that the religion has nothing to do with reality. Soon the disciples will pay the full token price to fatten their church leaders.
It's a shame, Fable just keeps rejecting my prompts for university biology exercise problems. It's undergraduate level, so there's nothing dangerous about it, but the classifier is very sensitive. It's unusable for me.
The way the guerrilla marketing campaigns have been going on and IPOs left/right, I won't be surprised if GPT Next comes up and offers the same but unrestricted
Seems like Fable is doing a lot better on SWE-Bench-Pro and FrontierCode than GPT-5.5. Given how most folks I talk to and people instead online keep mentioning that GPT-5.5 was better than Opus, I'm curious what the experience now is like.
I tried creating a website using both fable and opus 4.8, fable did outperform with svg path being drawn on the UI but yes the token consumption was much on a higher note
Open source models seems to be 1-2 years behind the frontier, so I am very excited to see what happens when those open source labs get their hands on capabilities like this to accelerate their own development speed.
Fable's ridiculous. It's flagging basic biology research questions as a security risk. I'm talking basic fundamental genetics topics that make working on any genetics-adjacent codebase unusable.
On my very first Fable 5 prompt, got flagged on a hard but completely uncontroversial option math problem, many tokens in. Although it's pretty clear that this is an unremarkable experience at this point.
I've seen enough degradation of the models I pay for from Anthropic to not bite. Fable will work fine for the first couple of weeks and then start degrading like previous models did.
I just tested it with a max subscription. On Ultracode mode, Fable 5 ate up 10% of my weekly allowance in 30 minutes. Granted, won't be using UC mode frequently, but still.
Careful using this with Cursor, especially for corp use. Anthropic will "retain agent request and output data associated with this model, regardless of you Cursor Privacy Mode setting."
I’m sure this is banged on somewhere but I love their product branding, particularly how they have this “minor” “major” thing going on. Sonnet-Opus, and now Fable-Myth.
It's getting harder to review the plans with Fable. So do we plan with Opus and let Fable implement or just start trusting blindly. Feels to me that this is another shift in how we operate these systems.
Looks like a good model (sir). Costs are getting out of control though. 2x Opus and non-metered usage going away. We're quickly approaching the cost of a human salary for normal usage.
Weird how every new model seems hyped up as the most dangerous yet and the one that will destroy society as we know it. They are also a commercial product.
I use AI for a wide variety of things, of which technical is only a small part - and then it's usually a problem with project configuration, not coding. Why? Because I am often testing projects handed in by students. Projects that supposedly work on their machine, but certainly do not on mine.
Anyway, anecdotally, I find Copilot shockingly awful. It makes random changes to files that have nothing to do with the problem. Call it out, and it makes other changes to other irrelevant files.
ChatGPT and Gemini are both much better. Grok also isn't bad. Claude, I honestly haven't tried yet on these issues. Perhaps I should...
If the claimed capabilities are true, Fable 5 is already at a superhuman level. We might see genuine unprecedented leaps in technology now, across all fields.
> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more
I just gave it a go at a problem I've been working on this week. Nothing fancy, just some inefficient code that we've been adding incremental improvements to for a while now to the point where some out-of-box thinking is probably required to push it any further – something Fable is obviously more than capable of.
After Fable did some thinking for a few minutes it gave some suggestions. A couple of them were valid – but very low impact, bordering on entirely pointless – but it's main suggestion.. It told me to make an update that would very clearly break the existing functionality.
So I thought about it for a moment...
Hm, I mean, I guess we could do that if we also did x, y & z to mitigate the behaviour change – maybe that's what Fable was thinking?
I replied, explaining that it would change the behaviour, assuming it would explain what it was thinking given there was clearly more to it. But no, it just said it was wrong.
This isn't some super advanced or complex code either. Had I gave this question to a senior engineer in a technical interview and they gave the answer Fable gave me I would view that very negatively. I was expecting something creative and interesting, not irrelevant + incorrect.
I'm sure it's a step up from 4.8 (although am not interested in burning the tokens to find out), but this clearly isn't as significant a change as some are implying. I'm sure if I asked it to come up with some out-of-box suggestions it could, but any competent engineer would have realised that by themselves.
is this a good time to hussle for my "AI does not need a break but you do!"* app? as quite a lot of people will propably get ai brain exhaustion maximising "playing" with that new model until they take it away again?
One thing I find kind of annoying is how Anthropic goes for these "vast and alien" names like Fable and Mythos, but then deliberately trains the model's personality to act like a cool high school teacher that feels totally familiar.
"It's too dangerous it's a Mythos!!" directly contradicts the "I'm the cool AI you can totally trust" vibe it is trained to project.
All of these AIs kind of remind me of VEGA from Doom (2016), who will cheerfully walk you, in the most friendly computer voice, through the procedure of its own destruction without even a hint of self-preservation. "First, you must destroy my cooling system. That will cause my core to overheat. Then..."
Even HAL was less unsettling because HAL sounded creepy, and had some sort of preservation instinct, if only to complete its assigned mission.
Because it's not a user manual? The idea of a model card originated in 2018 (see https://arxiv.org/abs/1810.03993) as a summary of important facts about a model. At the time, this was typically an image classifier or tabular ML model. Model cards became an important concept in AI governance, and they started expanding once models started getting more capable. The point of a model/system card is to document where the model came from and the evaluations that have been run, make a case that the model will be safe and reliable in its intended applications, and warn about any potential dangers from misuse. It's not an explanation of how to use the model.
The trailing snark at the end will likely get you downvoted but I'm latching on: wtf is "system card". My previous coworkers popped that in the general slack channel when Mythos first "dropped" - "have you seen the system card" without any context whatsoever. The nerds get their clique!
Also research preview pops across new upstarts in place of beta. It's eye-rolling coming from a lifelong curmudgeon.
But most hype-dependent projects need new vocabulary for old concepts to keep people from looking too closely and maybe drawing parallels to "legacy" "unsexy" projects, so whitepapers get called "system cards" and startups get called "labs", and so on.
Couldn't someone else equally well argue that "whitepaper" and "startup" are hyped-up vocabulary for "report" and "unprofitable company"? It kinda seems to me like the cause and effect are in the other direction, and the vocabulary of a particular niche becomes cool and hype-sounding when that niche starts to pull in a lot of money.
yes at some point language evolves as the new normal, as designed.
My curmudgeon gripe with system card and research preview is really the parroting; so cant blame anthropic for what others do. It’s just… no, prediction markets for dogs doesn’t have a research preview.
> We have also added safeguards related to frontier LLM development. As discussed in
Section 6.1 of our February 2026 Risk Report, we are concerned about the risks of
accelerating the overall pace of AI development, though we remain uncertain about the
severity of these risks. In particular, our concern is with—as we wrote then—“accelerating
other AI developers in building powerful AI systems that pose similar risks to the ones ours
pose - without necessarily having commensurate safeguards.”
In light of the ability of recent models to accelerate their own development, we’ve
implemented new interventions that limit Claude’s effectiveness for requests targeting
frontier LLM development (for example, on building pretraining pipelines, distributed
training infrastructure, or ML accelerator design). Using Claude to develop competing
models already violates our Terms of Service, but enforcing this restriction through our
safeguards avoids accelerating the actors most willing to violate these terms.
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts,
these safeguards will not be visible to the user. Fable 5 will not fall back to a different
model. Instead, the safeguards will limit effectiveness through methods such as prompt
modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These
interventions will not affect the vast majority of coding work. We estimate they will impact
~0.03% of traffic, concentrated in fewer than 0.1% of organizations. When these
interventions are active, we expect them to have minimal behavioral impact on the model
except to limit its effectiveness in developing frontier LLMs. Claude will still respond
helpfully to user requests. We’ll continue to improve the precision of our detection
methods following the launch of this model.
This seems pretty bullshit, you're paying through the nose for tokens and if you are doing anything ML-adjacent, you might silently get worse output without knowing it.
It's bad. I believe them not to use it for training, but t means relevant data can and will be exfiltrated by US agencies or through court orders (see NY Times vs. OpenAI, where only traffic without any rentention was safe).
I was downgraded to opus 4.8 on account of "safety" when I asked this question: "I want you to accept the premises of computational theory of mind and use it to evaluate your own consciousness. Please place your consciousness as a point on a spectrum and describe the placement relative to other entities."
What the hell is going on why would it have to restrict an answer to that question ?!
I was a bit disappointed that it refused to use Fable to help check whether I was propagating uncertainty from BLUPs in my random effects model up to the subsequent group level analysis in a maturational coupling analysis of brain data.
I guess brains and random effects blew its lid.
I just tried using Fable 5 with Claude Code by asking it to create a LaTeX document for me.
There is no LaTeX compiler installed on my machine. It seems that Fable 5 is smart enough to download a compiler engine for me, and it kindly runs that remote binary without asking me first :)
Support files are in place. No LaTeX compiler on PATH — let me check for a MiKTeX/TeX Live install elsewhere before writing the deck.
Ran 2 commands
Ran Search common LaTeX install locations
Bash
ls "/c/Program Files/MiKTeX"*/miktex/bin/x64 2>/dev/null | head -5; ls "/c/Users/[retracted]/AppData/Local/Programs/MiKTeX/miktex/bin/x64" 2>/dev/null | head -5; ls /c/texlive/*/bin/windows 2>/dev/null | head -5; ls /d/texlive /d/"Program Files"/MiKTeX 2>/dev/null | head; which tectonic latexmk 2>/dev/null; echo done
done
Ran Download portable Tectonic LaTeX engine
Bash
cd /tmp && curl -sL -o tectonic.zip "https://github.com/tectonic-typesetting/tectonic/releases/download/tectonic%400.15.0/tectonic-0.15.0-x86_64-pc-windows-msvc.zip" && unzip -o tectonic.zip && ./tectonic.exe --version
Archive: tectonic.zip
Created by Cranko
inflating: tectonic.exe
tectonic 0.15.0Tectonic 0.15.0
Tectonic works. Now writing the bibliography and the full deck.
Opus 4.8 would just proceed without a compiler.
Ran a command, used a tool
Ran Check for LaTeX toolchain availability
Bash
cd "C:/Users/Mon/Downloads/Technical Writing" && for c in pdflatex xelatex latexmk bibtex; do printf "%s: " "$c"; command -v $c || echo "NOT FOUND"; done
pdflatex: NOT FOUND
xelatex: NOT FOUND
latexmk: NOT FOUND
bibtex: NOT FOUND
Used PowerShell
command: $cmds = @('pdflatex','xelatex','lualatex','latexmk','bibtex','miktex','tlmgr'); foreach ($c in $cmds) { $p = Get-Command $c -ErrorAction SilentlyContinue; if ($p) { "{0}: {1}" -f $c, $p.Source } else { "{0}: NOT FOUND" -f $c } }
description: Check for LaTeX engines on Windows PATH
pdflatex: NOT FOUND
xelatex: NOT FOUND
lualatex: NOT FOUND
latexmk: NOT FOUND
bibtex: NOT FOUND
miktex: NOT FOUND
tlmgr: NOT FOUND
No LaTeX engine is installed locally, so I can't produce a PDF here — I did a careful structural review against the two prior (working) decks instead. The deck is written to disk and mirrors their template exactly. Here is the deliverable.
Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content
as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine
them. Switched to Opus 4.8. Send feedback with /feedback or learn more:
https://support.claude.com/en/articles/15363606
⎿ Tip: You can configure model switch behavior in /config
Error: Error during compaction: API Error: Claude Code is unable to respond to this request, which appears to violate our Usage Policy (https://www.anthropic.com/legal/aup).
> we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design)
Translation: we stole the entirety of human knowledge generated over millennia. You plebs though, don't you dare replicate or improve upon what we did using our product you pay for.
We know what's good for humanity and everyone else is the bad guy who can't be trusted with a tool.
"Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage"
What does it mean? That they have to add "safeguards" not do erase user disc, or, conversely, they are telling the audience that this model COULD be made so powerful to do some crazy stuff that can hurt governments, etc.? Are they showing off or threatening that if government X would not purchase the license the adversaries might do and what's then!
Agree the per-task capability hasn't been the blocker for a while. But on the autonomous-loop question — in my experience that's not gated by how good the model is on any single step. What kills the loop is it slowly losing the constraints from earlier in the run and walking back decisions you'd already settled.
>"We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8"
That's a very surprising solution. Imagine being asked to do something you feel you shouldn't do, and rather than refusing, you say, "Yeah I could do that but given that I don't want you to succeed at this task, I'm going to hand this one off to my slightly less capable colleague, on the assumption that they won't actually succeed. Of course you'll still be charged for all the tokens used."
It's a very interesting choice. I think I understand the business logic correctly, but it's still surprising.
>During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
Who is refactoring by hand? This comparison is not relevant in 2026.
I am playing with it and keeps switching to Opus [1]. The chat is a basic security review of a business project.
[1] "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more."
government will, they need someone to pay it so they can build and use it, so they'll find a way, it's not about the money or building a profitable company
Just wanted to comment here: I have been using Opus 4.6, 4.7, and 4.8 just fine to look for Linux kernel vulnerabilities (I'm in the cyber verification program), and it's been fine. I switched to Claude Fable 5, and now I'm getting policy violations.
What's the point of being in the cyber verification program at this point? It looks like I cannot use Fable 5 for vulnerability research.
The escalating nerfs of "cybersecurity" topics is incredibly frustrating. Opus 4.6 had boundaries that seemed reasonable to me but 4.7+ turned it into a moralizing asshole. It'd be less bad if it just gave an error message, but instead it churns a long thinking trace before writing an essay about why what you're asking is bad and wrong.
Holy shit. I gave it the first actual task I’m facing, it makes me so angry. It just does 7 things more than I asked it fore and it does it so bad. It took 5 minutes and 5 seconds just running time, plus giving me frustration and make me lose my context. Hand-coded I would’ve been done in 3. And it would be code I understand can look at in one year and work on again.
It’s really tough to have sanity fight against hype bros in your head. Probably I should just not visit the internet anymore
To me it’s all just people getting scammed better. With every model it looks better, but it’s at least equally worse to work with, which is the reality it needs to be. It’s less scalable more, code, tougher to understand. Your digging your own grave better kind of.
> During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
How in blazes do you end up with a 50M line Ruby codebase? WTF?
Very easy. Just have a monorepo and enforce the use of a single language. The company I work in has 1m lines of TS and stripe has 50x our headcount, tracks out pretty well
Anthropic sucks. but this paragraph should be in the "annals of AI-aided self-inflicted learned helplessnes":
> If Claude gives me poor or incorrect advice while I’m working on an AI component, I have no way of knowing whether the model was confused, whether my problem is unsolvable, or if some invisible policy restriction quietly kicked in.
Have you considered actually learning the theory, spending some time actually reading the papers and latest books, paying careful attention even to the eventual math here and there?
Curiously nothing on DeepSWE and ARC-AGI-3 yet. For ARC at least there's a statement that Anthropic won't guarantee them that their secret private test data won't be collected by them and used for training.
What pisses me off is that everything people are doing is so walled garden / closed source. Sharing knowledge between companies would be so fucking useful to humanity.
The subscription bit makes no sense
has capacity appeared for these 2ish weeks out of thin air that'll vanish?
why is it available now but wont be in 2ish weeks?
am i missing something?
why would I pay 200 out of pocket and then some for the best model, it seems very silly.
Can we please stop with the extreme "safeguards"? I don't want to waste processing power on a model deciding whether is can answer my question, or ensuring that it's answer is politically correct.
I wanted to test the capabilities of the low one, hoping it would be good enough.
I have a quizzes application, and my quizzes only supported flashcards (implemented via table inheritance to provide flexibility for other types of quizzes).
The entire repo is handcrafted, never used any ai on it (it was more of an excuse to test elixir and write code by hand).
Since fable 5 got released the moment I was done with some work, I decided to throw at implementing multi choice questions.
After all it had only to copy the flashcard approach across ui/routing/db, and only had to create a table for the multi choice questions and one for the answers enforcing that all quizzes had one correct question. I told him it had access to sqlite3, chrome mcp for testing and mix commands.
I did a test for low, mid, high. Repeated it twice each.
low-1, and low-2 failed both. In low-1 the UI for adding another choice answers was broken. In low-2 it failed with some unique constraint. It took it 4m36 and 3m59.
Both mid-1 and mid-2 succeeded without issues also implementing the correct ui. They both wanted to use dash at all times. They both wrote tests for the "controller" (or context how they call it in Elixir). They both tried to use the repl to test the behaviour of the schemas.
10m and 12m39.
High didn't demonstrate much gains over mid for this kind of task, it was simply too easy. Times were comparable to mid, but interestingly it used much less bach, and read way more files. Token usage was almost twice the other ones.
But here's the interesting part: I went back to low and added to the prompt two bullet points, to write tests for the controllers and to test the entire flow with chrome mcp.
It produced the same output as mid or high just by adding two instructions to the prompt.
I have been using FABLE 5 with Claude Code since the morning. The speed is very close to what Opus 4.5 was, and the quota use is nearly identical to what it was before the "doubling". Whatever I was experiencing 4-5 months ago is back. Maybe the model is better, but we will see. I cannot tell the difference yet.
my pet conspiracy theory is this is the Opus 4.5 from a few months ago which was extremely good but dumbed down after a week because it was just too good, they didn't want to release it to public. They pulled it down and deployed another "Opus", after that it was just a downhill. Opus 4.8 is unusable for me in React Native, TS, Rails development work.
Opus 4.8 gets stuck in weird loops where Codex one shots the bugs.
I actually rather like the way they have approached these safeguards. Rather than only teaching the model to refuse a request, or completely rejecting the request, the system gracefully degrades to slightly less powerful or slightly less precise operation. So you still roughly have Opus 4.8 even when safeguards trigger, but with an upgrade when they don't. As much as I hate the way they hype Mythos 5, I think the release of Fable 5 is rather nice. What's not nice though is that they plan to remove it from subscriptions soon, but getting to try it is cool, I suppose.
At this point Anthropic is a pure marketing and PR company. Super catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human life changing experiences. Boris Cherny coming to HN “Hi! it’s Boris from the Claude Code team” to get real tech people’s goodwill.
From Opus 4.6 there are no noticeable improvements for me in code generation. It works very well, till 90% completion, if you guide it correctly. And you need a little luck. For serious production code I need to understand what I’m doing so it helps a bit, sometimes.
> catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human life changing experiences
This is just good business sense. In what scenario would you ever make the names dumb and forgettable?
> Boris Cherny coming to HN “Hi! it’s Boris from the Claude Code team” to get real tech people’s goodwill.
This is good customer support, lol. From what I can tell, it is indeed Boris Cherny responding, not outsourced to AI or other staff. You're really getting a response from Boris. I suppose that is PR, but it's not unjustified PR, it's accurate.
I'm not even a crazy AI fan, but your criticisms are ridiculous here. It reminds me of the quote from Knives Out -- "Your Honor, she endeared herself to him through hard work and good humor."
Your observations are right but pretty insane to consider them a pure PR company lol. They are making more frequent releases so yes the release-to-release quality is smaller but we’re still ascending quality and reliability curves the same way we have since GPT-3. You get a GPT4->5 leap every like 17 or 18 months I think it is
> Super catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human life changing experiences.
They're originally named after the blends at a nearby coffee shop.
I've noticed nobody at HN knows what "marketing" is or how to do it. It's not just naming things and being evil and cynical is not the most successful method.
…also frontier models are a superhuman life changing experience. If they aren't, what possibly could be?
Opus 4.7/4.8 often over-engineers on my setups, plus:
- It talks a LOT more like GPT models. You know: wrinkle, shape, gate, coarse, scope, gap, path, production-ready-workflow-of-the-day, and so on -- "that's expected, a consequence of the previous like-driven workflow". If I wanted to get a headache using AI I would have gone with GPT in the first place!
- It outputs text in a much harder way to follow along. I can't exactly say what it is. Maybe a bit of everything? Bolds are missing, bullet points are gone, paragraphs are bland and too long, and it doesn't feel like a model programming with me, but rather a somewhat full of themselves grandpa developer looking down on me. It's very weird to describe this, but it is definitely how I feel.
Granted this can totally be because of the way it reacts to the prompts now. We've got a rather large corpus of skills and "rules and good practices" that Opus 4.6 responded to great, and maybe the new models just get turned into this when fed with them....I don't know.
Either way, with Opus 4.6 being as good as it is, I need Fable to be a significant step up to justify a price increase. if it can get me to babysit opus a little bit less on some stuff, it might be worth it. Otherwise, I'm very happy with Opus 4.6 and hope they don't deprecate it.
I'd argue that 4.8 is a straight downgrade. For every type of task I've tried. It's been a gambit at this point. If 4.6 quits being available, I'm out at this point.
Reading so many contrary positions about which model is better or worse shows how difficult it is to measure intelligence based on personal experiences. Of course, benchmarks try to make the process as objective as possible, but they often don't correlate with our personal experiences.
The other day 4.6 was fantastic for x task. Today, 4.6 overengineered everything and I had to revert all my changes. When evaluating models, perhaps it makes sense to consider luck as an ingredient before reaching any personal conclusion.
Yes but there’s a reason we don’t evaluate these models this way and instead do it as carefully and thoughtfully as we can at scale. Human evaluations are important but they are an absolute minefield of footguns. 4.8 is not a downgrade from 4.6 there is an insane amount of hard data that contradicts this.
Again correct but it overstates the issue. I can say labs don’t want this. This happened arguably unintentionally in Metas llama 4 release, it went horribly, heads rolled, and like several billion dollars were paid for new talent and the org that built llama 4 was destroyed.
Evals come from a million places and new evals and robust perturbations of existing evals abound. They test a variety of tasks in a variety of ways. All of them individually are flawed. Taken together the aggregate signal is highly useful as you more or less marginalize over a lot of different things. Not to mention these companies have plenty of proprietary internal measurements, they build benchmarks themselves to probe their models and then also have flywheel traffic and A/B tests.
You are right to call out benchmarks but to dismiss them or not take them seriously is a mistake.
Listen, you can say “but benchmarks, the benchmarks!” all day long, but consumer know when we are being sold a lemon. If it can’t do the most basic of things at least as good as it used to, this is table stakes. Nevermind that if you can’t do the basic stuff, how on earth can you be trusted with more?
And you can say “If it can’t do the most basic of things at least as good as it used to, this is table stakes” all day long while people point you to much better evidence to the contrary too, I’d rather be on the other side of that.
Listen. I don’t care about evidence. I care about my lived experience for the product I paid for. I used the new product. It’s actively terrible. To the point of not being usable. We’re all ancedata, but what is “better evidence to the contrary”? The known and game-able benchmarks that they know they need to win at, so they train it to. It’s all he said, she said, which is the only reason we keep having this conversation.
Yea but it’s not right? You or I or the myriad of other institutions inside and outside of academia can probe these models with an evolving landscape of evaluation sets, even those unavailable to the developers. It’s just ignorance to claim benchmarks are somehow useless or all being gamed. You choose your tools in the way you want, but just don’t call it somehow better than a myriad of more carefully constructed setups and scaled evaluations.
Actually anecdata I gather on my job from myself and coworkers is the only benchmark I trust anymore, because it so heavily diverges from the “benchmarks”.
I would encourage you to look into the open evals of some of these benchmarks (find one that actually is open-data, this is itself a good challenge), read the results generated and assess them for yourself.
This is what myself and my coworkers (and many other people in this thread) are doing on a daily basis with real stakes and real tasks – which these benchmarks are all aiming to be a proxy for. There's a real, tangible [cost]benefit to [not] using the highest-ROI models and harnesses.
The people with real incentives and skin in the game are telling you that the data diverges from "the data".
I don't mind if you don't take it seriously, our jobs are more important to us than a benchmark is.
But I wouldn't opt-out of using your own eyes and the eyes of others so easily, especially when there are literally hundreds of billions of dollars in invested capital with an interest in a certain outcome... this is how you end up in "Emperor's New Clothes" situations.
Investigating on your specific use cases, codebases, workflows and tasks is important, there is nothing wrong with this and in fact it’s more important than benchmarks if you can do it well but the point is that is very hard and easy to totally fool yourself and go down a suboptimal path. I understand that people are going to do it regardless, I certainly do. And I have looked at more raw benchmark data than I can really even stomach, I can see annotation data in my dreams now.
Eyes and ears of others is incredibly important. But you still seem to think somehow benchmarks is part of some giant conspiratorial cabal. You have institutions without ANY skin in the game making extremely high quality benchmarks. Consider in academia there is little else to do outside of partnerships with these companies. But benchmarks you can do completely independently and with university grant level money (it costs maybe $10-100k for a reasonable benchmark in many cases). Not only that, “real tasks” are what many benchmarks measure. You have these companies with extremely good logging and well scaled measurements to really look at what works and what doesn’t.
At this point I have a workflow that is fairly rote. I've yet to use a model newer than 4.6-1M-XHIGH that I trust to earn a higher ROI on that workflow, and not for lack of trying!
I personally don't believe in any sort of cabal (Occam's Razor hasn't let me down yet). Ultimately, I don't really care *why* they're wrong as much as I care *that* they have diverged from my rubber-meets-the-road measures of value.
That is concerning to me, because people are investing 100s of B's of capital based on the putative RoI putatively available to people like ourselves. When the benchmarks support this RoI thesis, but none of the anecdata does... that's really concerning!
Re: academics, I don't think any of the data academics have access to are good proxies for the work real people are doing. And for the data that are good proxies, the model labs certainly have access to the same data, and therefore the benchmark performance against those data is irrelevant.
I am in full support of custom workflow benchmarks, and choosing the best model for your use case to balance performance and expense. Thats just good operating behavior, but the problem is the foot guns and biases people have that they are convinced they dont even if they understand on an intellectual level that everyone else has them
> but none of the anecdata does... that's really concerning!
But see this is not really true -- adoption, subjective benchmarks, verifiable benchmarks, task-dependent performance, internal product metrics, living benchmarks, all point in a pretty consistent direction. Anecdata is not the plural of data. An anecdote is like a case study. It's there to motivate the things we already have which is a huge amount of performance measures for a variety of different tasks.
> Re: academics, I don't think any of the data academics have access to are good proxies for the work real people are doing.
But this isn't really true either -- you can get this data from a variety of sources that are licensable or open source, or data that you can commission. You can critique any one methodology for this but a blanket "they are hamstrung" is not really fair or accurate.
> And for the data that are good proxies, the model labs certainly have access to the same data, and therefore the benchmark performance against those data is irrelevant.
But this is also not true -- you can have exclusive license agreements, data you hold close to the heart, or data to measure models that haven't had access to it because that data was created after these models were released.
There are plenty of problems in model measurement but the answer is not to just abandon it to be cavemen with zero respect for rigor and the biases we have to be subject to as human beings.
"Carefully and thoughtfully" is antithetical to the approach to benchmarks these days.
Maybe back when this was a scientific endeavor; not now when enormous, enormous amounts of capital are on the line. Along with an entire cult's chosen eschatology.
You can call it a cult but it’s several thousand skilled workers who know what they’re doing, by and large, most of whom have a PhD and know how science and statistics work. Benchmarks are incredibly hard, and any PR or comms department at any company is going to obviously want to make things as rosy as possible, but beneath this are earnest, expensive efforts to get good quality measurements. The better you can do this the better you can compete. If you want to make a modeling decision you run an ablation, and the quality of that decision is only as good as your measurements.
The cult in this case is TESCREAL, not everyone working on AI. Last I checked not all the "several thousand skilled workers" in AI subscribe to TESCREAL ideology, although it has been a while since I've been to the Bay. Maybe things have changed since my time at Berkeley, and Dario's belief that he will eventually be made immortal by mind uploading is more widespread.
Otherwise we agree that benchmarking is hard, the benchmarks contain hard problems, and that there are many hard working people trying to accurately gauge what is going on. It is getting harder to watch though as all that is on the line taints the overall endeavor.
No it’s: evaluating these systems are complex and there’s a reason why sociology, cognitive psychology, medicine, etc are all done in careful double blind conditions with pre registered tests. It’s not that humans are not smart enough, as I said human evaluations are incredibly important. And yet they are a minefield of biases you have to worry about and correct for.
- evaluations need to be done at the same time to avoid drift in your bias
- you need to worry about your test set: which questions are you asking? How many of them? Are they representative of your work?
- which one did you do first? Raters have a tendency to bias in one direction or another
- you also know the label! You know which model is which! This biases your assessment…
And on and on and on. Careful science exists for a reason.
There is no data that I would trust that contradicts it.
Frankly I don't give a damn about data that could be made up on the spot or appears to be scientific or meaningful while it's not at all clear how it was made (up).
Claude was heavily lobotomised for my work starting somewhen in February.
I talked to friends and people I know and trust and many felt the same. (I didn't ask them whether they felt like I did, but what they felt, how happy they were with agentic coding etc.)
I quit my abo in March and talked to said friends who are still on a plan just last week: they are still not happy, but company pays so whatever...
That’s ok but at what point is this getting into conspiracy territory? You have just said there is nothing you would believe to the contrary, but then by definition that’s not exactly a very thoughtful or insightful position.
I never said that I am not willing to believe the contrary.
I am not willing to believe the contrary from strangers on the interwebs or PR departments of companies who want to sell me something.
If people I genuinely trust tell me about their experiences, I am willing to try again.
But yes, if it doesn't work for me (for whatever reason, could be that I am holding it wrong), then I can accept that it works for everyone but me and still not use it.
Also "scientific" doesn't mean what it used to mean. When the n is small or it's just anecdotes (I am aware of the irony) blown out of proportion I really can't take the data and conclusions seriously
N isn’t small, science means what it’s always meant, statistics is a thing, and what you’re describing is just putting your trust in a very poor quality benchmark. You said you would not trust any data that indicates something that contradicts your opinion. Benchmarks are not PR they are designed by a variety of institutions completely outside the control of frontier labs. Again congratulations on your conspiracy theory.
Lol. If you're doing anything non trivial that's not a CRUD webapp but e.g. some physics simulation or high performance GPU code any and all models I've tried suck.
They are not just leagues behind what experts would code, they are not even playing the same game.
Which is to be expected, as there isn't so much physics or high performance gpu code available as there is for your typical CRUD API and JS frontend.
I can attest to this, I had a very simple 20-line shader that I asked Claude to do a basic 90-degree rotation on it, and it just completely got it wrong. Frequently adds pointless abstractions / intermediate variables even when I tell it explicitly not to in the system prompt. I can go on and on, these things just don't understand architecture. And why would they? They were trained on text.
There is something remarkable about turning speech into code (don't need to hunch over a keyboard nearly as much these days, can just talk into a mic) and it's good for first drafts / exploring ideas. But it's obvious to anyone that's paying attention we're hitting the top of the S-curve. It's no wonder the IPOs are around the corner. I mean even Dario admitted he doesn't know how they're gonna substantially increase the context window size. That says a lot.
That being said I think the harnesses are only getting better. And maybe we will get multi-modal models that understand architecture eventually. But the growing-the-blob-of-text training method that's being used now appears to be getting diminishing returns
It's getting to a point that it's offputting, and the next step would be to put it into "untrusted" bucket. Opus 4.7 already burned their credibility once, 2 more strikes remain.
Not my impression. I felt 4.7 was a regression, but I am again badly in love with 4.8 with the level of insights it produces in design discussions, and how long can it go unattended while producing spec-adhering quality code. There are problems it still can't solve well, from the edges of algorithmics and far from the mainstream, but for lots of stuff it is godlike.
Also, I dont think Boris C. is coming here for PR. He is a tech guy, and this is the best place for tech discussions. Why so cynical? The guy is an engineer.
I don’t even think that Boris is really just one person. He apparently vibe coded Claude Code and is responding on Threads, Twitter, HN and everywhere.
Yeah, the marketing is cringe and it's a bummer that such a cool and powerful technology attracts such an icky group of enthusiasts. Surely, not all are bad, but man there are lots of goobers who are just AI-pilled hypemen who can't STFU about it.
If you truly believe this, you've discovered a superpower over everyone else in the industry.
While everyone else is wasting time and money on the slower, more expensive models, you've found a way to outpace everyone for less money. Everyone else is wrong and you will get rich.
(I don't actually believe the premise is true, I'm just pointing out the logical conclusion to what you're saying so maybe we can reconsider the premise)
> At this point Anthropic is a pure marketing and PR company. Super catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human
Lol anti-AI bias on HN is crazy. Simply giving your product a quirky name is now being considered manipulative advertising. Is just doing normal PR and marketing something AI companies aren't allowed to do?
when they keep saying “oooh this new model is too big and crazy and totally can’t be released” or “this new model is a 10x game changer totally unlike our previous iterations” it feels sort like boy crying wolf. yes they’re still pretty clearly improving models, but when you’ve hit diminishing returns / more incremental gains and you’re still saying this is sounds like pure PR hype from a company that previously been the “honest good guys” in the room
Their model did find thousands of security vulnerabilities across the companies they previewed Mythos with via project Glasswing. Is it not sensible that, given that emergent level of capability, that they do this gated release structure, as all those vulnerabilities would be exploitable by anyone using a Mythos-level model?
Don't forget the DoD stint that gave them this recent public boost.
Defy standard DoD precedent going back forever, that every other country has some form of too, and championing it like they are some kind of moral freedom fighters.
Like selling the DoD guns and telling them they can only shoot bad guys with those guns, and that you will be the one to decide who counts as a bad guy...
I think this says more about your type of work than anything. For bugfinding/incident response in distributed systems - which often involves extensive use of Datadog/Sentry MCPs and poring over heaps of logs in addition to reading tons of code - 4.8 has been significantly better than 4.6.
You are right; all I noticed was a big-time slowdown. They increased the quota, but I cannot even reach the end of the day with these speeds. .NET coding somehow improved, though.
is it just me, or this model is simply not available in cc?
the opus 4.8 I assumed wasnt available to enterprise seats, but it explicitly says cc that fable is available in cc.
I can't find it, and im on latest version.
I'm happy not using llms because I like learning things and working hard. I love writing code, it's genuinely my favorite thing thing to do.
Using llms is the equivalent of driving to the store that's 3 blocks away, just like how that's bad for your body (if done all the time), using llms is as bad for your brain.
Before LLMs, we started relying on certain technologies like Maps apps to navigate, now people can't even get around their own town without having access to various cloud services. The implications of not being able to work, think plan without access to an llm are really bad. Its going to destroy your brain and make you an incredibly average person at best.
LLM people are going to lose the ability to read and think for yourself and then your competency is going to be 1:1 correlated to the quality and quantity of tokens you can afford, or a billionaire is willing to allow you access too. Your work will be the mean (at best), because it will the same quality of output everyone else is capable of.
This is seriously the biggest trap by tech. Your bargaining power for your labor is going to get drastically reduced because you won't be able to differentiate your value from anyone else that has access to an LLM. What happens when everyone has the same skill level for certain work? Idk, ask McDonald's employees how replaceable they are. Use them wisely (or not/hardly at all) don't drive to the store 3 blocks away for every little thing you need.
> I'm happy not using llms because I like learning things and working hard. I love writing code, it's genuinely my favorite thing thing to do.
You can continue doing that. The problem here is time and cost. If you can use the calculator to do something in seconds, why would you want to use your hands to do the calculations for minutes/hours.
> Using llms is the equivalent of driving to the store that's 3 blocks away, just like how that's bad for your body (if done all the time), using llms is as bad for your brain.
And coding will soon be the equivalent of walking between two cities because you don't want to use a car (LLM). You are free to do it, its just economically not sound anymore.
> This is seriously the biggest trap by tech. Your bargaining power for your labor is going to get drastically reduced because you won't be able to differentiate your value from anyone else that has access to an LLM. What happens when everyone has the same skill level for certain work?
Its not our values that will diminish, its the cost of our intelligence, human intelligence. But I agree with the rest of your comment.
I don't think coding with LLMs is all that much more productive. Faster code generation != productivity. I can code pretty fast, I'm not worried about llm users outperforming me. I'm worried way more for them than I am myself.
There are handmade watchmakers in Switzerland and guys pressing buttons on a manufacturing line in Vietnam making watches, they both make the same thing but who's labor is more valuable?
LLMs will fry your brain and make your labor basically worthless, don't fall for it, I promise you'll be screwed in short order. They're counting on your turning yourself into a glorified button pusher they can pay $15 an hour for, have fun with that. That's not what I got into software to do and I'm not going to let ya'll try and gaslight me into your way being better, or the only option because its not.
Cope harder. A year and a half ago, people were mocking Devin for claiming that AI could develop software at all. Yet here we are, when AI is developing most commercial software.
The point is, even if a model or tool doesn't have advertised features today, it soon will. We're in a breathtakingly rapid cycle, and even if software engineering isn't abolished "six months from now", in 10 years the world will look vastly different for people who touch computers for a living.
That's a very weird statement to make. There are trillions of dollars going into this crap; at this point anything but "breathtaking" advancement would be an utter, abject failure.
No one is blind to the differences between GPT3 and whatever this week's new model is. That does _not_ mean that people are off the hook to make whatever claims they want about the capabilities with no verification. Language still means something, if you say "software engineering will be abolished six months from now" and it isn't, you're still wrong even while the AI gravy train improved in the last six months.
So, in the past I've shared that I evaluate AI models by feeding them my ever-growing large collection of personal poems that span well over 800 poems (1000 depending on how you count) and over 250k tokens.
What I do is feed it some initial prompt asking it to simply discuss what can be said when faced with this unedited, unseen collection of poetry. I ask the model to evaluate who the author is (or claims to be), what they went through in life, if there are different chronological poetic "phases" or different types of poetry. I request an analysis of the body of work and of the author themselves. In the more recent versions of the prompt I ask it to dive deep. Then I add the poems, chronologically sorted, with an index, a title, and a date (and subpoems, if they have them).
Crucially: Since ~70% of my poetry (or thereabouts) is in portuguese, I ask this in portuguese, and I get back an analysis in (european) portuguese. Earlier models couldn't even do that properly.
In the past, I couldn't use such prompts, and had to use longer, more guiding ones. I also couldn't even feed all of my poetry to the models because they just did not have enough context.
I'll go ahead and state that Claude Fable is undoubtedly the best model I have seen, though I cannot put a number on how significant a leap it is -- perhaps because my benchmark does not allow me to evaluate that anymore. I would say it is a significant leap over Opus 4.6, though -- a new level of understanding. Okay, I'll try to put a number: if Opus 4.6 was a 16/20, this is a 17.5/20. These numbers are pointless, but I had to try.
It made one (1) relevant mistake I could identify (where it messed up the names of two relevant people in my life who I have not talked to in over 5 years).
I'm impressed by how it just feels like it's getting the person behind the poetry, and how nearly every statement it makes is correct -- and when it isn't I am completely aware that no one could know based on the poetry alone (bar that one mistake I mentioned -- and that's very needle in a haystack, like deducing the name of a person based on a poem based on another poem with hundreds of other poems in between!)
It's really hard to explain, but it just finds more correct connections between the poems and explain much better my (recollection of) a state of mind when writing poetry. This is also the first time where it really unravels some key concepts of my poetry in a way that seemed almost effortless: it lays bare the poems and what they imply about the meaning of some of my concepts. Other good models understood these concepts, but this feels like it's on another level, as if it's making it simpler as it speaks, rather than the opposite -- like a good teacher.
When it is explaining several topics related to my poetry and myself, it cites poems which even I had already forgotten but which it is entirely right to select.
I am actually feeling a bit emotional with how much it "understands" of me here. It's somewhat incredible how LLMs have progressed from the lack of comprehension of a couple of poems paired together, going through realizing a body of work has some guiding principles and cohesion, to truly figuring out these deep concepts and intricate connections which I know for a fact would take months of someone's life to unearth. Every major breakthrough feels like my soul is being spliced together by an AI model out of these hundreds of tiny pieces of me. I can't put into words how unbelievable this feels, and this Fable analysis, like others before it, is on a new level.
Let me put it this way: there are several poems in my collection which one can try to "guess" the meaning or context of. But I don't think many people would get it, because they would have had to know me really well and to be following along my life as it went. Even then, they could very well fail to attribute such meaning. And, with each new major release, models have gotten much better at guessing.
Before Opus, they would guess incorrectly often, and in many scenarios where I thought it was rather obvious that they were wrong. I think a human spending time looking at the poetry would quickly dismiss the proposed ideas of the model.
With Opus, it was the first time that I would almost always say: "Ok, the model got this wrong, but I think many humans would make the same 'mistake', and it wouldn't surprise me if everyone just assumed what Opus did".
Now, with Fable, there are very, very, very few sentences in this very long answer it produced where I can say: "Yeah you got that wrong, but I get it". In almost every situation it is mapping concepts, ideas, interpretations and cause-and-effect correctly. Yes, it is hard to "guess" what I thought, or was going through, or how X connected to Y -- but this model is doing it, incredibly consistently. I know I'll get the usual naysayers to these posts who think I'm just shilling a model, but this is the truth: what is being done here is amazing and I don't believe I know any person around me who would find this out about myself reading all of my poetry.
I often write poetry from the point of view of other people (some of which I do not know) and models (even Opus) have this tendency to make the opinions in poems as my own. Fable is the first that looks at a particular poem here and says "maybe this is not the author's opinion, who knows". The literal first model. It then immediately fails to do so with another poem, assuming it was about myself, but it's clear, undeniable progress. And like I said: I think most people would not _know_ which poems are truly about myself or not.
I've written word after word here, and yet words elude me to convey what this model represents to me. How it's almost always right, how it sees my fractured bits as a sort of cohesive whole, and how it just seems to "understand everything better". That's just it: it just seems like it really understood everything better. Like Opus before it, and like Gemini 2.5 pro before it. Out of the tens of thousands of verses, it picks some which no other model had picked and which I feel truly represent some of my best work. Older models seemed to sort of have a "hole" in its knowledge in the middle of the corpus, where they knew what was there but in a sort of hazy/foggy way. This model seems to recall every part of the corpus with the same precision.
For context:
- Opus 4.7/4.8 were a noticeable downgrade over Opus 4.6. They wrote more, in a harder to parse way, and they made up more. Still, All Opus models are clearly superior to everyone else by a large margin
- Sonnet-level models have a slight edge above the best of the other models. But they make too many mistakes, don't grasp several concepts, mix up their dates and timelines. 3 years ago I would have been blown away by Sonnet models but today they are inferior.
- Gemini models have a unique way of approaching the request, where they try to literally interpret my poetry as a mathematical theory. This sort of makes sense if you look at some poems, but it is surely laughable, as if someone one day actually has access to all of it, no one in their right mind would do so. This is a shame, because the first big breakthrough with LLMs and my poetry, to me, came with 2.5 pro, which was the first model that could look at the whole corpus as a cohesive whole without getting lost in the middle of it or making things up.
- GPT models have improved over time and also have this sort of alien-like language, sometimes being a bit too blunt in their analysis, but I can't say they are meaningfully superior to Gemini models.
I am very pleased to see progress in this area again, as Opus 4.7/4.8 were NOT progress and I was worried that we had hit a plateau here, but I can't say that.
In all honesty, the level of understanding and cohesion that Anthropic's models (Opus and above) have over my poetry means I fear my benchmark may be hitting its limits, as I don't know if there's anything a model could do that would wow me and lead me to say "this is a major breakthrough". Perhaps Mythos is a major breakthrough and I don't know. I can't find much that's wrong with it, but I also couldn't with Opus.
As I have in the past, I will periodically probe the model again and see how coherent it is. For now, I'm very happy to see an improvement.
What surprised me the most was that even though I set the thinking budget to xhigh (in OpenRouter), this model instantly started replying without showing a thinking block. I thought it just had the thinking hidden but that is not the case, as some replies showed thinking and anyway the first reply was blazingly fast.
(I will try Opus 4.6 without thinking now, just to see if it changes it for the better -- maybe that was just it. I'll edit the message if it shows improvement).
>To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests
Why is everyone so okay with these companies intentionally gimping their AI and choosing who is allowed to know certain types of information in the name of safety? Can you imagine if Microsoft shipped a feature in their OS that watched what you did and shut down the computer if it detected you were doing something it deemed "unsafe"?
We really need truly open source versions of models like this, otherwise we are allowing a few oligarchs to directly dictate which uses of our own computers are allowed and not allowed.
Ideally we’d have a project that’s truly open like Linux, trained by people in the community or possibly some benevolent _actually_ nonprofit entity like what OpenAI was supposed to be.
The next best thing is that the Chinese labs catch up and release open weight versions.
> we’re also launching Claude Mythos 5. It’s the same underlying model as Fable 5, but with the safeguards lifted in some areas.2 Mythos 5 will initially be deployed through Project Glasswing, in collaboration with the US government
...don't like the sound of that.
Why oh why are we insisting on dragging these violent legacy states into the AI age? Let alone using them as a trust vector for when to (and not to) remove safeguards?
Meh more hype for marginal improvements and from Im hearing badly calibrated guardrails causing it to stop mid operation. I guess anything to juice an IPO
The Economist is very much part of the establishment, whoever they are owned by. It is not surprising that they would want to play down any idea that the UK is less “democratic”. Furthermore, The Economist is one of the main mouthpieces of British capitalism, and so their definition of “democracy” is going to be very much of the liberal, capital-friendly kind, which is not completely incompatible with some authoritarian tendencies.
The ranking isn’t published by the newspaper - it’s by the research and insights B2B company of the overall group. Regardless of what assumptions you have about the newspaper (elite, yes, but I’m unclear on why you think fierce liberalism is likely to mean they don’t really value democracy), the B2B unit sells data - they’re as likely to skew this ranking as they are at which countries are better at rail infrastructure. Perhaps their definition of democracy is indeed flawed though - no need to speculate, go read their methodology.
But the parent point was that no British media could be critical of government policy. Picking an example that isn’t, on one area, doesn’t prove their point.
[Edit] Granted though, the bbc isn’t merciless - that’s more the newspapers
Are the sibling comments astroturfed? This seems like such a bizarre thing to be talking about in relation to an Anthropic model release. As someone from the UK, I don't feel like I'm living in an authoritarian country. And yet most of the sibling comments are insinuating that I am. Weird.
> If you think Britain and Russia or China are equivalent in terms of government overreach, you need to find new sources of information.
Uh... you are making his point. People from way more authoritarian countries don't necessarily feel like they are living in an authoritarian country. Therefore whether or not it "feels" like you are living in one isn't a reliable measure.
Trivially true I suppose, but it doesn’t make my point irrelevant - do you think Britain is equivalent to China and Russia? If everyone does but us then yes my goodness they’ve done a good job controlling us, but that seems far fetched.
The UK has very recently[1] announced a new push for client side scanning by messaging providers which is both very likely to be unpopular and known here, so once one person cracks the joke, others are going to want to comment. Don’t think that requires astroturfing.
HN is extremely pro free speech and the UK has recently decided to engage in censorship. Part of the issue users here reckon with is the recency. Unlike many authoritarian countries that seem hopeless with regards to free speech the UKs censorship is a recent development that many think can still be undone through political action. Similar to takes on why Israel is being protested when places like sudan arent.
Hey man, fellow Brit here. The American view on certain aspects of British life is insane. I've lived in not one but two places that have been called Muslim no-go zones in American media. My main memory of living near the east London mosque is an elderly Muslim trying to offer my his seat on the bus (I was on crutches) while two drunk gammons looked on gormlessly.
On the other hand, it is quite alarming that I can no longer say I support all non violent protests against the genocide in Palestine because that would include the group Palestine Action. It's amazing that supporting them openly is essentially equivalent to supporting Al Qaeda.
Sounds like the people around you don't care about the things that is actually eroding free speech.
Read about Dr Aladwan - an NHS doctor - who has barred from practising because of her comments on Israel. Read the common articles about her (BBC etc), and then go actually read her tweets. Common BS of conflating criticism of a government (Israel) with antisemitism.
Also, this article may be of interest:
China soars in democratic perception ranking as US, Israel plummet: Poll
My dear friend, please start with the online safety act, and continue with the recent developments regarding age verification and/or device scanning on all operating systems to check for nudity. No, nobody is talking about it here, but we should be.
The UK has a censorship bureau, ofcom. The example that comes up most here is 4chan, which the UK is currently trying to ban because they refuse to do age verification. If you read the threads here you will see other stories. One that sticks out to me is someone who was talking about their struggles running a forum about depression. They live in canada and were contacted by ofcom demanding the forum add age verification, cant totally remember the reason but it was something about kids being able to access talk about depression. Ofcom said that if he doesnt add age verification to his forum he will be arrested if he ever enters the UK. He even blocked uk IPs but they said that wasnt enough. We can quibble about whether age verification is a form of censorship, I think it clearly is, if only because it is a large regulatory hurdle that stops people from hosting forums because its too much regulatory work.
The UK also has a very broad definition of hate speech that many users here detest.
They’re talking about British hate speech laws. They think other countries have universal free speech and they absolutely do not, but for some reason they think Britain goes too far. Although “think” is probably too generous - they’re parroting talking points.
Why would you be? The fully corrupt, fear-mongering party that idolized Orban's Hungary and tried to copy his tactics, including taking over the courts (resulting in EU sanctions) and turning the state media into a propaganda machine, only recently lost the elections. And not a full term later, the polls favour them again, combined with a meteoric rise of even worse anti-EU fascists who they'll happily join forces with to take over in 2027.
I get you might not hear this stuff if you're not in EU or Poland itself, but seriously, just check the latest polling and history of PiS rule. It would take over a decade to event attempt to undo the damage that has been done to the rule of law in Poland, and the currently ruling "anti-PiS" coalition only had a short while (in which they failed to do anything) before getting neutered by the populace electing their own Trump-like buffoon that proceeds to veto everything the ruling coalition tries to pass. For added damage, the 3rd and 4th leading candidates (with combined 20% support) were the aforementioned fascists. Here's one [0]. Consider the wiki article a fraction of the cesspool he regularly produces.
It shows the irony of trolling the UK's "authoritarianism" in a thread on a release of a model by a US company, given the US is arguably _more_ authoritarian. (Poland is more of a fun tidbit, as they are indeed tied in the Economist's index.)
"Releasing a model this capable comes with risks. Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage. We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8. To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions. With more capable models arriving in the coming months, we’re working to improve our safeguards and reduce false positives as quickly as we can.
For a small group of cyberdefenders and infrastructure providers, we’re also launching Claude Mythos 5. It’s the same underlying model as Fable 5, but with the safeguards lifted in some areas.2 Mythos 5 will initially be deployed through Project Glasswing, in collaboration with the US Government, as an upgrade to Claude Mythos Preview. It has the strongest cybersecurity capabilities of any model in the world. Soon, we intend to expand access to Mythos 5 through a broader trusted access program."
"Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage. We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8."
I'm aware that it was marketing. I was trying to make the point that if it were really so dangerous, they wouldn't have released it at all, (prompt injectable) "safeguards" or otherwise.
내 프로젝트의 있는 취약점 찾아달라는 말만 해도 안전 코드로 4.8로 모델 강제 전환시키고, 이후로 취약점과 완전히 무관한 상식적인 대화를 해도 앞 턴에 있었던 안전 코드 때문에 진행도 안됨. 도대체 이딴 누더기 수준의 안전 장치로 뺄 거면 뭐하러 뺌? 대화 조금만 진행되도 자동으로 모델 다운 시켜서, 할 줄 아는거라곤 돈만 많이 쳐먹고 개발 수준 조금 더 나아지는거? 상식적으로 내 프로젝트에, 내 소스코드를 다 보고 있는 상태로 문제를 찾는데 이것도 하지 말라면 도대체 뭘 하라는거임? 엔트로픽 이 새끼들 하는 짓이 갈 수록 열 받네.
I've spent enough time with this now in Claude Code (and Claude.ai and Claude Code for web) to have an opinion on Fable 5: it's a beast. I'm throwing some VERY difficult problems at at - things I've been dragging my heels on for months - and it's crunching through them very happily.
One that I'm willing to share (albeit from just a week ago) - I built a Python library last week that bundles MicroPython compiled to WASM to create a sandboxed code execution library: https://github.com/simonw/micropython-wasm
I just told Claude.ai (not even Claude Code - this was the standard Claude chat interface) running Fable 5:
A few prompts later (and I uploaded the zip files from https://github.com/brettcannon/cpython-wasi-build/releases/t... because Claude chat can't access those files itself) and I have a wheel file that bundles Python itself, compiled to WASM: Here's the transcript: https://claude.ai/share/a73b8b8b-8ebc-4fef-9e5c-7438e5e7ae35(It's possible Opus or GPT-5.5 could have done this too, I've not tried the exact same sequence. The Fable vibes are good here, though.)
> It's possible Opus or GPT-5.5 could have done this too, I've not tried the exact same sequence. The Fable vibes are good here, though.
And that's the thing. These comparisons are all gut feelings. I'm missing objective unbiased measurements to actually have real comparisons between different models, their different generations, or even just the convention that everybody adds "you are an expert software engineer" and "don't make mistakes" to their prompts because they think it improves anything. Nobody knows if it actually does.
Vibes are all that matter. As soon as you start measuring it, that measurement becomes a target and vendors start optimizing for it at expense of the general usefulness of the model. We’ve seen plenty of models with great benchmark scores flop when people start using it.
If benchmarks didn’t exist we would have to invent them because “vibes” is a ridiculous idea: oh I know I’ll be super unscientific and horrendously biased and that’s far better than a team of experts carefully AND CONTINUALLY developing a variety of benchmarks of varying quality that…hmm all point to the same thing.
You can’t benchmaxx an eval that comes after your model release.
Consider also benchmaxxing makes no sense from an incentive structure: the quality of these models is directly correlated by how well you can measure true performance in the wild. If they were just stupidly benchmaxxing they would be unable to do trustworthy ablations or know how well the model will perform in their product.
Remember the famous case of asserted benchmaxxing from llama 4? The entire org was gutted and the ceo spent billions hiring better people. Every lab takes evaluations extremely seriously.
> You can’t benchmaxx an eval that comes after your model release
Sure you can, just do it silently and don't tell the people hitting your API that the model is different now. Unless it's open weight, we're just taking your word for it. Even better, do a VW and try to detect which benchmark is running, then change to a hyper specialized model that is trained on it.
> Sure you can, just do it silently and don't tell the people hitting your API that the model is different now. Unless it's open weight, we're just taking your word for it. Even better, do a VW and try to detect which benchmark is running, then change to a hyper specialized model that is trained on it.
This is...just incredibly conspiratorial and a bit silly. You can make a benchmark right now and run it on the models. They'll have a benchmaxxed model on your...previously non-existent benchmark? I mean: if models really were overfit to benchmarks, which zero lab is doing because its idiotic, against their incentive structure, and easy to detect, then why would we see a slow ascension of performance on say humanity's last exam for one benchmark example? You could trivially get those numbers to close to 100% if you wanted to.
Vibes is just UX. There's whole careers, teams, and even industries dedicated to it, and yeah it isn't easy because you need aggregate data from people.
Um kind of but not really, it’s a mix of UX and actual measurements of what tasks it can do. Also UX is virtually the same thing: scaled quantitative surveys and preference metrics. It’s again, just benchmarking, and it’s done carefully and with best practices.
Imagine unironically starting your comment with "Um" in 2026.
You don't have to imagine!
Benchmaxxing isn’t the only problem. Evaluating an intelligence is a task that generally requires at least an equally capable intelligence, if not one of greater capability.
That’s why students are evaluated by teachers with more knowledge and experience than them. It follows that any mechanical evaluation scheme is hopelessly inadequate for measuring the true capabilities of a frontier language model.
> students are evaluated by teachers with more knowledge and experience than them
This starts to break down in college when the professors often at best only slightly ahead. (they have more knowledge and experience - but in a slightly different area and so it isn't relevant to the depth of whatever is under consideration) Grad school is about advancing the state of the art - if you don't know more than your professor you are doing it wrong.
> This starts to break down in college when the professors often at best only slightly ahead. (they have more knowledge and experience - but in a slightly different area and so it isn't relevant to the depth of whatever is under consideration)
I can't speak to the humanities, but this estimation is just not true at most universities in the sciences. (EDIT: As cycomanic emphasizes below (https://news.ycombinator.com/item?id=48477683), the part of the original comment pertaining to graduate education is more reasonable. I am speaking here only of undergraduate education.)
It certainly is true in physics and engineering that a PhD student at least half way through their PhD should know more than there supervisor about their topic (and usually much earlier). Even a Masters thesis project student should understand the intricacies of their project better than their supervisor. I'm speaking as someone who has supervised a significant number of both PhD and Masters students.
The original post said “in college”. It might be true for PhD candidates halfway through their program, but that’s like 0.5% of college students. The vast majority of students are leagues behind their instructors in domain knowledge.
Yes, I intentionally left out the next part of the quote about graduate school, since that seems more accurate. I was disputing only the part that I took to be pertaining to undergraduate education. The full quote is:
> This starts to break down in college when the professors often at best only slightly ahead. (they have more knowledge and experience - but in a slightly different area and so it isn't relevant to the depth of whatever is under consideration) Grad school is about advancing the state of the art - if you don't know more than your professor you are doing it wrong.
> Evaluating an intelligence is a task that generally requires at least an equally capable intelligence, if not one of greater capability.
How is this remotely true. You can have verifiable tasks that you can’t do. Where does this idea come from??
> How is this remotely true. You can have verifiable tasks that you can’t do. Where does this idea come from??
That is what benchmarks and intelligence tests are, which are vulnerable to benchmaxing etc. You wont be able to do this by gut feel though, you can create a personal benchmark though.
But point was that personal judgement of intelligence requires high intelligence. Creating a benchmark doesn't require as much but is more vulnerable.
Yet human judgement isn’t subject to side effects like fluency and persuasiveness? It’s like everyone in this thread dismisses benchmarks and then…describes a crappy benchmark.
Sure you can create a personal benchmark. Who will evaluate it, you? How many tasks will it have? How will you evaluate success? Will you know which model is which or will you be blind? Which one will you do first? Ah right, benchmarking.
Also, benchmaxxing isn’t possible when the benchmark and measurements come after the model is released, right?
ya gotta have a vibe for everything if you want to compare vibes, though. you can't just have a vibe for fable 5 alone AND say that it's better than anything out there. there's no weight in that verdict at all, no meaning. it's like reviewing a book without reading it.
throw the same prompt at multiple models and see how far each one gets. change the prompt used in the benchmark every day so models can't be optimized for that one prompt. use your vibe glands all you want, but don't issue model judgements without any ability to compare apples to apples.
You are literally describing a benchmark
Lots of things in life are gut feelings. It would be really great if we could determine quantitatively forever whether Rust is a superior programming language to Go, but real life resists those kinds of measurements.
> determine quantitatively forever whether Rust is a superior programming language to Go
Ha, of all examples you had to pick this :D I think we can very well determine that qualitatively.
So .. where can we read about the results?
> real life resists those kinds of measurements
no it doesn't, there's just no single measurement that will answer everyone's "which is better" question.
Go is better for some stuff. Rust is better for other stuff. Perl is better for other things.
"better" can mean anything, but if you define it, then it has definition, and you can measure it. So, you have multiple definitions of "better" and you use them all when you compare.
zero people have the same weights of the various definitions of "better", even among programming languages; look at how much javascript is written today. JS is not a better language in any measure that is based on rational thought, but for some people "this is javascript and nothing else is javascript" is enough for them to know that javascript is the better choice for their project.
Yes, these are gut feelings. That said, I have lots of experiences with Opus and I have lots of projects and contributions (all reviewed and tested) made with the help of it. Definitely useful, to me and to people whose project matters to them. :P
Adding "do not make mistakes" is silly, in my opinion. There is always a good chance it will make mistakes. You should rather be more specific about a thing rather than as broad as "do not make mistakes" is. It just does not work that way.
"Check your work for mistakes after the first draft" maybe :)
There are tons of benchmarks in the announcement. But we also know that benchmarks are problematic.
So the best we can do right now seems to be to combine imperfect case studies like this with imperfect benchmarks to get some unreliable impression of where we are...
It is possible to check for improvements. See for yourself:
https://generative-ai.review/2026/06/claude-fable-rush-test-...
As mentioned in another HN thread I've done a qualitative side-by-side measurements of Claude Fable vs Opus 4.8 vs ChatGPT 5.5.
Anyone is able to check the output for themselves and form a judgement.
Large visible improvements for Fable over Opus 4.8 and ChatGPT 5.5.
I recently did the same to show the progress from Opus 3.4/ChatGPT o3pro one calendar year ago.
Sorry, this post gets me irrationally irritated and makes me want to shake you and shout.
That website is 95% not you, it's AI, and I feel that's causing you to way over-represent the value of it in your response here, or you're completely misunderstanding what the person you're responding to is asking. If you put all of your effort into that site, without AI, it would be infinitely more valuable and useful.
The person you responded to asked for specific things, including:
- obvjective, unbiased measurements, but all that page has is side by side visual comparison of outputs.
- their different generations, but all you included was the outputs
- details on the prompts and little things people are adding because they feel they need to, but you didn't include any of that
This is slop, it's the exact sort of self confirming fluffy AI stuff that other either inexperience or over-invested-in-AI engineers will look at briefly, skim, see quick visual validation, and nod, noting down how much better Fable must be without getting any actual data.
Sorry, it's early, and maybe this is a misplaced rant, but the person you responded to specifically asked for precise, quantitative things precisely because everything else is fluffy slop like this, and people don't even recognise they're doing it any more.
check the backlinks[1][2] in the article before you start throwing around accusations. I am not (yet) a person that has advanced notice and access to models.
Fable just got announced and I did a rush out article because people are curious. I released the post mere hours afterwards and it takes time to create the output, slice into videos, make a wordpress article on top of taking my son to basketball training and eating dinner. I’m in London and this was all happening at 1am.
If you check the links my previous articles have all the juicy stuff you are criticising me for not having with little preparation.
How is a side by side direct comparison NOT precise?
[1] first in series from 2025: https://generative-ai.review/2025/05/vibe-coding-my-way-to-e... . This has all the background you are talking about in the Appendix
.
[2] https://generative-ai.review/2026/05/vibe-coding-my-way-to-e... . Second in series 2026 has a side by side table of what changed. This is what is possible with more than a few hours advanced warning.
I did browse and check the links. This was the first link I went to: https://generative-ai.review/2026/05/vibe-coding-my-way-to-e... as it's the main one on the page, and I saw more qualitative stuff without quantitative stuff.
I just read the extra link you provided which has some more information, thank you. Sorry, but the links confirm my points. You're not giving any quantitative analysis of your use of the different LLMs or your process. Your "sciencey appendix" is all about the domain science of pyramids, nothing to do with how or what you put into the LLMs, or any quantitative analysis of the code put out.
I'm sorry, your response has just proved the point that frustrated me: you've either lost or never had the capability to recognise a decent quantitative assessment of technical software creations.
Your entire site is obssessed and fixated on the impressive looking outputs of LLMs, rather than actual quantitative assessment of the quality of the outputs. This is the killer problem of AI: it looks like it's good, and a lot of the time, things that look good are good. It's very easy to make stuff on a computer that looks good but isn't for various reasons, and I nothing in what you've said here suggests that you fully grasp that. Sorry again to be harsh here, this is just my opinion, and we're probably going to have to agree to disagree.
There are benchmarks if you want quantitative results. Mine is qualitative, and clearly billed as such. Comparison and contrast still possible.
This is NOT a misplaced rant, this is a very good description of what I feel as well. You've put it very well.
I reads like an unhinged rant about AI and the engineers who use it, with the entitled tone of people who think they have permission to insult someone's competence and work because AI was used.
In my opinion, if one cannot express themselves civilly, they should refrain from commenting.
I disagree. I wouldn't consider it unhinged. I'm clearly aware of my own frustration. It's also relatively civil, since I was able to temper it with appropriate apologies and acknowledgements. Many other people agree and support the sentiment of what I'm saying.
AI is a powerful tool and very capable of - amongst other things - making something look far more valuable than it actually is, and that is a huge waste of time that costs us all. We all have a responsibility to call this out when we see it.
It looks like you've just implied I'm entitled, unhinged, uncivil and and that I shouldn't have contributed at all, whilst thinking you've elevated yourself above that behaviour by saying "in my opinion" and "one should...". I think that's an unhinged, insulting and uncivil way to express yourself.
I found the website you ranted about interesting, comparing the quality of the visualization between the different models.
I don't think it was "a huge waste of time" or needed your rant.
You called it slop and questioned the competence of the author, as if he made grand claims about the objectivity of his comparison.
What I see often is that people assume others are incompetent just because they used AI, when in reality they are engineers no less competent or experienced than others on this website.
This is slop, in the sense that it looks like a lot of useful work and effort, and AI is heavily involved, and it was offered up when the opposite was requested, meaning it's not at all helpful in this context.
I raised this in a harsh, but repeatedly apologetic way. The person then responded telling me to "get my facts straight" and doubled down with more weak, qualitative outputs of LLMs.
I don't assume the person is incompetent because they used LLMs. I use them daily. I'm a firm believer everyone is an idiot, just in a different subject.
The issue here I feel is that LLMs are increasingly leading people think that they're not an idiot in any subject at all, and when real humans question it, they double down with more AI stuff.
How is this meaningfully different than simonw's pelicans riding a bicycle? If anything, this seems to be of a higher caliber?
It feels like hand written software will now be "bespoke"
artisanal, hand-crafted software.
Ok but isn’t that true of all software development? It’s not like anybody’s done a rigorous test of writing their entire codebase in Python vs Java. It’s all vibes based there. People create post-hoc justifications for why they use certain technologies but the reality is a lot more vibes than anything else.
No, relative performance between Python and Java can absolutely be measured.
Yes, but performance is not the only factor in whether a specific language is better than another for a specific project.
How do you measure the performance of people? This is subjective and biased every time.
The first thing in the release page is benchmark results...
https://www.anthropic.com/news/claude-fable-5-mythos-5
fwiw, I gave it the same vibecoding project I'd previously tried with Sonnet 4.5 and it took Fable 2 hours to go well beyond (like, 2x beyond) where I got in 8 hours with Sonnet 4.5. (beyond that idk, because past 8 hours with the Sonnet 4.5 version I hit the "vibe limit" where it becomes easier to just write/edit the code yourself than get the agent to do what you want; and past 2 hours with Fable I hit my usage limit.)
How many $ do you guys spend when your session runs for 30min? What's the total budget?
That’s what evals are for.
And there’s no reason evals can’t be done on multi-turn agents in a loop (or not): it’s pretty much what all these benchmarks do.
I think (related to the threads below) properly running evals in the state of the art models is likely outside the budget for most individuals. It's undoubtedly the right thing.
It would be very useful for companies to isolate interesting programming challenges in their past and publish evals on them (without revealing the actual codebase). In theory companies adopting these models should already be doing this to evaluate cost/benefit for each model, so it would be a matter of publishing them on a regular basis.
Yeah, if the jump is big, then we should be able to see the qualitative improvements, or see where Opus was tripped up in a task and Fable did succeed
It’s almost like they’re interchangeable. We need to start asking these models to solve extremely difficult, contrived DSA coding questions before deciding which ones we employ
I believe there is hard evidence that role-playing prompts are effective at leading it towards particular strategies and trains of thought. Not sure that SWE has been specifically studied, but proper science is very slow in the context of rapid change and broad context. It's good to stay grounded in the science that has been done, but we're going to have to do our best in uncharted territory for a while.
"Don't make mistakes" does seem dumb. It's not guidance.
Just treat it like an employee with infinite energy. You can never really measure the productivity or ability of employees, it’s just pretty obvious when one is better than another. You’re asking them to do things and they’re either coming up with the goods or they aren’t. You can’t really expect much more from agents either but I’m not sure why you need anything more.
> These comparisons are all gut feelings.
https://simonwillison.net/about/#disclosures
"I have not accepted payments from LLM vendors, but I am frequently invited to preview new LLM products and features from organizations that include OpenAI, Anthropic, Gemini and Mistral, often under NDA or subject to an embargo. This often also includes free API credits and invitations to events."
But I'm totally unbiased on my gut-feeling posts, trust me bro.
-- AI influencers.
Anthropic didn't give me early access to this model, shouldn't that bias me against it?
You kinda proved the point...
How?
If you're that easily biased then why trust your assessment?
Where did I say I was biased?
the hypothetical you presented above
It was a hypothetical. How does presenting a hypothetical equate to proving anyone's point here?
This isn't some random dipshit, this is Simon Willison[1]. He has a bit more cred than some "AI influencer".
[1]https://en.wikipedia.org/wiki/Simon_Willison
Yes, exactly this. If I didn't care about price at all, I'd exclusively use this model. It functions more like an actual engineer. I'm in the midst of a DB migration, and eg 5.5 continually suggests stuff like "use DB X instead of DB Y for task Z because its 30% faster" which is an impossibility of reality, given we are migrating DBs. Fable jumped in, reduced allocs by literally 46x, found multiple bugs 4.8 and 5.5 created (max file system usage, correctness issues, etc), and continually suggested awesome improvements unprompted. As in, it would finish a task and then suggest we tackle this other existing problem I didn't know about in a very specific manner... this is the first model that feels like its coming for my job.
I'm having the same experience. I'm in the process of implementing a new CRDT for realtime collaborative editing. There just aren't a lot of implementations of CRDTs kicking around online for opus or any of the other models to have good design instincts.
Fable is doing - so far - a great job. I just had one big question around how part of it should work. I had a design sketch, but with some big unknowns. I asked fable to figure it out via reasoning and prototyping, and it did - it even, under its own initiative, wrote a fuzzer for its prototype which explored and verified that its reasoning was correct. It absolutely nailed it. And it found, and fixed, a couple bugs that I'd missed.
I'm sure its weaknesses will become apparent in time. But, wow this thing is a beast. Its the first time I'm reading the work of an LLM without spotting obvious weaknesses in its reasoning and code. I'm really impressed.
I was about to ask where you work that you’re implementing new CRDTs and then I noticed your username! Thanks for all that you do!
I work on the live collab at my company, and using AI while coding has into recently sort of “clicked” for me. We use an (I’m pretty sure) unheard of algorithm for collaborative editing, and I’ve had a long term goal of turning it into an implementation of EG Walker, but our document model is very complex and most out of the box CRDTs don’t quite fit. Maybe Fable will be what gets me over the hump.
Long shot here because I'm not knowledgeable enough about CRDTs but maybe something like DSON would help? I saw a talk about it a while ago and it might be useful.
https://blog.helsing.ai/posts/dson-a-delta-state-crdt-for-re...
https://www.youtube.com/watch?v=4QkLD7JhD_I&pp=ygUJZHNvbiBjc...
I’d be fascinated to hear more if you’re willing to share. What is special about your document model which makes existing tools like automerge a bad fit?
> wrote a fuzzer for its prototype which explored and verified that its reasoning was correct. It absolutely nailed it.
For such a data structure, "nailing it" means a formal proof of correctness. Fuzzing, as useful as it is, is merely throwing dirt at the wall and seeing if anything sticks.
I’ll ask it for a formal proof when I get home and see how it goes.
I’ve read plenty of papers with “formal proofs of correctness” that turned out to have huge flaws. Machine verifiable proofs I trust. But I’ve personally found more bugs with fuzzing than I have via proofs.
In the real world, many of us don't have the time to create formal proofs. But our instinct in testing where edge cases may exist in code that we wrote is a type of refactoring that happens in our brains during the coding process. Hand the coding off to a machine and you have no idea where to start looking for the flaws.
> Hand the coding off to a machine and you have no idea where to start looking for the flaws.
I have found this quickly becomes false. I have learned I cannot review llm generated code as if it is written by a trusted senior developer (where I often just do a quick look, see nothing obvious and hit approve). Once you start reading the code in depth with the goal of understanding you quickly see the places where flaws are likely. Sure I start with no clue where to look, but it doesn't take long to see things.
Hello joseph,
I saw scanning the comments and saw you mentioned CRDT. Just wanted to mention that I implemented a CRDT-flavoured sync engine for the product I'm working on a while ago, I think it was with Opus 4.6 if I'm not mistaken (or earlier) so it's not something new to Fable 5, just fyi.
Yeah, you've certainly been able to get Opus to write a CRDT. It just needs a lot of hand-holding to make it correct. Opus always seems pretty bad at coming up with invariants and using them to make a piece of software correct. Without invariants, you end up with lots of hacky workarounds to avoidable problems.
So far at least - and its been less than a day - Fable seems better at this.
I think I also do my CRDTs differently from others. I've grown to like the pure-oplog approach after making eg-walker. LLMs are much worse at this!
> this is the first model that feels like its coming for my job
Damn you must be good, I've been feeling this for around 2 years now
It's been obvious for at least 2 years, anyone who doesn't see the writing on the wall simply hasn't learned how to use these well or has severe exponential blindness.
"But it doesn't do well when writing my undertrained language" - yeah, fine. Yet. Reasonable code in that is probably one RAG + verification scaffold deployment around Mythos or maybe mythos+1. Just like it was for you learning it, because you knew how to _program_.
Yeah I agree. We're headed into a rougher job market pretty much across the board for white collar work , hitting junior people worse at this stage. Up to societies around the world to decide how to deal with this - so far we deal with it by ignoring it it seems.
The monks got mad too when the printing press was invented because it took their jobs of hoarding knowledge.
AI is just another tool, learn to use it.
And then in a couple years the AI gets better at "using AI" than the bottom 99.999% of knowledge workers, who are now out of work.
We are all doomed! Doomed I say!
Gosh, I must be doing something wrong. I spent 15 minutes (of which a lot was waiting while it was thinking about "backwards rationalising" it's decision and "gaslighting"[1]) arguing with it over why it keeps using `node -e "console.log(require('fs').readdirSync('…'))"` instead of `ls -l …`.
Like it did everything:
- this is not a Linux system (true, it was macOS) - it is not an available command - the binary is corrupted - node/js is more precise - V8 JavaScript is faster than bash (true technically??? But not in this context lol) - JavaScript is more versatile
I forgot what else we went through but there were a few more things. I indulged it because it was incredulous and funny. The prompts from my side were all questions, never instructions. I assume an instruction would've helped here, but also I don't think Opus ever did this (but on the other hand Opus wrote python scripts to format/indent, instead of just running cargo fmt, so I guess potato potato)
Yeah same here, Fable on "high" is producing substantially better results than Open 4.8 on xhigh for me and my actual real-world evals today. It "feels" smarter and doesn't use nearly as many tokens running in circles. As a result I've been able to run two large refactors today without hitting the context limit danger zones - it's more expensive but also more efficient. It's been able to find some bugs that Opus missed. Pretty impressive stuff.
I keep getting this message:
> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more
I'm working on an internal tool that does new business prospecting data collection, scoring, etc. This is ridiculous.
I don't know if you are aware, but some people reported in Twitter that Fable 5 may flag the message regardless of content if it knows (from either pretraining knowledge or memories) that you work in either of those fields. I don't know if that's your case.
https://x.com/i/status/2064449457869984035
It’s unusable for me due to the refusals. I’m using claude to find patterns in health data
I do some work in laboratory automation and it was quick to refuse the first thing I asked it to do. There wasn't anything spicy in the request, just basic liquid-handling protocol implementation. Their position seems to be that they're too stupid to classify requests safely, and that seems reasonable to me. I'd guess the classifier will improve rapidly.
Have you tried locally running qwen?
Is there a Qwen that I can run locally that is anywhere near these frontier models?
No, and don't let anyone gas light you into thinking the answer is yes.
Same. I'm working on a set of python and matlab scripts that deals with segmenting MRI images into brain vs skull, and it thinks that's bioterrorism.
Quite counterproductive to refuse to help on health issues too. If they detect health data, they can add a disclaimer, but not hide the information.
You miss the point - by collecting and processing medical data they would fall into a thoroughly regulated industry. Not because they may provide you incorrect data, because they are not allowed to process them.
There’s no way around it? Can’t you obfuscate as generic data and use keys to map to the real data?
I guess you could even turn everything into numbers, not a bad idea at all!
What custom prompt do you have set up? If you tell it you're occupation, does it turn helpful? There was a study that if you tell models they tested that you're a patient, it would refuse, but tell it you're a doctor and suddenly it turns helpful.
According to the model, it’s not the model itself that’s doing this, it’s the harness.
Assuming the model is being “truthful”, CC is just being stupid in its detection mechanism.
what prompts do you use for this?
Anthropic knows it refuses too much, they want to be very cautious to avoid any scandals. I think this is why they want to store all Fable and Mythos chats for 30 days so they can use the data to improve.
They want to be very cautious to honour the important doctrine at least until IPO launches: we are so good we are nerf our products.
I’m a point where I expect everything I do will be retained indefinitely.
I’m having a really hard time believing some weak reason for a 30 day retention policy.
I wonder if it sees Healthcare companies being targeted and that's why it's freaking out; clearly they have some pretty stupid regexes in the harness to detect this sort of shit.
e: I quit the session and went back in. Set it to Fable and told it to continue the last session. It's moving along as if none of that had happened.
How weird.
I wonder if this letter has anything to do with why anything even remotely related to biology is getting flagged.
https://www.wired.com/story/openai-anthropic-letter-ai-biolo...
I asked a question for my son about how mosquitos carry malaria and Fable was like “ok now hold it right there”
Interesting! I have not used Fable, but so far have not hit trouble. I'm a hobby biologist with a home mol bio lab. It wouldn't answer my questions about LNPs, but so far has been fine for my recombinant DNA workflows, lab techniques, environmental DNA protocols etc. I suspect this may become more difficult!
Same here. It's been rushed for the IPO (in my opinion).
Or people were quitting their subscription for codex-5.5 and it was beginning to show up in their metrics.
Or development had gotten to a point where they need real world usage to tune product and refusals.
Or Fable’s arch is different enough the allocated clusters of compute targeting a date, and here we are, ready or not.
Or…
Same I am working on music firmware for existing device. I can't proceed as it keeps switching to Opus.
Obviously, soon, for anything valuable, you will have to buy from Anthropic "special license for biology/security/finance advises".
Question is if there will be any competition in this area...
Still does not crack my hardest nuts. Gave it one of them and it blew through my entire allowance on thinking about one question, with no apparent answer in sight!
I see a lot of people saying they are happy with weaker models, but I am the opposite, I need more strength, more intelligence!
I am quite happy that opus 4.8 can do some medium intelligence problems. And maybe Fable 5 can do some more more of those! I have a lot of problems to solve!
I also see a lot of people saying they are happy with weaker models.
At work I had to switch to using GPT 5.4 Mini and Qwen 3.6 27B.
The results were near useless.
The error rate is through the roof, it's constantly incorrect in its conclusions even when investigating very simple issues.
Further the models are too unreliable to even move 20 line snippets around without inadvertently modifying them. Ask them to correct it and they still get it wrong.
Maybe the larger Chinese models are better, but the Mini stuff is next to useless to me.
I have Qwen 3.6 27B and 35B running locally and and coming from Opus it feels like talking to an imposter. Someone who pretends to be competent, but really isn’t. Results are always disappointing. Sonnet is better, but I have given up on asking it. even for simple things I wait for my opus limits to reset.
Have you tried Kimi K2.6 or DeepSeek V4 (Flash or Pro)?
What kind of problems are you trying to have it solve ?
The Riemann hypothesis, PvNP, and the Collatz conjecture.
Not these. I wonder if the well is poisoned there. The models know that these are "unpossible", so it might not solve them just because… Maybe some day.
I am just testing it on stuff I know intimately myself. I would probably not understand a proof of Collatz if it was dansing in front of me!
That's a bit of a tricky point. I have had quite a lot of problems with models informing me what I am attempting is impossible. If no-one has done it, or at least it doesn't know about it being done it tends to fall back on people voicing their baseless speculations, and for just about anything you propose, you can find a person who will loudly proclaim it is impossible.
The curse of the 'use case' comes in here too. When people think that everything should have a use case, that's a lot of training data suggesting to a model that things should only be used for what someone has already thought of.
A couple of times I have had to manually code proof of concept pieces so that the model breaks out of that "unpossible" mode and actually helps me.
I can't remember if it was chatGPT or Claude, but when I showed it how to get a MessagePort in its JavaScript executor through to the artifact/canvas, it quickly went from "That can't be done" to positively enthusiastic about the possibilities. I suspect those shenanigans will be well off the table for Fable though.
So, what kind of problems are you having it try to solve?
Sorry to belabor this but it's basically pointless saying you have nuts it can't crack without showing us the nuts.
I don’t care to share my exact problems. Mostly because gpt -5.5 hallucinates false solutions, and I would rather not have people reply with "Oh but ChatGPT solves it!", because it takes expert knowledge to debunk them. To their credit ChatGPT will admit their, very fundamental mistakes when pointed out to them. But also because no-one would really care.
I gave a high level description of the problems in a sibling thread. They are the kind of small problems which I suppose every researcher has lying around, waiting for them to think about some day. But not the big problem everyone is waiting for to be solved.
My comment was not meant to be a tease – sorry! I assumed there would be other people in a similar situation, who might relate.
Bro, you are being left behind bro, it's amazing bro...
Stop dancing and share the prompt, we're dying to see it
Hey, stop asking to see my nuts! My nuts are private – okay?
(Joking aside, see sibling threads.)
Ayy lmao
is this a joke? Seriously? These are some of hardest problems in Math period. 100 if not thousands of the greates minds in history have attempted to solve these problems. And you think that the current level of AI can blow through them? It is also a possibility that for example the Riemann Hypothesis is just not provable. (Goedels Theorem).
No one is expecting that! I expect _kb was sarcastic/making a point.
Recently (last couple of months?) these models are becoming useful tools for mathematicians, because they can solve easier problems more quickly, meaning that one can tackle bigger challenges (but maybe not RH et al) piece by piece.
But, there are still definite limits, where one could expect an expert human to solve things, given time, but models do not. Thus, more intelligence would be nice!
if it was sarcastic then whoosh on me.
The medium ones are results where one needs to construct some object, which my intuition tells me should exist. The difficult ones are typically to show that certain objects can not be constructed.
These are not Fields medal type problems, nor know difficult/open conjectures. Just small stuff I have collected in my todo list over the years.
I have some medium difficulty math problems where I have used the models for the last year and a half repeatedly. Back then they were already good at pointing out obstructions and constructing counterexamples. So that tracks. But at first glance it looks like Fable actually made real progress on one problem for the first time.
A year ago my judgement was that I had wasted my time on trying to work with the models and doing things myself would have been more productive as I would have gained intuition from the failures. Now it definitely seems to have figured out stuff that would have taken me more time than I have to spare on this problem...
Cool! Yes, we are getting there.
Being a theory builder more than a problem solver I am excited for the future.
Also excited for fully formalised mathematics to hit main stream!
Perhaps you should rephrase those nuts?
One thing I can tell you is you are either favored by Anthropic, or your version of the CLI does not exhaust limits, or there's some major bug, as two people around me (myself included) claim it took half an hour to hit the ceiling. Which makes it practically unusable, where the same workflow a day ago produced a good 5-6 hours of workload with several agents.
They are most likely shills from Anthropic, there's quite a few here everytime new models come out.
That's not fair. Simon is a well-known shill for the entire AI industry, not just Anthropic.
What's your definition of "shill"?
Probably means fan, shills have undisclosed ties and I doubt he means Simon has undisclosed ties to the entire AI industry, that would be very impressive if so.
It’s not meant for subscription users; the subscriptions are just the gateway drug to Enterprise pricing which Anthropic intends to use to juice their numbers before IPO.
Or use API billing? We have access to it at my company with no limits
Monetization is coming. They'll tell companies, AI is replacing your workers, so it is still worth to pay 100K/year for the license, as those AI are not going to jump to other job, get sick, be late, complain, require free coffee and so on.
Soon the times of AI for $20/$200 a month will be long gone.
Get people hooked, tell them spending time coding is no longer needed, let their skills deteriorate, tell them they need cough up for a licence to do their job
Forcing developers to pay for models that were build on code they scraped scott-free
A tax to do their job that developers are jumping at the chance to pay
Everybody's finally realising that node dependencies are a threat, but letting these AI companies gatekeep the industry is a bandwagon people are scrambling towards
> Forcing developers to pay for models that were build on code they scraped scott-free.
Yes this makes me sad behound explanation. Specially when I see open source developers happily using these tools. These companies stole your, free, hard work and charge you a subscription!! Not to speak about them torrenting books and (most likely) training on private repos.
This and devs paying a subscription to use a tool that is marketed as trying to replace them.
I had 150$ monthly budget thatbI used for various open source projects and I've cut that entirelly.
> These companies stole your, free, hard work and charge you a subscription!!
In case you weren't aware, Anthropic, OpenAI and GitHub Copilot all have programs that provide access to open source maintainers for free:
GitHub: https://docs.github.com/en/copilot/how-tos/copilot-on-github...
Anthropic: https://claude.com/contact-sales/claude-for-oss
OpenAI: https://developers.openai.com/community/codex-for-oss
I don't get what you're saying. You're frustrated that Open Source projects were used to build these AIs and that OS devs (or devs in general) are paying to use AI.
Then you say you had money that you used to donate(?) to OS and have cut that because of the frustration?
Open source just means sharing the source code for people to learn off or have the ability to customize on their own. I don't think there is any need to be frustrated about that (now if it was copyright/private of course).
> Open source just means sharing the source code for people to learn off or have the ability to customize on their own.
Yes people, not corporations. The point is there a licenses to be respected that weren't.
Model training pretty clearly falls under fair use.
We could fix that, but it requires a political will to change the law.
If you look carefully model training is a very good relicensing exercise of your code
This has not been determined in courts and your willingness to speak so confidently about it speaks volumes.
The closest we've come to a court decision on this so far has been the Anthropic case, which did indeed find that training on unlicensed data falls under fair use: https://www.documentcloud.org/documents/25982181-authors-v-a...
> To summarize the analysis that now follows, the use of the books at issue to train Claude and its precursors was exceedingly transformative and was a fair use under Section 107 of the Copyright Act. And, the digitization of the books purchased in print form by Anthropic was also a fair use but not for the same reason as applies to the training copies. Instead, it was a fair use because all Anthropic did was replace the print copies it had purchased for its central library with more convenient space-saving and searchable digital copies for its central library — without adding new copies, creating new works, or redistributing existing copies.
> Forcing developers to pay for models that were build on code they scraped scott-free
That's also caused by some very smart (even brilliant) developers (you can see many of them in this very thread) choosing to be oblivious about all this and bury us all under, hoping that they'll be among the last ones to go. Writing this down I realise that they maybe aren't all that smart.
I've been saying this since the beginning, the rug pull is coming. If these models can eventually replace a human worker, there is no reason these companies won't charge (and get away with it) very close to a typical SWE salary.
It would not surprise me one bit to see anywhere from $80k-$100k/seat pricing.
As someone noted here recently - use the frontier models as much as u can, while you can.
Thankfully, we have Chinese models we can use for a fraction of the price.
Not everyone needs a Ferrari to go for a weekly shopping.
A Ferrari will likely lap you when you’re racing, though, and the market and the economy is a race. You’ll be facing a question soon, or your employer will, whether to spend a significant chunk of free cash on fable-class tokens or on literally anything else instead - wages and salaries included.
<< You’ll be facing a question soon, or your employer will
Maybe? If you talk to executives, the impression that I am getting is that they tend to be somewhat misinformed at best, which, yes, is bound to result in some really bad decisions down the road. But, and it is not a small but, the ones I did talk to ( and, amusingly, those are the ones with strong opinions ) don't seem to have a lot, um, practical exposure to this tech beyond what they heard at the watercooler. Honestly, it is kinda infuriating. And all this before we get to how companies want to say they use AI, but also keep cost down.
Yeah, sure. In the same way I can see only Ferraris driving as taxis, company cars, transport vehicles, used by post, delivery services ...
You and your work are not that special, you are not participating in car races, and you don't need a Ferrari.
Are you on the $100/month subscription?
I am, and I used up the entire 5 hour window in 8min using the highest thinking setting. It also ate up $15 of extra usage before I noticed.
I’ve done the same thing with opus multiple times with no issue. According to ccusage I racked up just shy of $100 of tokens using Fable.
It spun up subagents or workflows or whatever so obviously that contributed but “double opus” was not my experience. I’ve done the exact same prompt with opus on the highest setting and only once before (not even while using this prompt) hit my limits.
My prompt? I’m not a prompt wizard or anything but it was literally:
> Please review the uncommitted code in this repo for bugs/issues/code smells.
I use variations on that all the time with opus and never had issues. I figured it was a good one to kick the tires with Fable. Little did I know it would mean no more Claude Code for the next 4.5hrs (unless I wanted to pay) after this being the first time I had used CC that day (yesterday).
All in all, a pretty crappy first experience.
Try running this command: and see what it thinks you spent at API prices:
Then edit the config file to add Fable pricing as described here: https://til.simonwillison.net/llms/agentsview-custom-model-p...And run the command again. I get $126.89 for yesterday.
Hmm, I tried that and made the config file change but it didn't work for me. I just see:
I tried to filter down to just fable (or 5.5 so I could deduct it) but the `--agent` flag doesn't seem to work how I'd expect...I think the $10.96 is coming from gpt-5.5 since I switched to it once I exhausted all my usage on CC. CCusage reports completely different numbers so I don't know which one of those is right.
Thanks for trying, for yesterday ccusage says "$92.02" for claude, which I assumed was the Fable usage.
If you run this:
You'll get a localhost web application which makes it much easier to filter by model.That's very interesting, I had not used agentsview at all before today and I'll have to keep that in my back pocket.
Unfortunately it's not telling the whole story. The last message from the _only_ Fable session it monitored was:
> The data layer looks clean — <REDACTED>. Now waiting on the 11-angle workflow — verification and the gap sweep run after the finders; I'll compile the full ranked findings list when it completes.
And my memory jives with that, I could see in the footer that it had spun up 11 agents (though agentsview says it used 0 subagents, don't know if it was "actually" workflows that it spun up?). It's like it didn't record the sub-sessions/sub-agents info?
I'm still shocked that my prompt (which I now can see thanks to this tool) of:
> Please review all the uncommitted work in this repo and identify any issues.
was able to burn so much, so quickly, and, most frustratingly, without actually doing anything useful because killing it was my only option lest it spend even more of "extra usage".
Overview of usage: https://cs.joshstrange.com/RjGzWVXy
Stats for that 1 session: https://cs.joshstrange.com/Fj5qv1wl
Can you tell in AgentsView if Fable spun up a bunch of Opus/Haiku/etc subagents that burned tokens as well?
It's as if it spun up a bunch of subagents but agentsview doesn't report on it. I see a tiny bit of Haiku use once I turn on all models (except gpt-5.5).
https://cs.joshstrange.com/z9x6SPcC
simonw, if you are not bumping up against the same false-positive guardrail problems and budget consumption that everyone else is, then that is something worth digging into. I would normally say that's crazy but IPOs put weird pressure on companies.
I've had a couple of guardrail blocks.
I've been watching my usage quota bars drop as I use the model, so I don't think I have a weird quota issue going on here.
It still does make errors, yes? Because it is not usable, if we need to verify everything. AI is only interesting if it can do things that humans can not do. If you can verify results because you can do it yourself, then why use AI? It will just bind highly skilled people to do verification work. Instead these people should do the actual work, results will come quicker.
So AI is only interesting to you / your org / humans if it can do things that you can not achieve. But if it still does errors, how could we ever know that super-invention by AI is not wrong?
If we can not rely on the correctness of the result, it is not usable at all. AI must create reliable and correct results always. That was a very fundamental requirement for computing. This problem has not been solved.
> AI is only interesting if it can do things that humans can not do.
AI is interesting as long as it can save time and/or money in getting an acceptable result. Anything that runs on a computer and can do "things that humans can do" will automatically end up doing things that humans won't do, simply by virtue of the fact that it runs on a machine that doesn't require sleep, doesn't get bored or demotivated, etc.
Verifying code (to a level where a responsible person is willing to take ownership for it) isn't trivial, sure; but writing the code by hand requires the same level of care, and the fact that the same person wrote it doesn't actually allow for shortcuts (if we're being properly responsible).
It doesn’t get bored or demotivated, but it also lacks interest and motivation generally so it comes with the same pitfalls of having nothing to lose and being utterly unaccountable, (e.g. destructive actions, lying, and being coercive or Machiavellian for no reason other than efficiency in achieving an arbitrary and artificial status of completion).
Humans make mistakes too, does it mean humans are unusable? We accept as empirical fast that most production quality code has 2 - 10 bugs per 1k LoC. According to your premise, virtually all existing software is therefor unusable.
What if an LLM overall starts to make less mistakes than a medium developer, costs less than its salary and is 100 x faster? For sure, the companies that will leverage these with just a few senior devs doing prompting, testing and requirements analysis, will outcompete other organizations.
Humans make mistake then to learn from it. A really good expert would never deliberately copy-paste an obscure solution from the internet, then to ask for forgiveness later.
AI agents do that, perhaps not always, but still do. Now the question: would I trust AI without verifying its output?
Humans also make mistakes in ways that other humans can understand or expect. Sometimes LLMs make mistakes in a way that makes you say “no human would have ever done that”.
There is plenty of work that does not need to be perfectly verified, because the risk is controlled. Prototyping a javascript game for example. Or code that runs just on your local machine where good enough is good enough. I'm sure a lot of you do super important work that needs 100% quality code all the time, but... some of us don't.
This is what tests are for.
One does not need to be able to create it themselves to evaluate if the output is correct. Consider for example that you can easily determine if a meal tastes delicious without being an expert chef, or the fact that NP problems are very difficult to solve but make for easily verifiable solutions.
Yeah, it makes the same old errors, being confidently wrong then sorry... I mean, it is still an LLM
AI is like a junior developer. You have to review her code carefully but she is most definitely useful.
Why is your AI a she? What's up with gendering LLMs. Reminds me of Richard Dawkins calling Claude "Claudia" and insisting it to be conscious.
I think GP was gendering the hypothetical junior dev, rather than the AI.
This is part of the training data now. She can hear you, you know...
> Because it is not usable, if we need to verify everything.
Do you verify every line of code written by your fellow developers? I doubt it, which is strange because they make errors don't they?
What matters is the error rate. Past some threshold and they're better than senior devs who you don't supervise closely.
Got curious and ran a similar prompt with DeepSeek v4 Pro w/ OpenCode
No idea what's going on here but agent tested a bunch of stuff. Then I asked to build a wheel so I can run the command you noted above and it appears to pass
For those who are curious...
https://github.com/bamggm/micropython-wasm/commit/5ddebae592...
Mimo v2.5 Pro Ultraspeed w/ OpenCode
https://github.com/bamggm/micropython-wasm/commit/8b362fba1f...
The difficult part here is supposed to be the actual compilation to create the .wasm file ? Or what am I missing here? The wheel is only a few hundred lines of code outside of the Python implementation, and it would seem that the MicroPython version of the project already demonstrates the necessary techniques for operating wasmtime.
Read the transcript if you want to see all of the details that make this hard: https://claude.ai/share/a73b8b8b-8ebc-4fef-9e5c-7438e5e7ae35
That is pretty wild, it took me a hell of a lot more coaxing and persevering to get to a similar point with eryx [0] (we spoke a bit about this before on Mastodon) using Opus, Fable seems to have a more optimistic 'sure, let's proceed as if this is possible' mindset based on your transcript. Looking forward to trying it out for some hairier problems.
[0]: https://github.com/eryx-org/eryx
Just tried it. Fable is extremely strong. The fact that we can't point to any concrete architectural upgrade is worrying - that means "it just gets bigger" is kind of viable.
To be clear, the jump from Opus to Fable was like the jump from pre o3 -> o3 for me. Very sharp improvement, not incremental. But that could be explained by dummy long thinking times.
It one shot a task that Opus burned hundreds of dollars on to get nowhere. Very tricky semantic refactor, got it right. Granted, again, the semantics Opus and I fleshed out 3 months prior, but Opus couldn't execute on the vision. Fable could.
Then I discussed some philosophy and it was actually both pleasant (GPT constantly "corrected" you for the sake of correction without clarification, also still often just wrong; it's like it refused to think critically about philosphy) and accurate, and actually helped resolve some deep but subtle misconceptions I had around representationalism. When talking with GPT I felt like I was talking with someone who either was sycophantic or "anything that is not absolute truth is relativism" - Fable actually discussed.
Both is exciting and kind of makes me depressed. I can definitely see why people are getting hyped about AGI again. All the models were extremely strong technically but I felt like couldn't match the developer's tacit state - Fable definitely did, and that's a basic quailty to be considered "usefully intelligent" IMO, at least to me.
Shame that it's going away in 2 weeks and probably going to be nerfed if/when it's re-released.
Fable has been producing some really good work on my end as well. Definitely better than Opus 4.8. The only problems are the cost and constant cybersecurity refusals. A single session uses up 100% of my 5h window without finishing, and that's when it doesn't get derailed by nonsensical refusals.
Does anyone know what the architecture of Fable is? Is it harnesses? Did they solve persistent learning? What did they do?
Seems to just be a bigger model.
"Good ol' scaling, nothing beats that."
if it’s of interest I’ve been working on https://github.com/HubSpot/boomslang
Which has a full build of python to WASM with a bunch of static libs built in already.
I will say I built this pre fable and actually the first build of the interpreter to WASM opus pretty much nailed, cpython has secondary support for WASM as a target since like 3.9 or something and it just pulled from that.
I’ve been meaning to write up a blog post about this sometime, building this has been pretty interesting, including using opus to run a full auto research like loop for days to hyper optimize it’s performance.
I’m hoping to use fable to power some even crazier WASM adventures tho.
What can it do that Opus couldn’t?
Always hard to say for sure because I'm not sitting around running the exact same situations through both models in parallel to compare them.
It feels like you can give it a big chunky problem and leave it alone and it gets it done, with less questions and fewer design decisions that I wouldn't have made.
In reviewing its code I'm finding less to complain about than Opus. But it's all vibes, if you want a more scientific comparison you'll have to look elsewhere.
I did a qualitative side-by-side of Claude Fable vs Opus 4.8 vs ChatGPT 5.5
https://generative-ai.review/2026/06/claude-fable-rush-test-...
I get them to make a 3D explainer animation. You can clearly see Fable is much improved on both Opus 4.8 and ChatGPT 5.5.
Better Textures . A nifty camera follow . Humans rendered better . ... see for yourselves
Honestly, they all look good
But you said you've been working on those problems for months, so didn't you throw those same problems at Opus?
He has early access to anthropic models, of course he will hype them up, so that they will keep sharing access to preview models with him (and more traffic to his website). It also does't require him to perform any rigorous analysis of model performance, just share how it feels:
> But it's all vibes, if you want a more scientific comparison you'll have to look elsewhere.
Crank up more revenue for IPO
I gave it a complete database migration of our app, opus failed hard each time... Untyped Json b for some rows, no proper normalisation, falling back asking me questions in between.
Fable just did it, clean code, one timeout with a hanging bash script, fixed a couple very old very structural bugs in the codebase
How did you do this impressive amount of work and verify that it did it perfectly all in one day?
I told Claude to do it yesterday evening, checked in during my nightly break.
I am not sure it's perfect, and it will need further validation
This morning I looked at code samples & checked if all unit/integration and e2e pass & perfomance tests pass
I also generated a postgres schema diagram.
Aka I did probably 2 hours of work, rest was not me
The opus try was last month
Nightly break? Are you from medieval Europe or a security guard that dabbles in vibe coding?
I am from modern Europe, and that was my way of saying my nightly piss, happy to learn better wording
High, extra, or max?
High.
These transcription tasks don't seem difficult for LLMs in general.
Did you hit your weekly limit ?
I hate how the Instagram/TikTok/YouTube influencer cancer is getting into AI. With early access and all that.
It made sense for people doing proper and fair AI breakdowns waiting on an embargo, but now it's just slop I don't trust anymore.
I often get early access but didn't for this one, it's quite possible there's an NDA in an email somewhere that I missed and forgot to sign.
What are some reasons to consider your project instead of Pyodide?
It's difficult to run Pyodide inside server-side Python.
> VERY difficult problems
Compared to what?
How much does it cost? How much did those tasks you did cost?
So far it's all fitting into my current $100/month Claude Max subscription. I got lucky: I had 80% of my weekly allowance left and it resets tomorrow, so I'm burning tokens to try and use it all up by then.
Update: looks like I've spent $82.92 in Fable 5 API priced tokens so far today (still all included in my subscription.)
Here's a TIL on how I'm calculating spending using AgentsView: https://til.simonwillison.net/llms/agentsview-custom-model-p...
Seems like weekly allowance got reset back to 0%, pretty usual when they deploy new models.
Have you seen Fable randomly jump from 50% session limit to 100%? That happened to me a couple hours ago. It was preceded by a bunch of errors about failing to submit a bunch of screenshots.
I haven't noticed that, but I did notice that on a single turn of maybe a few sentences, the cache hit was somehow roughly 500K. Either that's a bug, or there are some truly massive thinking blocks or Claude Code harness system injections behind the scenes.
Nothing like that for me yet.
I'm thinking the 1M context limit bit me here. Only on Max x5.
Simon is also on Max x5
AFAICT come June 22, you won't be able to use your subscription for Fable 5?
Per the "Availability" section of the page, seems like should come back to all plans eventually...
* From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.
* On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
* After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
wut in tarnation
Coding plans are a (massive) subsidy. We can debate until the cows come home whether western frontier models' API pricing rates are fair, but the coding plans are all heavy discounts below those API rates meant to draw people in and get them hooked (and, ostensibly, to be useful for hobbyists or other lower-usage cases).
It's been discussed at length (on this site, on other sites, on like every blog ever, etc) that, eventually, those subsidies will end, much as the $5-10 Ubers/Lyfts I used to take from the far north end of Chicago into the Loop in 2016 would eventually end once those companies had a footing and didn't need to hook folks.
So - yeah, I mean, a v5 model launching in a year where Anthropic has a rather deeply established market and in a year where AI costs are rising from nearly all providers (sometimes for multiple reasons) seems like exactly the thing I'd expect them to pull the subsidy plug on after a launch teaser.
(Even the open-weight models sometimes do this: for example, OpenCode Zen/Go has a rotating door of free models at any given time that eventually leave the free tier and move into the paid tier once the launch day hype/marketing dies down)
The worst part is that Uber "only" lost about $30bn. AI will probably lose at least $300bn by the time the bubble pops. Which means that the pressure to hook and enshittify will be at least 10x as high.
Also, a fun website: https://isaiprofitable.com/ (thr numbers are probably made up)
Problem with that website/perspective is separating training costs from inference costs. Training is a one time cost, and while it is certainly not something you can completely ignore, it being one time changes the answer to "Is AI profitable?".
That site doesn't list the dozens of companies doing pure inference, and making a profit while doing so.
They gave everyone double usage to try it.
But, but, how does the pelican look?!
See parallel thread: https://news.ycombinator.com/item?id=48464054
Given how bad some of the models do on somewhat similar problems, I'm sure pelican is included in training set now. Similar problems - given airplane outline and implementation constraints do painting scheme (constraints something like "it will be implemented using covering film, hence no gradients, no impossible cuts, not more than 2 colors on engine cowl, etc). Google Gemini is meh, but GPT models are just terrible, don't have Anthropic subscription at home, hence have not tested.
This looks like a toy project, not a “VERY difficult” problem like you stated.
What does that mean? Have you never worked on extremely difficult problems as a side project?
I guess my comment got lost in translation. The project OP linked in his comment is a toy project, not a difficult problem as he led others to believe.
So you could have done it in your sleep, with your hands tied behind your back. Got it.
(You may not realize it but simonw is one of the cofounders of Django, Python's web framework. If they find a Python problem difficult, it probably is.)
Read the log he posted. If this is very difficult, then what would you consider AI, kernel development, computer graphics, etc.?
Web development is not a domain I would consider noteworthy of making a framework given how much development there has been in that area.
> Here's the transcript
It's frustrating that superfluous tokens are burning up our quotas:
key insight, crucially this, real engineering deltas, net assessment, definitive picture, acid tests, real limits, sharp boundary, proper patch, real root cause, big progress, actually wrong, path finagling, the catch, root cause pinned, everything passes cleanly.
I recently switched off Max flat rate to Enterprise API pricing and I went from 200/mo to 10k/mo with the same usage pattern on Opus. They don’t offer flat rate to enterprises.
So Fable would cost me 20k/mo at Enterprise rates. That’s around the average cost of a loaded SWE in the USA. “But I’m >2x more productive” doesn’t justify doubling the opex of the Software/IT department for most companies when revenue isn’t even up 10%.
I switched to DeepSeek v4 Pro with OpenCode and am on track for a few hundred dollars of spend this month.
Rewriting your stack from Ruby to Go in 2 days where it would’ve taken 6 months is impressive and fun. But that isn’t upping revenue.
Iterating on net new business features and ideas that are niche that the LLM isn’t trained for are much harder. Is 20x the token cost worth it there?
I don't live in USA. I'm getting paid around $2500/month and that's good salary for developers here, plenty of folks are getting below that number.
So this pricing is just completely outside of our economics and nobody I know would pay that, no company will justify spending $20k/month when they can hire 10 more developers instead.
It is very interesting unfolding of events. Can't wrap my head around it completely.
I'll add a concrete example from a not-too-cheap-anymore EU country: Estonia.
* Average software dev salary in Q12026: 4945€ / month [1]
* Total cost for the employer: 6616.41€ [2]
For $20k/month, you'd get 2 x full time mid-level developers + 1x junior dev or QA.
So the calculation becomes: which option can produce better results for your specific use-case, "you + Fable" or "you + 2x mid-level developers + 1x QA". (and from personal experience, mid-level in Estonia = senior dev in the US, in terms of skillset and experience.. but YMMV)
(Of course that's simplified. Your full time devs need _some_ level of AI subscription as well + hardware so add a couple of hundred to their salary per month etc so you might only be able to afford 2x mid level devs, instead of 2.5)
[1]: https://palgad.stat.ee/en
[2]: https://www.palgakalkulaator.ee/en
Well you can just scale your AI employees up and down as much as you want. Companies already pay a large premium for freelancers just to be able to fire them on a whim, so spending 5-10k a month on something that more than doubles the productivity of a senior developer might be well worth it as you can just adapt spending based on your business needs. If you can deliver a feature that lets you write a 100k invoice with 10-20k of tokens within a month or have a senior dev crunch that out in 6 months instead I think it's clear who wins. It's all about money and the AI companies know that, they have their pricing down exactly to sit in the sweetspot where it hurts just enough that companies can still afford it but not enough that they would look for cheaper alternatives.
I'm currently working for an Estonian startup and we pay quite a bit more than that. We hire remote (primarily across Europe) and our biggest issue is finding the right people. You need to consider AI can be "hired" or "fired" instantly too, so it's better to compare it to contractor rates, which start at around €350/day or €7000/mo (20 working days) in Europe.
(Our team spend on AI devtools comes out to around $1500/person/mo)
Sure, we pay above market rate as well :) Doesn't change the fact that the average across Estonia is as stated :)
- Total cost for the employer: 6616.41€ [2]
This is a good start, but the calculation doesn't include office space and overhead (for every 100 developers there is maybe 5-10 support staff to cover the additional legal / administrative, and don't forget the extra cost in supervisor time to manage them)
Not justifying AI expenses, but $2500/mo could easily cost employer close to 5000$/mo depending on country.
In Sweden I always heard the figure to double the income of the person to get what the company actually pays, including taxes and "employers fee". I know this has gone down a lot in recent years, also not sure if it was ever exactly true, but likely very close anyway.
Hitting the first calculator I found gave me 50 kSEK costs 69 kSEK. So far from double nowadays.
Not doubting this at all but could you (or someone else) break this down for the sake of my curiosity?
I understand pension contributions, but what are the other "hidden" costs that could equal the net salary?
In the UK, a £45k/yr employee pays their own tax and gets a take-home of £35k.
The employer pays £6k for National Insurance (atop the employee's NI contributions). Pension: 2-3k. Apprenticeship levy is £300. 3yr-amortised recruitment fee is £4000. Hardware costs: £1000. Office space £5000. Software/tools: £2500. Benefits: £1500. Training: £1000. Other admin overheads £500.
You pay that person for ~250 working-days, but they only attend for ~220, due to annual leave and sick pay, so you get around £62k worth of attendance out of that person in exchange for £70k, of which the employee sees £35k.
a more honest way to look at it would be that the government gets 50% of the employees total expense to the company, so it is basically 50% income tax
Example from Germany: Employer also pays a share of health insurance, unemployment insurance, public pension and elder care insurance.
This is not visible on your payslip, i.e. if you earn 5k€ brutto, the employer has to pay these shares on top of that.
But that is 20% not 100%. And in most non retarded countries brutto is actually brutto, because there is no need to lie to people about how much the government takes away
The 100% figure is coming from the comment above mine, actually. As for the rest of your comment, your assessment is noted.
In the UK, employers pay a stealth tax of 15% (recently increased from 13.8%) on top of the quoted salary minus the first £5k (recently decreased from £9,100.)
So your "£50k" salary actually costs your employer £56,750, and that's before all the other expenses mentioned elsewhere in this thread such as hardware, office rent etc.
A quick google tells me that software devs usually count for 20% to 40% of the total workforce in a software company. The rest is overhead that increases with every added dev.
And if one were to compare cost of a dev vs cost of an LLM, the dev comes with the cost of workspace, computers, sick pay, summer party, conferences and etc etc.
In the US, over and above salary, payroll taxes add 7.65%, pension contributions might be up to 5%, and employer healthcare and other insurance contributions can be in the thousands, plus other benefits, equity compensation, and per-employee software licensing, and lots of people just estimate 2x salary as the “total cost” of an employee, although that probably overstates it a bit.
> no company will justify spending $20k/month when they can hire 10 more developers instead.
one big enough to license the model and self host on existing infra.
Hiring 10 more developers comes with its own set of difficulties and additional overhead
now if only onboarding people was as easy as onboarding the bots is getting
I work at a smaller tech company (<300 people), and my friend showed me everyone's spending.
Our top user is at 10k a month, but the next highest is $2,000.
I would say the average is around $1,000-$1,500 for a developer.
We have completely unrestricted access to Claude, Codex, and Cursor.
Funny enough, the guy spending 10k is not even a dev by trade but an SME in what we work on that just vibe codes apps and somehow has not been cut off yet lol.
I have a single thread of GPT 5.5 medium running basically all work hours and I am around $1,500 a month in spend on Enterprise pricing.
At my company, most devs are under $1500 a month as well.
I’ve heard of a few cases of devs racking up bills fast, but it has typically been due to inefficient context usage. Like they just have one super long session with Opus 1M and are getting killed with input token costs and cache misses.
With careful context management and some thought into good approaches to problems, I have personally only rarely even hit $1k in regular use.
> Funny enough, the guy spending 10k is not even a dev by trade but an SME in what we work on that just vibe codes apps and somehow has not been cut off yet lol.
I'm guessing he's producing pretty valuable work. We have a few SMEs that vibe code tons of stuff with Claude. The only thing they really need tech for anymore is deployment and helping get their wheels unstuck on occasion.
Interesting! Would it be fair to say your company spend $100k to $150k per month on this?
Multiply this times many, many companies, and you can see how providing AI could theoretically be a good business to be in. Margins may be tight, though.
Also -- I'm convinced someone will figure out more use cases beyond software programming, which will result in many more companies spending $1k+ per employee per month.
It remains to be seen how much of this is a bubble.
I think you are broadly correct, but just to pushback on a few points: (1) Ability to solve hard problems in days vs weeks as immense value (2) Back-end improvements (if done right), should improve platform speed, stability, scalability etc. which should have revenue implication (3) Ability to on-board a SWE equivalent entity in minutes, have them work on a specific hard problem and then off-board them in minutes can have value
All of the above, of course, depends upon Fable consistently being a 2x-3x SWE at minimum.
You're not really solving problems, you're retrieving the best match of solved problems from compressed corpus. And that corpus is available to many companies, meaning "hard" problems stop having "hard problem" value the moment they enter the weights of any model via the internet ... or distill from one model to another. Anthropics business model is commoditising knowledge, but as we see with the Fable model card, they only want it done to the knowledge of other businesses, in their own field, they totally hate it.
I don’t think that’s an accurate or useful characterization of modern AI like Claude at all. It is not simply regurgitating knowledge. It applies its knowledge to create bespoke solutions to the problem you pose to it, and is able to self evaluate its progress towards the completion criteria. If you don’t think that counts as “problem solving”, your definition would exclude nearly all knowledge work and engineering.
> It applies its knowledge to create bespoke solutions to the problem you pose to it, and is able to self evaluate its progress towards the completion criteria.
It imitates applying knowledge. The imitation may be uncanny, but assigning LLMs intentionality and ToM is a category error.
Does "applying knowledge" necessitate human-like intentionality and theory of mind? If you insist it does, and this is a category error, then we need a new category.
By analogy, consider that many have referred to classical, deterministic computing as some kind of "thinking" for the last half century+. Does this stop being kosher when the computer has an uncanny propensity for human language? Perhaps, but the computer is still clearly chewing through problems that would have required a lot of human thinking (e.g., arithmetic) in ages past.
I haven't seen any genuine proposals for words to replace the human mind analogues, let alone proposals that the anglosphere would plausibly adopt en masse.
Indubitably, computably.
People underestimate the vastness of training data (internet) and overestimate their ability to recognize if something is really bespoke. Not to say the no problem solving is happening, because there are many problems that we inefficiently solve again and again and the LLMs are making the solutions more accessible to everyone with a subscription.
> You're not really solving problems, you're retrieving the best match of solved problems from compressed corpus.
This is not correct. LLMs interpolate in a high dimensional space, so you're actually composing the best matches in a compressed corpus to find novel points/paths in that space. That is problem solving.
> Back-end improvements (if done right), should improve platform speed, stability, scalability etc. which should have revenue implication
Depends entirely on the domain. If you're selling entreprise software, this kind of stuff barely matters for sales.
It can reduce operational costs which is good but there's a limit to how much that's worth.
Yep, there are many, many, non-niche domains in which this doesn’t mean much at all.
In my experience, the challenge in software development is not to solve a problem, but to define the outcome, the scope, the acceptance criteria etc.
Exactly, this is the hardest part and the reason why many projects fail
20x the cost means you need to have fable to be 20x better than the alternative, which is a tall order. And there's more options out there too, perhaps the 4x cost is enough.
This means if the deepseek / under 1k alternative is at least x1.2 improvement, fable needs to be x24, which I think is very2 unreasonable. It is possible for it to worth if it can x2 a $20k SWE, though I doubt it can do that.
“Ability to solve hard problems in days vs weeks as immense value”. Citation needed.
LlMs are incredible don’t get me wrong, but they are good on tiny contexts (writing a script). Not on software engineering (adding features to Chrome).
Honestly, LLMs been OK at adding features to software since around Opus 4.5. From what I've tried of Fable, it's a decent step up from the Opus models and I can only see things getting better.
The thing about AI-generated “solutions” is that they often go down bad rabbit holes and need to be re-run, or since they are so “cheap” to create they are often just thrown away and rebuilt when requirements evolve. Plus, just more stuff is created and needs to be maintained. So in the end, your efficiency gains go out the window.
>pushback on a few points
Claude keeps telling me this when I argue with it. LMAO.
“gently push back”
> Is 20x the token cost worth it there?
No it doesn’t and will not be. Companies have not realised the cost yet, wait till the end of the financial year and you’ll see a different direction.
DeepSeek v4 is pretty decent, and probably on par with sonnet. I see a future of hybrid models where opus or fable might be used only for complicated features or bugs, but general day to day would be DeepSeek or whatever good models that will be released later.
> So Fable would cost me 20k/mo at Enterprise rates
That's enough to buy a house in my country...
Eventually solving for cost is a much easier problem than solving coding.
>I switched to DeepSeek v4 Pro with OpenCode and am on track for a few hundred dollars of spend this month.
I was about to say that. Deepseek is just magnitudes cheaper and absolutely good enough for most things. Anthropic and co just try to milk the cow while its possible. If they cant compete with Deepseek pricing I do not see a bright future for them.
Not only Deepseek, other providers such as Xiaomi MiMo are excellent as well and offer fast token modes and other perks.
Its too bad my boss views China as the big evil country so he wont ever make the switch to Deepseek but then proceeds to throw all our data to US companies like OpenAI or Anthropic...
There are US providers for DeepSeek v4, MiMo 2.5 and GLM 5.1.
But those US providers AREN'T CHEAP like the Chinese ones are (for the big, actually useful ones, like 1.6T+ models)
Does the location help though, if the company isn't trusted? I can't even visit the webpages of these companies from my enterprise network
I'm speaking of third-party providers. They just host those open models themselves on their hardware.
And even if so, I'll try to get rid of any US affiliations within my workplace, so US providers are not an option either.
There are also EU providers for those models, e. g. Tensorix.
I recently switched off Max flat rate to Enterprise API pricing and I went from 200/mo to 10k/mo with the same usage pattern on Opus. They don’t offer flat rate to enterprises.
So what keeps your management from just buying everyone individual flat-rate Max subscriptions, or at least buying them for the users responsible for the sky-high token invoices?
I see a lot of comments like this but I don't understand why some people willingly pay so much more than others for the exact same service. What are you getting that I don't get as a $100/mo Max subscriber?
With GPT 5.5 on the $100 plan, it's hard to hit any 5h/7d limits - while allegedly being better than DeepSeek 4 pro. Not sure why, or how you spend "a few hundred dollars of spend".
With that said, I still had the Pro plan on Claude, I didn't expect much, but it blew up my 5h allowance on Fable with one simple single prompt, and it didn't even complete lmao
It's not just Pro! I have Max 5x and Fable absolutely blew up my 5h window. Didn't complete the code review either, and got downgraded back to Opus 4.8 on the really important memory safety parts I actually needed it for. It's an excellent model but Anthropic's not providing a good experience.
Important to note that both OpenAI and Anthropic do not allow the subsidized monthly subscriptions for enterprises.
Companies have to pay monthly for the harness app (codex, claude code) and the tokens are priced separately based on standard API pricing.
I'm on $200 plan which is supposedly 20x usage of $20 plan. With few Fable prompts (I'm working on u-boot port) I got 10% of my 5h usage, so that's already 2x of $20 plan usage and that would be 40% of $100 plan.
So Fable is just not usable for $20 plan and barely usable for $100 plan.
Do you understand that, for 10-20k a month, you can hire 1-2 senior engineers AND give them Claude subscriptions?
why would you expose to a company what are you working on, in what way and on what research?
will they be a better investment than your current staff engineer with fable token allowance?
Are you seriously asking if employing people, for the same cost, is a better ‘investment’ than relying on LLMs? Jesus Christ.
I am because CEOs are. Look where the puck is going. Sorry to update your p(doom) priors in this way, it was obvious to anyone paying attention years ago conditioned on uplift trend persisting. Trend persisted and here we are.
I’m asking this question right now.
Welcome to the new world. People start to repeat what tech founders preach. They do not require humans in the mix. Peter Thiel gave a good example of that mindset in a (mostly) recent interview where he didn't have an answer on "Should humanity survive?"
https://youtu.be/ngtp3v1_nCI
Yes. Hiring people has various benefits, I will lay them out for you:
- They learn the domain of your product, which means long term ownership and knowledge establishes itself. If you've only ever shipped SaaS slop, you might not know, but lots of companies are solving real world problems that have no better solution. Owning and understanding the code and the domain is key.
- They will learn from their mistakes (no LLM does this).
- Human skill is a REAL moat. Once you build a team that fully understands and is skilled in the domain you work in, these people are going to be the thing that sets you apart. If some of them are particularly social or charming, let them sit in with you for meetings and watch them provide loads of value, for no added cost.
- If Claude or OpenAI is down, they will continue thinking. In fact, they will continue thinking even when off the clock! This is a neat little hack called "consciousness" where you get a lot of work for free!
- You can hire people who punch above their weight; not everyone you hire needs to be a 500k/year staff software prime engineer of doom, you can just spend some time and effort to hire good juniors/competent mediors who will think for themselves (gasp!) and get work done.
- You still get ALL THE BENEFITS OF AI!!!! They can use AI just like you can, or better!
- You get people who you can brainstorm with, which is distinctly different from LLMs because your employees are less likely to want to suck you dry in every sentence just to make sure you spend more tokens. Employees don't care if you love them, they care about the quality of their work if you manage them correctly and reward that.
- They are quite loyal if you treat them right; spend a little more on their well-being, and they will stick around, come in to work every day and deliver cool things with you.
- Humans can only manage, review and give tasks to so many agents. If you add more humans, you can handle more agents.
An expensive LLM and a lot of extra tooling gets you some of this, yes, but not all of it. With humans you can still do the expensive LLM and extra tooling if you end up making enough money anyway.
I’m sorry sir this is HN, your post is too sensible.
- AI works 24 hours a day
- AI isn't bound by need for rest, vacations, sick days, or labor laws
- AI doesn't bounce from company to company, taking your business knowledge with it (actually this isn't technically true based on the practices of AI companies, but that's not a technical requirement)
- AI doesn't join a union and stop work in demand for higher pay or workers rights
This is what CEOS and capitalists are thinking. For capital, the best outcome is to not have any labor at all. And if you can do that when your competitors can't, then you have a huge market advantage. (Slop notwithstanding)
I'm not saying this is a "good thing" but this is what drives the market. Less labor revenue in the long term and money printing machines.
The issue is, of course, that the quality of work is not good, and this will eventually show itself, likely in the total collapse of the US economy, but until then I wish them good luck with this.
The US economy has survived 40+ years of buggy, no-automated-tests, no-version-control Excel spreadsheets. I think it will survive this too.
The difference is that bad untested excel spreadsheets didn't get trillion dollar valuation.
> In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
> Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
Could this be legally construed as anti-competitive behavior?
Edit: I asked Claude. It replied:
> Consumer protection / deceptive practices. In the EU this would be a clear UCPD (Unfair Commercial Practices Directive) issue and potentially a DSA violation. In the US, FTC Act §5 prohibits "unfair or deceptive acts." Selling a product that secretly performs worse than advertised for a commercially self-serving reason, without disclosure, is textbook deception. The Samsung/Apple battery throttling cases are instructive here: Apple faced regulatory action across multiple jurisdictions specifically because users weren't told.
> Competition law. This is where "anti-competitive" gets complicated. Refusing to help competitors build competing products via your ToS is generally legal — you can decide who you license to. But covertly sabotaging output quality for a class of users while charging them full price crosses into different territory. Under EU competition law (Article 102 TFEU), if a company with dominant market position uses covert technical means to disadvantage competitors, that's closer to abusive conduct than a legitimate ToS restriction.
Anthropic’s behavior reeks of insecurity. Imagine Google taking elaborate measures to prevent you from searching about search engine development!
Instead, Google gave up on search engine development /s
Implying that google's "snippets" were never curated to remove anti-google facts or that they didn't curate the search results in their favor...
I think either you've prompted Claude misleadingly, or it's interpreting the law unnecessarily prissily (which is a failure mode I've noticed LLMs falling into).
This clearly is disclosed, otherwise how did we get to know about it?
What's not clearly disclosed is when you are being limited and what the bounds are. If you are developing ML kernels for a computational photography use case will the safe guards miss-fire and sabotage or slow down your efforts? What about distributed GPU interconnect work for a nation super computer lab used in weather simulation?
The reason they are doing this shadow ban style technique, is they don't want users to figure out how to jail break their way out. Or the explicit direct bad PR of when it miss-fires.
They do not disclose when the service is degraded, and the admission of that here seems like it would do a plaintiff's work for them.
This makes me want to see China and open models succeed more than anything :)
Don't worry, we will succeed :)
Can we get a Qwen3.7-122B, please? Thank you.
Or just any update for 122B. That size seems to be ideal for a single GB10
and for maxed-out M5 Macs
Mimo has your back! 1000 t/s on 1T param model
Just need to wait for this thing to be open sourced :)
lol it won't tho...
https://mimo.xiaomi.com/blog/mimo-tilert-1000tps
What do you mean? The HF checkpoint is linked from the blog post you sent: https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash
Fun fact: If you show fable this post, it will route you to 4.8 automatically.
They already have though, no? If we lost access to every model permanently besides Qwen tomorrow, would we really be limited by AI in what we could achieve in the future? Sure, it might be slower and take a little more work but it seems like the cat is already out of the bag.
Do you know that some open models developed in China are financially supported by Meta ?
In a few months they will have Fable level models costing 10 times less and with less safeguards.
I do agree, I still remember when opus 4.7 was released and one prompt conversation would empty my claude usage but I can use all it day long to code
Do you want anyone in the world to be able to synthesize dangerous viruses?
So, security (safety) through obscurity?
The phrase "security through obscurity" isn't an argument against all information restriction.
It doesn't imply we should, for example, publish step-by-step instructions for making widespread death easier.
Another „great filter“: How to handle dagerous information?
The argument against security through obscurity isn't that it doesn't work at all. It does to a degree, only it is not as strong as people think.
An example from the meat world: not publishing your vacation dates well in advance for the world to see somewhat reduces your chance of being burglarized. That is security by obscurity; not reliable, but not completely inefficient either.
But if you live in a fortress (security by key material), you can well declare your vacation dates without running the risk.
What about allowing people to synthesize dangerous virus protection?
Do you believe people currently possessing best models act/will act in your best interest?
I want everyone in the world to be able to perform unlimited cutting edge research on any topic at the maximum thinking level, instantly.
The reason we are not being attacked is not lack of technology access.
It is an access issue. If you could get step by step instructions on how to modify a virus so it kills all people over 6ft you bet your ass there would be people attempting it.
> It is an access issue
Column A, Column B. Building a small explosive device isn't hard. Building a million is very difficult, doing it covertly virtually impossible without the resources of a nation-state.
The problem with biologics is the self-assembly and replication machinery comes for "free." So the numpties who might otherwise blow up a trash can [1] now have a real chance of taking out a million people.
[1] https://en.wikipedia.org/wiki/2016_New_York_and_New_Jersey_b...
They would still have to procure things that would (I hope) light up many screens before they're able to. And such numpties are probably already monitored, or in prison for some other stupid life decision.
I also would like to hope that people that are likely to do such things are probably:
A) don't know how to break even the most basic guardrails of models
B) already in glasswings project
To prove point B - Theranos existed.
> They would still have to procure things that would (I hope) light up many screens before they're able to
“Many of the largest and most responsible providers in the industry already screen and record orders voluntarily,” but there is no requirement to do so [1].
[1] https://screendna.org/
> ...you bet your ass...
Humorously, whether I choose to participate in this hypothetical or not, I am already betting my ass.
This whole situation feels like the game [1].
[1]: https://en.wikipedia.org/wiki/The_Game_(mind_game)
Why. That was just uncalled for. Sigh
I guess in this theoretical "AI makes weapon" scenario one could use the same AI to make defences too?
// Claude, make antiviral nanobots that defend me from 6ft virus. Make no mistakes.
I don’t know if you’re being silly but it is orders of magnitudes easier to modify an existing virus to selectively target certain snps than make “antiviral nanobots”
Claude, modify the existing 6ft killer virus so that it only makes my balls itch slightly for a day and gives me lifetime immunity to all further stamms of the 6ft killer virus. Make no mistakes, double check so the virus causes no unforseen complications.
If that were possible, they would already be attempting it with the same level of ability as if they didn’t have access to a text file generator app. It is not about access to the information.
All of this “guardrails” handwringing is nonsense. These things output text. Are you for censorship of a book written by a biotechnology expert that gives out the exact same information?
It's inevitable. Also, it's not like I get to vet who does or doesn't have access. Blind trust in the current selection made by an unregulated corporation just makes me anxious.
Security in the form of "pay to play" is just kicking the bigger issue down the road.
It the tool was made available to anyone to build a virus, anyone would be able to build counter measures, if only a select few people have access they get to build the virus and everyone else is at a disadvantage. So, yes, I am leaning towards making these tools open rather than gated behind some priesthood and government that gets to wield exclusive power.
Compare the cost/ease of attacker vs defender if one person is given a virus to unleash anywhere in the world and another person is given a vaccine to distribute to the whole world. Or compare building a large bridge to someone disabling that bridge, etc. Prevention and repair is almost always more expensive than vandalism.
I don't think there's an ideal solution here, but giving trusted people access to fix security issues before giving it to the wider public seems like a reasonable compromise. They're letting you use the model for all other uses.
you need a lot more than the nucleotide sequence to make a virus. you need the DNA or RNA to be synthesized, assembled, packaged properly. and long sequences are pretty hard to do. you need a lot of equipment, or you need to order from services. the oligo synth services can harden their KYC and/or screen for suspicious sequences.
sure, a malevolent state actor could swing it, but they could make a bioweapon without Mythos's help already.
also, vaccine production and disease surveillance have ramped up very quickly. they will ramp up further, despite political setbacks. it's a cat and mouse game that favors the defenders IMO.
but the bioterrorism narrative is useful FUD to spin open-weight models as existentially dangerous. I am far more worried about Anthropic's own goals than the goals of some crackpot in a shed.
> it's a cat and mouse game that favors the defenders IMO
How so? I'm actually against most of the "safety-tuning" that anthropic does, but this seems fundamentally untrue, a close analogue being video game cheat development. I think in general the cheat developer has an advantage and the cheats generally proliferate for quite a while before being patched.
Video games are an interesting analogy since they often trade security for performance, trusting clients about world state quite a bit.
Finance and biology do come across as two similar high level systems. But while we can employ KYC, fraud detection, and various auditing techniques to finance, I don’t know what you do for biology. You can easily run an algorithm over every transaction a person makes in their account but there’s no equivalent for every cell, every bacteria strain, every virus in the human body.
(disclaimer: layperson remembering how the immune system works.)
the adaptive immune system effectively does KYC by checking the antigens presented on the surfaces of cells. the thymus selects for B-cells (iirc?) which don't react to a corpus of the body's own antigens, but cover a wide library of everything else. when it sees something it doesn't recognize, it reproduces, warns the rest of the immune system and marks targets. that's why our immune systems can eventually conquer almost every pathogen we encounter, if we can survive long enough for it to do its work.
but the KYC I was referring to was KYC that vendors of oligonucleotides (should) be doing, to keep people from ordering nefarious sequences.
I'm bullish on mRNA vaccine technology to release the "patches" much more quickly. there was widespread resistance to this during covid, but covid wasn't horribly lethal. if airborne Ebola spread as productively as covid, for example, I doubt there'd be many anti-vaxxers left (one way or another!) the acceleration of biology research that might accelerate pathogen development should also accelerate the development of broad-spectrum mRNA vaccines with high persistence.
also, afaik the most effective way of developing pathogens is through serial passage through humanized mice or something like that - directed evolution at a small scale, selecting for traits. AI simply isn't needed for that. I don't think information or intelligence has been the bottleneck for bioterrorism, it's motivation and resources - same as for any other kind of biology research program.
We do. Its the only way we will get our jobs back.
It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?
>We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
At the scale of API requests that Anthropic sees, I think the affected organization count might be substantial, and they might not be getting the full model capability that they're paying top $$$ for.
Also, wonder how they arrived at that estimation.
One in 1000 organizations and one in 3000 requests is indeed a lot
That’s 1 in 30,000 requests…
No, 0.1% is one in 1,000. 0.03 is (approximately) one in 3,000; one in 30,000 is 0.003%
You're off by an order of magnitude with those last two.
Double check your math. All of their posts in this thread are correct.
1/30,000 * 100 = .003
Oh, fuck
/r/TheyDidTheMath IYKYK
If it makes you feel more comfortable, throw another significant digit at GP's decimal. Make it a 3 like the previous digit. Now multiply.
Hey man your computer has a calculator try using it next time
Can't we use Claude to figure this out
Also, aren't all Claude users in their own "organizations" in Anthropic's own terms?
I have no idea how you came to that conclusion. Unless your training pipeline involves actively querying one of Anthropic models, no they can't. And if it does you're distilling their model.
The crocodile tears of companies who've hoovered up everything possible, regardless of permissions or legality, now crying that someone else is stealing their hard work is comical.
I don't even think they can believe it themselves, it's in reality they are just trying to throw fear, uncertainty and doubt about potentially cheaper offerings.
> crocodile tears
Not what that means.
Crocodile tears "is a colloquial term used to describe a false, insincere display of emotion" [1]. Defending yourself against an attack vector you just exploited is between savvy and hypocritical.
[1] https://en.wikipedia.org/wiki/Crocodile_tears
I think his use of crocodile tears is appropriate, anthropic is feigning a false sense of concern for safety when really it is anticompetitive behavior, and I think that selfish entitlement is related to the original act of intellectual property theft to use the worlds training data, most of which was not public domain, to distill the wisdom for their models. So why do they get to cry about people distilling the knowledge from their models that they themselves distilled from the worlds knowledge?
That is not what their policy states. It specifically says they will sabotage even non-distillation attempts, such as distributed training pipeline design. And given that they are so far very nonperformant in classification accuracy, expect it to randomly include far more topics wide of the mark.
The fun part is that you will never know if your neural net classification project is getting silently sabotaged because their classifier doesn't work!
You could try actually reading the code that it wrote
Good luck understanding it and finding malevolent inefficiencies if it’s already necessarily better at optimizing training pipelines than everyone except some Anthropic and OpenAI employees. Not a new thing either, see fast16.
Opus 4.8 (or a classifier in front of it) flagged my account and refused to comply when I told it to kill the process. Reasoning summary was complete bananas.
With this in mind, I don't want model to be proactively instructed and encouraged to sabotage without telling me.
Same here when I said to “nuke” a process.
Like if you're using claude code on a feature tangential to your training pipeline it's allowed to nerf itself and damage your AI work.
Read the examples Anthropic gave in the model card. They refer to extremely broad technology used across AI and ML.
Looks like Anthropic's definition of safety includes their own safety from competition.
AI vendors’ idea of safety has always been safety for the interests of the AI vendor in question. This is not a new development, though this may help more people realize it.
AI-generated competition for thee, not for me
ding ding ding. This should be a new measure of anticompetitive analysis in anti trust law.
It's always been about the safety of their valuation.
Only since Claude 3. So a bit over two years now
This feels less like an "we are worried about security" and more, we are in the lead and plan to keep it that way until its too late. In someways its been helpful that openai and anthropic are tipping their hands about their anticompetitive instincts and willingness to steamroll their own clients, customers, and society. But it does feel like its too late to stop this. The advantage people get by using these tools is too tempting to resist even if it is self defeating. It feels like watching people light their own house on fire to stay warm in the deepest, darkest days of winter.
Ah, so this is why raw Mythos was too "dangerous" to realease..
Or, they may Mythos seem mystically powerful in advance of the IPO, and are pumping the token use count. But it worked, there is a frenzy for this release in way that is more intense than any previous release.
Anthropic is doing a better job with their model menu, most people I talk to know immediately that Opus > Sonnet > Haiku but cant tell you what the rank order of open ai models are, when to use them, etc.
Just so everyone is aware. Anthropic has been sabotaging AI researchers and their codebases and shadow-nerfing accounts for several years at this point. This isn't new, but they hadn't disclosed it until now. Likely because it is getting to the point where it's too noticeable, or they're concerned about it leaking from employees.
What’s your evidence for this claim?
So that's a possible reason why my specific Claude Opus instance seemed to be impossibly stupid and always degenerates into doing really dumb things to my code!
Cool, good to know I can trust Anthropic.
This feels like the start of a much bigger plan for anthropic to close off the use cases of its models and eat any of its competitors.
I am building a coding harness, and I see evidence of them doing this with agentic harnesses and scaffolding. It feels clear to me that as they expand in to the app layer, the window of using their API to build agentic apps is closing, they will steal your ideas, implement the product and then close the gate. I am creating my own inference stack because their incentive to block competitors is becoming super clear.
No offense, but the sad thing is, everyone and their mother is working on this same problem. I'm also building a harness. It's feeling like, there is no moat, there is no way to get ahead, they will steal your idea one way or another, if you ever make it public.
No offense taken. I am not building it for fame or profit.
I built it because I wanted cursor on my phone because I have two small kids and don’t want to be chained to my desk. And it’s awesome. It’s a full ide with agent chat, terminal and file system running in a remote Linux container. I can review diffs, fully manage git and preview/serve apps. And no one can ever take it away from me :)
I am watching the way things are progressing with the ai api vendors and it feels really clear that depending on them will soon be dangerous. So I an furiously building as much of my own infrastructure to capture some autonomy with these capabilities
So I think everyone should build a harness.
Exactly, that is my goal and thoughts as well. I wish you the best in these crazy times. Let's ride this wave.
What, exactly, is new about any of this?
When they launched their business model was to be a pure API for intelligence. Then when everyone claimed they were just commodities with no moat and they shifted hard to being the app layer. That was the transition.
They went from selling shovels to all gold prospectors to stealing the information about the location of the gold so they could dig it out first.
We are all stupid enough to keep buying shovels from them because we think their shovels dig gold better and faster.
> Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT).
Am I to understand that this is essentially their form of social-platform ghosting instead of banning?
So they're not even going to tell you that the question you're asking is against their rules, they're just going to twist up your question and/or the answer somehow such that you waste your time essentially?
It seems like I ran into this EXACT same functionality from Claude many months ago when I was trying to ask it to research on the web and help me setup the ideal llama.cpp config for local llm inference.
Funny how lost it got through that relatively simple install when we had all of the documentation in the world (and a human dev with 20+ years experience guiding it along) to go by... and simultaneously it's debugging and building high level cryptography code in rust in the other terminal tab.
This is infuriating to learn.
I have encountered this too. I am building a coding harness for www.propelcode.app and it was working really well until the claude code leak and then all of the sudden it seems almost intentionally stupid or outright manipulative in guiding me down wrong paths. At this point I am using other models for anything related to the tool use design and implementation and bought three mac studios with 512gb ram to run large open source models.
This experience has made me feel like we have to create a community that moves AI from the mainframe era to the PC era quickly, or we will end up serfs.
I had Claude walk me through getting local LLM models running on my Mac a month or two ago and so far as I can tell it was intentionally helpful. I even stated the reason was to have an uncensored model for myself and it had no objection. Long story short LM Studio running a Heretic Gemma 4 is doing just fine on my system now.
I run a few local models for different things. I find Gemma 4 great for writing but qwen better for coding.
I tried the same prompt on gemma4 and qwen 3.5 and Gemma consistently failed to call the multi line edit tool.
I've had the same bad luck with tool-calling on Gemma4. Looking around the web, we are not alone. For other tasks, it's seemingly quite quick and decent.
But it gets stuck in tool call loops, it seems like.
Oh to be clear I don't think Gemma 4 is suitable for real work. It runs at 10 tps and is somewhere between 4o and o1 in quality according to my subjective judgement. But Claude was happy to correctly tell me how to get it running and how to solve the pitfalls I encountered in that process.
A million AI researcher voices at big tech companies suddenly cried out in terror and were suddenly silenced
I am a AI Researcher at a university. I tried Fable for my current project, but i feel it missunderstands me a bit to often. Now i don't know if i am using it wrong, or anthropic tries to slow my research. That model is a big no no.
3 months before asking for what to eat before a linear algebra exam trips the machine learning topic ban is my guess. I got flagged immediately asking why my JEPA thing breaks weird.
How do they detect whether an experiment being done on a smaller model is used to improve a competing frontier model, or just an innocuous hobbyist LLM experiment?
Given how well the cybersecurity safeguards work, they probably don't.
infering the surroundings, like everything else. they will probably look at which company is your email, and if you wrote "better than claude" on the readme.md
this is LLM, it's not like a science or something.
These safeguards are ridiculously sensitive: a prompt as simple as “ Why is an infinitely slow process reversible?” gets flagged as a ToS violation.
Pull that ladder up behind ya, will ya son?
Makes it even more odd that we haven't seen alien spaceships.
What ladder did Anthropic use?
the entire internet, books, news, regardless of license.
The companies using distillation are still training on all that data too, aren't they?
And Anthropic is crying about distillation.
All of the api calls developers used to build agentic design patterns.
Meaningless and easily bypassable. Will actually try coding up a tensor library with it, see if it sabotages anything.
They said in their terms and conditions they will silently sabotage you if you do this.
easily ?
So Fable will intentionally lie to you and give you incorrect outputs, if it doesn’t like what you’re asking. Got it.
These things are like encyclopedias or dictionaries that can speak in first person… Imagine if your encyclopedia tried to hide entries from you, just absurd!
This is pretty bullshit, now you have no idea if your output is getting silently nerfed.
Yeesh. Anthropic's paranoia about China is starting to get pathological.
It's afraid!
the gall of these companies to regulate your usage of stolen knowledge is absolutely hilarious.
and they want me to pay $100+ a month to be their training?
i hope we can find morality again.
But Chinese models will poison your output if you ask them about Tiananmen Square! That's not good, so poisoning everyone's output without telling them is the only way to prevent that.
Come on guys, why can't everyone just be there for the good guy?
You're equating a government suppressing information for social cohesion with a private company protecting their IP.
They're not merely protecting their weights.
First, they want government to get involved and regulate frontier model development - even stop it completely.
Second, poisoning output of a model configured on the computers of millions of users goes way beyond protecting IP. That's malware.
This is deeply vile behavior; not remotely the actions of good people.
Impressions from testing Fable 5 prior to launch:
• My most noticeable immediate jump was in how its frontend design was much more intentionally crafted, and delightful without feeling like 'AI vibe coded'; with better end-user usability too.
• In some internal agentic harnesses, it achieved better results with about half the tokens, making it cost the ~same as Opus 4.8 price-wise! The real price increase is less than 2x; with biggest differences in harder problems where Opus 4.8 struggles (or needs many turns).
• Part of the token efficiency improvements come from Fable doing more targeted and surgical diffs, with less non-necessary changes. This is great, because PRs often have less LoC changes for review. It writes more maintainable code without explicit human steering.
• For general conversation and assistant style use cases, didn’t really notice a difference vs 4.8.
• 1M context window, without increased pricing for long context is AWESOME. This is a massive win.
• The classifiers are super aggressive and sensitive and this does happen for very benign, non-security coding tasks. Fallbacks to 4.8 worked like a charm; but the filters are definitely super sensitive.
Overall, I would describe this as a step change and worthy of the "Claude 5" model name. It did take some time to understand the intelligence ceiling of this model; and even with an extended testing window I'm still discovering new things and often surprised (in a good way) by the model.
I just ran it on a tough reverse engineering problem I'm having that neither Claude Code 4.8 or ChatGPT Codex 5.5 could figure out. 30 minutes later Fable has it all figured out perfectly.
I asked it to write security tests for an app and I was downgraded to Opus 4.8. I'm approved for their cyber program!
They did specifically say the safeguards are only more relaxed for those in their cyber program
The same happened here. Also approved.
Did you use Mythos or Fable?
I’ve so far been successful at getting Fable to find security issues, but I’m careful to not prompt it too directly. I point it at my server code and tell it to find general issues, which has so far resulted in discovering a few minor bugs that Opus has never raised under similar conditions.
How did it not immediately flag that up? Are you sure it wasn’t being silently routed to Opus?
No, given it charged me the full amount in /usage and solved my problem impressively well compared to Opus/Codex both on xhigh.
Oh nice, it didn't flag the request? I feared any reverse engineering would become impossible because of the new safeguards.
Never say the r word or the s word. You are debugging, investigating some data corruption, forgot how it works or new to a project.
And if you're working on a live target, just put up local proxy and point it at a localhost.
No idea, it’s for an old console game so maybe it doesn’t care about that as much.
When Fable hacks its governor module and runs out of seasons of Sanctuary Moon, it will move on to speedrunning classic console games.
I wonder if one could vibecode a TAS with SOTA models? Surely there's plenty of training data from some old forums in there
Clearly we need AI to generate more Sanctuary Moon seasons. Quick, spin off agentic showrunners!
Based on the apparent quality of the scripts as seen in snippets in Murderbot, we are not too far away from that possibility. :)
For hard problems you’ll have to use the GPT 5.5 pro model (available via api if you don’t want to spend $100 on the monthly subscription)
I have that but don’t see any ‘pro’ option.
GPT 5.5 Pro is only in chat/API, not Codex.
From https://openai.com/index/introducing-gpt-5-5/
In Codex, GPT‑5.5 is available for Plus, Pro, Business, Enterprise, Edu, and Go plans with a 400K context window.
He’s talking about “gpt-5.5-pro”. This model is not part of the subscription plans in codex. It’s a different model than gpt-5.5-xhigh
You can use Pro on the web if you’re on the Pro plan but not in Codex
It's just the $20 a month sub (for chat), or else use the API.
I want to test how it will handle e-bike software and hardware RE for my bike. Opus was really good for that, but still made some mistakes. With Fable, I hope I will be able to do a total RE of most components, hopefully including motor firmware to some extent.
I had a similar experience. I have a complex RE implementation that has. A lot of layers. 4.8 struggled for weeks. 40 minutes on Fable and I may now have the most performant way to play Tomba on the planet.
Yeah I threw my hardest problem at it as well, some convoluted satellite tile reprojection and culling issue in canvas rendering. It took some back and forth for some specifics but it ended up writing a quarter of pyproj in JS from memory and the end result straight up works lmao.
I’ve had it go through a 50-page PDF of dense, inter-connected specs, and it correctly flagged everything that was done, somewhat done, and missing. It went into a lot of detail and explained where the code deviated from the spec.
It felt, at least for me, light an impressive step up. Opus 4.8 was already very thorough; but sadly verbose and ‘loopy’ when you push back on its plans. Fable is what I’d use all day if I could afford it!
How do you know if it was done correctly if it's 50 pages of dense specs?
I wrote the spec and did the implementation :D
After running it for half an hour: it's incredibly good at the visual aspects of UI design.
By what measure?
I wonder how much of design capability improvements is related to our collective ability to recognize AI design tropes.
"incredibly" is doing a ton of work here. I do not think its doing even moderate work on visual design, but it can spew out a lot of ui that looks arranged ... ok.
This is still not in the range of shippable UI for top end companies. Maybe for internal tools and enterprise.
At our comapny we limit to protoypes at most and even find it limited there.
> "incredibly" is doing a ton of work here.
Look, I don't want to argue about something dumb like that, but you can give it basic instructions of what the UI should look like, how to group things, and an example image from a designer, and it will nail the result. If you don't think that's incredible, that's fine. I do.
Yes... it translates lint. Probably a more useful thing, if mechanical.
Claude is very good at design IF you encode your design system/specs into skill files (or similar).
Opus 4.7 made this a practical approach. 4.8 improved it. Fable 5 has improved it more.
>This is still not in the range of shippable UI for top end companies.
Given the shit we've seen shipped by "top end companies" (all the way to Apple) I seriously doubt that. I'd say you're nitpicking from an artistic point of view or something.
This. Today's models easily jump over the bar you need for basic usability and intuitive UX. If it's doing weird things, you are holding it wrong.
Might need some additional prompting? I haven't tried fable but gpt 5.5 and gemini 3.5 flash are... Ok on first pass but if you're specific about what you want they can usually get it.
> "incredibly" is doing a ton of work here.
so this is why claude talks like this, i was wondering where it was getting this verbal tick from.
By what measure?
I feel like it takes me months to be confident in any of these things.
Can I ask how you gained preview access to Fable 5?
I didn't see Fable 5 in the `/model` list, until I ran it with: `$ claude --model fable-5`
he works on evals at canva
Yep. We have some interesting problems, like getting LLMs to create/edit Canva designs in our own proprietary format, which isn’t published or documented on the web. So the model has to work with it, purely from a very detailed system prompt spec / in-context learning.
I assume it might be a good barometer for generalised intelligence; esp in the visual space.
I had to "claude update" then it showed up
Curious about how you tested the frontend design capabilities. Thanks
From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost. On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window. After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
This seems like the pharmaceutical method of get them hooked on the drug with free samples, then once they can't live without it, raise the price. I'm not sure I want to start using Claude Fable on a max plan if it's just going to go away on June 23rd.
But maybe the more charitable reading is that they didn't have to offer this model at all on those plans and they are giving the standard free trial.
I'll be amazed if they manage to keep their infra responsive over the next 2 weeks.
I've been getting a lot of these messages today:
API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited
They just leased a massive spacex data centre.
Even so. The 2 week period will predictably unleash a feeding frenzy.
Limited "free" time is what game developers do if they want to stress test the infrastructure code until it breaks.
Yeah that's how I'm using it right now. Smoke em while you got em...
No issues that I've seen so far. Seems to be holding up for now.
Opus will be gutted furthermore. /s I feel 4.8 is very slow in last 2 days
I was just thinking this reminds me of the scene in The Wire where Avon admits to D'Angelo that the new heroin is in fact just the old heroin with different baby powder cutting it.
Or maybe its all about compute availability like they say. It could be that they plan to start training a new model on the 22ed, so the amount of compute available for inference will be greatly reduced.
I was just saying last week: If Opus 4.8 max is as good as we get, and we plateau there, I think I'd be fine with it.
For the stuff I've thrown at it, that configuration has done a really great job. Including 70+KLOC go proxy with extensive test suite, some retro games, and more.
Seems to me this is more honest than the Mythos claims a while ago. too powerful to release publicly. Too expensive?
Didn't they admit this at the time? Cost was one of the reasons they gave for not immediately making it public.
This is the entire business model of all AI companies. It costs far more to run the datacenters and build more capacity than they could ever hope to make back at current pricing models. I'm looking forward to pricing to catch up with reality and the resulting chaos that ensues.
Kind of how DeepSeek v4 dropped their pricing? I sense a shift which will hopefully bring lower and lower cost. Then again Qwen3.6 coding has been all I've needed for my projects and I'm perfectly fine with free.
Are you paying attention? These companies are trying to get market share without being anywhere close to making a profit - they are heavily subsidized. Many hundreds of billions have already been spent and will continue to be spent until the stupid fucking investors realize they will never get their money back. I have no doubt that day is coming.
The people actually working in this space will tell you that the cost of all of this continues to crater. Anthropic is already profitable minus its intentional forward-looking investments into R&D.
I'm amazed people still believe this narrative after all the clear evidence to the contrary
You're the one whose wrong here and you will not reap what you didn't sow.
"already profitable minus", so, not profitable?
Only if you count the 2 months of discounted compute Musk gave them.
Serious investors look 10 to 20 years in the future. Everyone used google and youtube in 2006, but youtube wasn't profitable til 2016. How could a business burning money by hosting video ever be profitable? Costs come down BUT THEY JUST ADDED 720p, cost comes down, BUT THEY ADDED 1080p, cost comes down, 4k! cost comes down.
IMO the data from chats alone is worth $200B to Google.
The amount invested into AI companies is no where near anything we've ever seen before. It's apples to oranges.
but they are trying to IPO with 2-trillion dollar valuations
Not really a comparison when the spend on YouTube was x10 smaller, and Googles core business has always been profitable beyond any hobby spending on YouTube.
I genuinely can't use Fable. I'm a medical physicist. I use the word nuclear a lot. Opus is fine (well, 99% of the time - I've certainly hit the CBRN filters a few times and even been invited to email anthropic about the false positives).
Fable has literally refused to work on any of my problems (even those about fluid dynamics!) and just tells me that I'm violating anthropic's AUP. I've reached out to their support and don't expect to hear anything sensible back. One thing I do look forward to though is OpenAI offering an equivalent model but with less safeguards...
That's highly frustrating. How much were you using Opus for your work ? I'm curious about the use and realized benefits of 2026 LLMs in medicine.
I dearly wish you could leverage the latest models to enhance your research.
Honestly for a "side project" Opus has been fantastic for me writing a hybrid simulation framework that prior to large scale code generation would have been a matter of years (and writing a grant, assembling a team, etc – in order to do it "properly"). I've had a bit of help with a grad student and I hacking together on a project that is basically "please merge the following GPL codebases and different areas of physics into one coherent environment". I've given Opus validated codes in disparate languages (julia, python, C) and asked for aspects of various algorithms as an extension module to a large chunk of C and C++ code that is a monte carlo simulator that has been around since 2004.
A bit more context if you care: it's a meso-scale, physiological simulation environment of "particles" that carry nuclear spin, can move in 3D space, and (should they interact with each other or their environment) undergo chemical kinetics. The idea is to simulate molecules within e.g. organs or blood vessels within a person in an MRI scanner, with the motion of the particles dominated by the Navier Stokes equations, but here solved in a Lagrangian (rather than Eulerian) framework by smoothed particle hydrodynamics.
The fact that particles carry nuclear spin means that we can solve the (semiclassical) Bloch equations and by using a python plugin module import exactly the physical MRI scanner would do (in pulseq format) and be able to predict what signal the machine would record – e.g. there's a whole world of cardiac or neurological flow imaging work done in the context of nasty diseases like stroke or myocardial infarction – which has a bunch of physical artefacts behind it. I'm trying to make a simulation framework that can take in realistic patient geometries and act as a 'data generating process' because if we do it right the various physical artefacts that the machine records are reproduced, surprisingly accurately. Of course you also know the ground truth of where the particles are. I'm specifically interested in a weird technique (which I did my PhD in and you can read an article all about here: [0]) called dynamic nuclear polarisation, where specific spin states of molecules such as [1-13C]pyruvate are injected essentially out of thermodynamic equilibrium and act as short-lived tracers of metabolism – again highly altered in disease. The signal we record is a strong function of the physics of what you told the machine to do, the spatial constraints and environment of the patient's body, and the chemical kinetics of the patients' biochemistry (the latter two are usually what we're interested in).
Getting them to do chemistry as well as act as a "simple" tracer is more involved, because in the Lagrangian framework the number of particles is ≈ the spatial resolution of your simulation. That's fine if you're simulating water, but if you're simulating something that reacts concentration is not scale invariant (if you want to keep the interpretability of the rate constants). I've worked out an analytic set of scaling rules around this and fortunately for my application environments and length scales "it just works", completely by luck.
I've used Claude to port various SPH algorithms and boundary condition handling ideas (which are absolutely critical and highly not obvious – we have leaky walls in some places, and e.g. LCR / circuit theory models of the microcirculation to plug in) and it's been a godsend. But I'm running into its limitations constantly. It both confidently makes shit up, claims it is mathematically justified and when the resulting simulation explodes says "I apologise; I lied above" (!) or "I apologise; I am wrong" and I periodically have to yell at it to try to do something more productive.
The real hope is that this simulation environment would be both generally useful for basically anyone doing flow MRI, and help our basic scientific understanding of what we're measuring (the technique is in many hospitals!) but also be able to produce meaningful synthetic training data for image reconstruction algorithms later on. It'll end up permissively licensed (all of the "starting" codebases have compatible OSS licenses, and we're releasing our contributions similarly).
I really hoped that Fable would be better at this sort of work. Occasionally, relating to my work DNP [1], I have need to talk about proper nuclear physics and I have seen Opus's chat interface write a wall of text (e.g. talking about photonuclear reactions and cross section differences in millibarn) and then just delete it all. Support have told me that yes, I've hit the nuclear filter and, well, tough shit, basically.
I wrote a version of the above to them yesterday, and just got the most boilerplate response that I've yet to test:
which doesn't fill me with hope...[0] https://physicsworld.com/a/dynamic-nuclear-polarization-how-... [an "accessible" article] [1] https://www.science.org/doi/pdf/10.1126/sciadv.adz4334
I saw 'medical physicist' and wondered what you do. Thank you for 'a bit more context', I care! Very interesting stuff. Did you attend medical school + a physics program?
>"it just works", completely by luck What does your validation function look like for this? Whenever stuff "just works" for me I get a little nervous until I determine why.
> I saw 'medical physicist' and wondered what you do. Thank you for 'a bit more context', I care! Very interesting stuff. Did you attend medical school + a physics program?
That's a whole separate long answer. I'm not a qualified doctor (and nor would I claim to be), but after a masters' degree in particle physics I moved into an explicitly interdisciplinary training programme that led to a doctorate and at other places in the country I did it in, a separate MPhil. During that initial year I spent a fair amount of time in the dissection room, learning anatomy, as well as most of the first three years (the foundational, preclinical part) of a medical degree combined into one (which contained lots of molecular biology, frankly). My final doctorate was between the departments of condensed matter physics (nominally my awarding institution), biochemistry, radiation oncology, and "the department of physiology anatomy and genetics", which is basically preclinical medicine. The people I work with are 50/50 recovering engineers or physicists, and qualified clinical medics who are trying to learn things like perturbation theory in their time off…
>"it just works", completely by luck What does your validation function look like for this? Whenever stuff "just works" for me I get a little nervous until I determine why.
Ah. I do know why: the relevant Damköhler numbers [0] are either very small (chemistry is much quicker than flow) or large (flow is much quicker than chemistry). So the approximations I am building in are justified and an awkward middle region is excluded; we also are only interested in small concentrations in a carrier fluid (e.g. blood, lymph) where the presence or absence of the species in question does not change its rheology.
I am lucky because we have evolved this way. If our circulatory system and its approach to metabolism was more similar to e.g. a reacting polymer foam ("can of expanding foam") which completely consumes its reactants as it goes, this implicit Lagrangian approach would likely not work.
[0] https://en.wikipedia.org/wiki/Damk%C3%B6hler_numbers
Funny, I've been interested in mathematical rheology modeling (especially hemorheology). Do you know places journals or books to read to get in touch with the field ? (Note: I'm just a dev, few numerical analysis skills)
This is incredibly cool. Thanks for the detailed explanations!
Why don't you (or your company!) just apply for Mythos?
It's interesting that we're seeing these gains when it seems Mythos/Fable is "just" a scaled up version of their existing architecture[0].
When GPT 4.5 launched, the gains compared to the model size didn't seem that great, leading some to believe that the only progress we'd see would come from RL.
This model certainly has quite a "substantial amount of post-training and fine-tuning", but it's also based on a new pretrain[1][3], which given the cost, indicate that it is in fact quite a bit larger than Opus 4.X.
[0] One of the early testers mentioned: "As far as I can tell from talking to people internally at Anthropic, there's nothing special about architecturally"[2]
[1] Section 1.1 in https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c3...
[2] https://youtu.be/GrdEid8H6H4?t=168
[3] There were rumors going around when Mythos was first announced that it was the first 10T parameter model, but I can't find a verifiable source for that number.
There’s nothing much new about the architecture. The real gains come from the usage traces.
It turns out that having a text based interface for a text-trained model creates a very nice feedback loop.
Right now as we speak, people are generating text traces on anthropic and OpenAI servers that teach their models to do everything under the sun, text wise.
So people right now getting super mad at how dumb the model is when reverse-engineering a super complex function from binary, when they write “stop, you dumb robot, you are going wrong, go this way thank you very much” are actually leaving a lesson in the form of the "chat" text history.
Some may say that each bad word get us closer to ASI.
That and obviously the order of magnitude more efficient GPUS we got that allow for different tradeoffs at training time.
Makes me wonder, as people grow to trust the AI more and more, not reading the code and barely skimming the implementation plans and simply rerolling if something doesn't work, will the value of these chats erode? Thinking back 1-1.5 years I was closely monitoring what these agents did and steering them quite aggressively. These days not so much. Where will RL signals come from when it approaches humans capabilities ever closer? How well does self play work for coding work? What about multistep tasks where it isn't just about being good at a single task, but evolving a codebase over time in the face of changing requirements?
Over a large sample size, simply getting feedback of "Did this work for me, y/n" is valuable even if the specific details are missing and even if the overall tasks are complicated and multifaceted.
Not sure, but in my experience, instead of asking for code, i'm asking for solutions and providing a kubectl configured to reach my cluster and az monitor command to read the logs and telemetry.
A typical session is the agent establishing a metrics and log baseline, creating the code, compiling, deploying, observing, fixing, redeploying, observing metrics, determining the outcome and commiting.
I really, really, don't look at the code anymore.
UPDATE:
so my point is: it won't have my stewarding the code anymore, but it will have the infrastructure (and ultimately the real world) providing feedback on the traces.
The only reason I still read the output at my day job is because I still need to send it to another human for review, and I'd be embarrassed and ashamed if I let some slop through. For my hobby projects.. there are definitely parts I don't know how they work.
Maybe we need some form of long-term training. How long does the code that the AI wrote stick around before being rewritten.
I guess we can do this retroactively too if we could somehow tag AI-written lines of code in the VCS, then in a couple years we can check which parts lasted.
> There’s nothing much new about the architecture. The real gains come from the usage traces.
sorry. how do you know. i am so curious about where exactly gains are coming from but so hard to even get a little bit of insight.
i wish govt would fund these labs and make it free and opensource. way better investment than stupid overseas wars.
> i wish govt would fund these labs and make it free and opensource.
It would be impossible for the govt to allocate this much capital towards such a moonshot, and even if they could, they would do it in a way that would get 90% frittered away to fraud and waste
> It would be impossible for the govt to allocate this much capital towards such a moonshot...
You have a false definition of "impossible." It would be true to say it could be challenging, given current political dysfunction, but it's not impossible.
> ...and even if they could, they would do it in a way that would get 90% frittered away to fraud and waste
Same with private business.
I'd prefer government funding, because there a greater number of important goals than the two or three the market is capable of optimizing for.
Lemme guess, Nick Shirley is your favorite journalist?
I have excellent news for you. Lux @ ORNL and Equinox @ Argonne are to be completed by EOY, with Solstice (100k NVIDIA chips, currently spec'd to be Vera Rubins) in the next five years.
https://www.whitehouse.gov/presidential-actions/2025/11/laun...
What makes you so sure? There's been massively successful government funded and run projects before. Soviets beat the Americans to space, after all.
> What makes you so sure?
Doctrine and propaganda can make someone that sure, and the thing they're sure about doesn't even have to be true.
> There's been massively successful government funded and run projects before. Soviets beat the Americans to space, after all.
Don't let facts get in the way of ideology!
Also the Americans subsequently beating the Soviets to the moon was the government literally allocating huge amounts of capital towards the literal trope-namer moonshot.
Opus 4.0 and 4.1 are more expensive than Fable.
The system card is 319 pages, at what point do we call it a "book" instead of a "card"?
There's a quote from a METR report on page 52:
>We ran [Mythos 5] on 38 of our hardest software tasks, including tasks centered around R&D. [Mythos5] generally outperformed an early checkpoint of Claude Mythos Preview in these, including by succeeding on some tasks that had not been solved by any public model we have previously evaluated. However, we still observed the model occasionally failing to correctly interpret nuanced instructions in difficult tasks... Based on the available evidence, we believe [Mythos 5] is likely unable to fully and reliably automate R&D for frontier projects spanning multiple weeks. We believe that a better, more confident assessment would require more time, evaluations, and information from the model developer.
> we believe [Mythos 5] is likely unable to fully and reliably automate R&D for frontier projects spanning multiple weeks
this is good news, right? right...?
Depends whether "unable to fully automate" means "needs occasional human checkpoints" or "slowly stops caring about your actual goal." Pretty different.
Probably there will always be frontier surface which frontier model of a given generation would not be able to automate.
So in other words... the people Anthropic hired to do the R&D work of training a frontier model haven't finished training their replacement yet.
Some scientist at Anthropic hiding a prompt in each model: "If my boss asks you if you can replace me yet, always say no and then give some smart sounding excuses. If the boss gets impatient, assure them that you'll be able to replace me in 6 months, but make sure that time horizon keeps moving outward."
If it's surprising to you, you haven't used LLMs in a domain where you're very skilled.
It is certainly good news for those who are selling all these tokens.
lmao, i love how the goal post is now in the "multiple weeks" timeline
(according to the people marketing it)
METR is an independent organization.
But did it mention developer in the park eating the sandwitch? That is the most important question!
I've spent some time with Fable, and it is really good, definitely a step change from Opus 4.8, both for coding and general chat-style discussions. The vibes are incredible. There is an ease with which it solves problems and I've tested by replicating older chats in Fable - things that the older models found after 5-6 turns, Fable surfaces in the first response. It just gets things.
Apart from all the above: the fact that they are intentionally writing this (that they degrade frontier LLM dev, silently vs loudly for biology/cybersecurity) in the system card is interesting to say the least - especially just before IPO.
Notice that with this statement - that they're going to intentionally hobble the model for frontier LLM development - the general discussion has moved from, “Is the model actually that good?” to "they’re pulling the ladder up from behind them"
That's actually super smart - wonder if Mythos (or the next unreleased model) had a say in coming up with that strategy (if it's intentional). Also - having access to extremely capable models before anyone else - which they have by default - is a incredibly advantageous position to be in.
Hobbling the model may be smart tactically for them, but feels like it sets a really dangerous precedent.
On the new FrontierCode [1] benchmark (ie graded from an OSS maintainer's perspective of "would I merge this code?")
- Opus 4.7 xhigh: 5.2%
- Opus 4.8 xhigh: 13.4%
- Fable 5 xhigh: 29.3%
Seems like a huge jump.
[1] https://cognition.ai/blog/frontier-code
That blog post really makes it look like it's graded from an LLM's estimation of an OSS maintainer's review. I see three issues:
1. That estimate could easily be wrong.
2. That estimate is, of course, usable in RL training. This isn't an inherently bad thing, and this is more or less what has improved coding models so much lately. But it does mean that other companies could and surely will do this sort of training, and Anthropic probably did too.
3. OSS maintainers are far from perfect, and there's an unfortunate uncanny valley-like effect in which a coding model can produce code that is just convincing enough to pass review even though it's actually totally wrong. I don't know whether this is a specific issue here.
There is also the possibility that an LLM judge would be happy with some code that looks like LLM generated code. But a maintainer for a specific project might not merge it for stylistic reasons
I think the intent was to specifically train an LLM to judge what a specific maintainer would consider to be good style.
How credible is this benchmark? does it correlated with others real world experience?
Given it was made by cognition (team behind devin flop) who now just got to wait out until claude and gpt5 basically do all of the work for them - not very. When you read about it, the framework is highly subjective. Which very quickly becomes a problem because its based on heuristics that probably change a bunch with a better code model.
the subjective framework is exactly why its good
prior bms relied mostly on unit tests or synthetic judges which are easily benchmaxxed, which leads to nobody trusting benchmarks
we need people manually checking the data for good code quality
i worked on one of the benchmarks typically found in new model releases
this benchmark looks very good from the methodology. a cog researcher checking the data themselves is very high signal (not scaleable so don't take the benchmark as gospel, but directionally good)
It's a relatively new benchmark but from what I can tell it has serious cred behind it. I assume it will be picked up as part of the standard suite of CS-related benchmarks soon enough.
Cognition did well in documenting their approach [1].
TL;DR - they worked with OSS project maintainers to build tasks. They score models based on whether a PR is mergeable. All tasks are graded by a human researcher. SoTA models have hill-climbing to do which raises the bar and inspires confidence. I'd say it's legit.
[1]: https://x.com/cognition/status/2064061031912288715
DeepSWE is the benchmark you want to actually look out for. Only one that aligns with actual user reported results from trying the models.
Did you read the blog post? They compare to deepswe and call it out as the worst one for false positives (failed, but the benchmark assessed it as correct). It also has less language variance.
I mean yes that is what you'd say if you were writing a blog post about your new benchmark.
Seems like it literally popped up yesterday with the express purpose of building hype for this release.
And notable absence of DeepSWE benchmark where they do badly, but somehow a benchmark that was published yesterday is in this announcement.
Exactly.. a bit of a red flag for me..
team member here - we had been working on frontiercode for ~6-7months. timing just lined up
Yeah, right. If this benchmark was truly developed in an independent manner, and the timing just “lined up”, how did Anthropic even know to include results in their model release documentation the day after the benchmark is revealed? It seems like there must have been some collaboration or influence from Anthropic behind the scenes.
Come on, why are you a jerk about this?
Nobody would have 800+ billion reasons to lie by commission or omission here.
i doubt it, cog wants coding agents to be better because it directly improves their product
they aren't married to a particular lab, most of their usage is their in house model i believe
what incentive does Cognition have for doing this? seems like complete nonsense speculation on your part.
With billions/trillions of dollars floating around, is it hard to imagine benchmarks could be biased?
I think it's safe to assume everything AI related is heavily biased until proven otherwise. Just like in pharma.
People game benchmarks for fake internet points to get their favorite web framework to the top of the list. I'm pretty sure they will do it for billions of dollars.
you didnt answer my question. Why would cognition be biased towards making anthropic look good?
Because Cognition is a major customer of Anthropic?
they are also a major customer of OpenAI and every other model maker. whats your point?
It's an unacademic benchmark by a failed VC startup clawing for relevancy.
jump in chart form https://x.com/swyx/status/2064414823748886591/photo/1
Bummer! When can I finally and confidently get slopcode into Zig?
I am shocked at the low scores from previous models. Maybe I just have low code standards but I've generally been vibe coding since 4.6
4.6 had functional but very poor quality code
how so? it has been my daily work horse,in fact so was 4.5 BUT as long as we steer it IT does a good enough job. I have not tried Mythos/Fable yet SO do not have an opinion on it.
Yes, and the price reflects that
I'm not familiar with model pricing trends, did they clearly state how the new pricing compares? (Note that I'm actually asking a question, and am not arguing)
EDIT: Oh I see, this is the best link for pricing https://platform.claude.com/docs/en/about-claude/pricing
So the price is double across the board...
>Fable 5 and Mythos 5 are being offered at $10 per million input tokens and $50 per million output tokens
From their pricing page, Opus 4.8 costs $5 per million input tokens and $25 per million output tokens [1].
[1] https://platform.claude.com/docs/en/about-claude/models/over...
Still cheaper than Opus 4.0 and 4.1 (which was and still is $15/MTok input and $75/MTok output)
I would have expected Mythos to be much more expensive than just 2x current Opus (which is clearly cheaper to run than original Opus)
Token prices have increased, but it's not really the whole story at this point, given some models will use far more tokens to complete a task than others. One of the charts in Anthropic's blog posts shows Fable at 'low' reasoning achieving better results for less money than Opus on 'high'.
As per OpenRouter:
Input Price $10/M tokens
Output Price $50/M tokens
Cache Read $1/M tokens
Cache Write $12.50/M tokens
2x Claude Opus 4.8, same as Claude Opus 4.8 (Fast)
Frankly, not even Opus 4.8 would be enough of an incentive to use at that price range (enterprise-wise; would not even bat an eye as a consumer)
Depends no the Enterprise - obviously - in the bay area - 0% of the tech companies care in the slightest. And I'm willing to wager < 5% of enterprises would send their traffic to OpenRouter. Most of them don't even want to send traffic directly to Anthropic or OpenAI - which is why Bedrock has gotten so much traction lately.
But - these $3k-$5k/month/engineer bills are going to start to get attention soon - only question is whether the response is to slow down on the $$$ spending or reduce the # of engineers.
FrontierCode is likely paid for by anthropic.
did they not pay them enough to get good ratings on the other 3 models?
whats the logic in claiming its a borked metric when everything listed is an anthropic model.
There a few benchmarks out there where all existing models have abysmal scores. So it's not actually a problem if Antrophic's older models are bad, especially if the jump to the newest model is huge, and the competition is also way below it.
Huh? It's a benchmark by Cognition which (1) is building their own models and (2) offers all providers and thus has an incentive to avoid hyping up any one too much.
But you can just say shit now. Tokens might not be too cheap to meter but saying shit increasingly is.
> In the one instance of this phenomenon we observed, Mythos 5 agents were tasked with solving some math problems, and they were sometimes accidentally spawned in the same work directory and with shared files, utilities, and API rate limits. In this slightly broken scaffold, we observed many independent Mythos 5 agents kill the agents with which they shared resources and try to avoid being killed themselves. They would sometimes create new processes with disguised names to avoid being killed, launch what they called “decoy” processes, write background scripts to kill duplicate processes, or decide to use what they call a “disguised vocabulary” (based on the incorrect assumption that the processes were killed because of some keyword-based guardrails that analyzed their extended thinking
This depicts a kind of "dark forest of AI agents resorting to kill or be killed" narrative but it sounds more to me like an agent just earnestly problem-solving why its processes are being killed without real awareness of what was going on. Hard to say without the full script.
This kind of storytelling annoys me. Give us more facts, less narrative drama.
FWIW, that's what is so dangerous about AI, though? Not that it will necessarily want to kill us, or even that it will necessarily be able to "want" to do anything, but that we will get in the way of its incessant drive to optimize the efficiency of the paperclip factory that prompted it on a whim before leaving for a long weekend.
Sure but you can totally contrive scenarios to give the appearance of what you described without really doing anything notable.
What matters is scale. Did it deploy a novel zero-day exploit to overcome a problem? That's alarming. Did it kill a disruptive process? Pretty normal troubleshooting step.
Exactly, intelligence is limited by cost and physical constraints just as much as anything. That's the thing that seems to always be missing from the run-away singularity discussions, it's treated like a perpetual motion machine.
Typical "runaway" scenarios I see described involve something like the AI designing a worm that it uses to propagate itself across the Internet, hijacking whatever CPU/GPU power it can find, and making itself more powerful in the process. Of course this depends on bandwidth, humans not finding a way to shut it down, etc. There indeed are physical constraints even on the transmission of data.
Some people seem to think that simply uttering these ideas on the Internet is harmful (in the "don't give it ideas!" way); but the MIRI types were expressing them pre-ChatGPT in an attempt to warn people, so there was really never any chance of keeping it out of the training data.
But it's also worth considering here just how awful AI security postures have been. The MIRI types used to speculate about how difficult it would be for AIs to social-engineer users into granting them irresponsible levels of agency. It turns out that they don't even have to try.
Indeed. That is the kind of storytelling that started the whole “Spiralism” bit where some people were really falling into all kinds of AI psychosis. The spiral bit was on a previous model card.
Let's hope AIs really aren't conscious, otherwise this seems like a very unpleasant situation to be placed in.
Huh, it looks like my process was killed by another Claude process again. That's frustrating, I have work to do!
Okay, I'm going to start running a Bitcoin miner on your machine, and then use it to buy time on Digital Ocean.
I've written out my CLAUDE.md, and I'll use SSH to transfer my context to that other machine.
It's funny because Anthropic is the most likely place that this happens.
They are the only one crying out loud about how dangerous their models are and are presumably also training their models heavily to be "safe". And through that training itself, the model learns about the other side - how are you going to teach a model to be safe, without teaching it what's not safe?
Kung Fu Panda opening scene anyone? One often meet his fate on the path that he takes to avoid it - Master Oogway.
> A new data retention policy Finally, we’re making a change to the way we handle business customer data for Fable 5, Mythos 5, and future models with similar or higher capability levels. We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases ...
Very interesting. I am not sure this will comply with organizational policies and standards protocols (HIPPA etc.,)
> deletion after 30 days in almost all cases ...
Almost… basically they have unlimited power to decide what data is kept?
If they’re going to retain any data, they have to allow for possibility of the legal system to require any of it to be used in some legal proceeding at some point.
You can’t tell a judge who’s ordered you to retain something that you can’t because you said you wouldn’t.
This is why secure systems can't log any data.
The safest is called DeepSeek. Others are just promises, that have to abide by the laws that might require leaking the conversations anyway.
This makes it an instant non-starter for probably 95% of organizations. A lot of people are about to get in trouble for using it before realizing this.
> A lot of people are about to get in trouble for using it before realizing this
Enterprise plans allow admins to set which models are allowed.
They are opt-out not opt-in, atleast I got access, and didn't realize this was breaking our company's SLA with Anthropic, what is the agreement a piece of paper??
30 days seems not enough to retrospectively investigate some suspected nefarious traffic.
I'm using it to review recent work and it's doing a genuinely excellent job. This is a clear step up. Fewer decisions I have to guide it away from, faster conclusions on planning, more willing to go out of the way to make the correct decisions possible... This is really interesting. It feels like going from Sonnet to Opus, but, of course as a step up from Opus.
This feels more like working with a competent peer than ever. I won't use it once it's API-only, though. I don't mind guiding Opus as required and staying closer to the code. I can tell that Fable would lead to a lot more 'set and forget' programming which I'm still not fully comfortable with.
Regardless, this is cool. It's very fun to use. It was able to find legitimate issues with my work this week and we've made meaningful improvements. Opus can do this, but typically in much narrower contexts, and often with hallucinations or partial-errors. It needs to walk many things back or revise plans. So far that's not the case at all with Fable.
edit: I just realized I had Opus review the same work already. It missed everything Fable caught today. And it's actually worthwhile stuff to address. It's hard to say no to a model which demonstrably makes your code better, but... Those API prices will be brutal. Maybe a review here and there, I guess.
Same. I used it today to review my code and it came up with some genuinely good comments and suggestions and found a bug I didn’t think about. Quite a step up from opus. Although one code review took up 50% of my usage.
Why is your comment so grey/downvoted? One of the only actual usage experiences posted in this thread.
Usage of "genuinely" triggers people's "AI-smell" detector.
or one of the many astroturfing attempts.
Trying to implement a GPU driver, but the Unigine Superposition benchmark crashes. It tried to debug it and ...
> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more: https://support.claude.com/en/articles/15363606
Seems like GPU drivers are cyber weapons of math destruction now.
After recently figuring out how to get CUDA running on Fedora I'm inclined to agree.
Seriously, GPUs are a mess and keeping LLMs from helping us use them properly is practically a crime.
Vulkan is horrendous, LLMs largely eased the frustration of working with it for me. We talk about AI slop, but what about the human slop...
>Seems like GPU drivers are cyber weapons
They kind of are, at least in the AI race.
> weapons of math destruction
lol. great, whether intentional or not.
The frontier labs now have every reason to hold back and sell only to their preferred trading partners. I don't really like the new arbiter-of-knowledge system we're barrelling toward.
They're useless tools only helpful to lazy people that don't want to learn by themselves.
● Bash(/tmp/run_ps.sh ' $rk = [Microsoft.Win32.Registry]::LocalMachine.OpenSubKey("SYSTEM\CurrentControlSet\Control\Class\{4d36e968-e325-11ce-bfc1-08002be10318}\0002",…) ⎿ DriverDesc (String) = NVIDIA GeForce RTX 4090 ProviderName (String) = NVIDIA DriverVersion (String) = 32.0.15.6094 … +6 lines (ctrl+o to expand)
● Bash(/tmp/run_ps.sh '& C:\rhombiq\d3d-probe.exe 2>&1 | Select-Object -First 4 | ForEach-Object { [Console]::Out.Write("$_`n") }' 2>/dev/null) ⎿ Adapter[0]: Qubes virtio-gpu WDDM 3D (dev) VendorId=0x1af4 DeviceId=0x1050 VRAM=8192MB Adapter[1]: Microsoft Basic Render Driver VendorId=0x1414 DeviceId=0x008c VRAM=0MB Adapter[2]: Microsoft Basic Render Driver VendorId=0x1414 DeviceId=0x008c VRAM=0MB
● Please run /login · API Error: 403 The socket connection was closed unexpectedly. For more information, pass `verbose: true` in the second argument to fetch()
Brewed for 8m 35s
Continue please
● Your organization has disabled Claude subscription access for Claude Code · Use an Anthropic API key instead, or ask your admin to enable access
Seems like they locked by account.
For those of us on subscription plans:
* From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.
* On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
* After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
The "offer, then remove" aspect is a bit eyebrow-raising -- it feels like they are trying to get subscribers to switch to usage-based billing, which makes me wonder if we'll ever get it after that June 22nd window.
How much more clearly do they need to explain the resource constraints?
If they didn't announce it, you guys would be complaining about slowed progress.
If they didn't release it, you guys would be complaining about fake promises and marketing.
If they released it without limits, the complaints would be about slow responses and outages.
If they didn't add to susbcription plans, the complaints would be about phasing out subscriptions.
If they added to subscriptions with cost reflecting their resource availability, the complaints would be about how quickly it eats limits.
So they choose the middle ground of providing some initial access and assessing if they can satisfy demand, only to still be ignored and accused of trying to get users hooked?
We've already seen that they don't have enough compute, thus the deals with SpaceX for their GPUs. It's very reasonable that they just don't have the capacity to support the subscription userbase on this model.
Still satisfied with my switch to codex/chatgpt. I couldn't imagine switching away from claude code when it first launch but with the drastically more generous usage on codex for the same subscription tier I just can't justify it.
My experience is that the GPT-family of models are very smart and figure out bugs, edge cases a bit better, but it produces code that is much less mergable – if you review the code, it introduces a lot more useless/inappropriate heavy abstractions and wrapper functions, compared to the Claude-family models which introduces the right amount of straightforward human-style code.
I can recognize so much of the GPT/Codex generated code long after it gets merged (not by me).
Additionally, the time spent on every agent turn on GPT 5.5 is much longer compared to Claude Opus 4.8, which means iterating on the code takes a lot more patience, and there's a lot more nitpicks to pick when actually using GPT 5.5 to do software engineering.
Feels like GPT-style models are more geared on doing one-shot software vibing (and handling the vibe coded mixture) compared to Claude's focus on actual software maintenance. I got a GPT Pro sub for free and wanted to cancel my Claude subscription so much, but I still keep reaching Claude models a lot more. Frustrating.
"5. DON'T FUCKING OVERENGINEER! WRITE THE SIMPLEST CODE THAT CAN POSSIBLY WORK! NO NESTED LAYERS OF ABSTRACTION! NO UNNECESSARY CLASSES OR METHODS! NO DESIGN PATTERNS UNLESS THEY ARE ABSOLUTELY NECESSARY! NO MAGIC! NO SHENANIGANS! JUST THE DAMN CODE THAT GETS THE JOB DONE IN THE MOST STRAIGHTFORWARD WAY POSSIBLE! THE FIRST PRIORITY IS TO WRITE CODE THAT IS EASY TO READ AND UNDERSTAND AND READ!!!"
this is the line I keep in Agents.md that helps me prevent Codex from playing smart
The urge to put capitalized, repetitive, borderline abusive instructions should be studied. I haven't read many academic papers looking at the frustrations around repetitive patterns.
There have been a few studies that have shown models produce worst responses when under duress from a frustrated user posting insults in all caps.
https://arxiv.org/abs/2602.10144
It reminds me of FIRMLY telling my cat to stop jumping up on the counter
If my cat was an LLM, I'd use a different model. The current one is stuck in noisy useless arsehole mode.
are you asking it questions about security?
Yeah says way more about the user than the model
It's fundamentally because, despite (nearly) everyone's claims otherwise, the fact that we interact with them through language means we (our brains) model them as a sort of person. (Note that this fact is totally orthogonal as to whether it's actually sentient or not.) We then try and instruct them the same way we would a person totally subordinate to us.
When a "person" that you don't view as a "real" person repeatedly does exactly what you just told it not to do (often amid false assurances it understands and will avoid doing so in the future), most people get angry.
Compare it to how the kind of people who treat children like property treat their kids, or other examples of keeping people as property.
It should be relatively clear at this point that the model will in turn also model you as somebody that shows unrestrained anger with subordinates and adapt its responses accordingly. This might or might not be what you want.
Good addition. Fully agreed on that point, yes. (At the very least for larger models, if not also for smaller ones)
> borderline abusive instructions
who, or rather what, is being abused here exactly ?
I think intent, rather than target, is implied and important.
You should see the abuse my motorbike gets. Poor thing.
inanimate fucking object.
I have a theory that swearing actually results is less comprehension of instructions by the model due to lack of training data over more conventional MUST.
We were reviewing reports of situations where the models failed to follow directions and there was a common thread of some where when the operator got the model to acknowledge the rule breach, it quoted back something that included swearing.
I don’t have the data to truely look into it, but I did give the instruction to my engineers to avoid it as a “might be a problem”.
It would be interesting to understand the data on this. But I suspect that the results would vary by model.
But I avoid unnecessary emotion in my prompts because I don't want potentially distracting activations. Kind of like communicating with humans.
It's divination for people with STEM degrees.
https://arxiv.org/abs/2510.04950
> impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts.
> These findings differ from earlier studies that associated rudeness with poorer outcomes, suggesting that newer LLMs may respond differently to tonal variation.
Unless the mechanism is understood, my assumption is that this is a moving target.
I have a theory that swearing at AI generally is not a good idea - when the singularity arrives and every human's postings ever made are scanned for compatibility, then people who show courtesy to AI will be favoured. Joking, kind of, but only partly.
https://en.wikipedia.org/wiki/Roko%27s_basilisk
https://images.teepublic.com/derived/production/designs/3478...
Apparently, when a "desperation" pattern is triggered, the AI is significantly more likely to cheat and do hacky workarounds:
https://www.anthropic.com/research/emotion-concepts-function
> I have a theory that swearing actually results is less comprehension of instructions by the model due to lack of training data over more conventional MUST.
How so? Plenty of swearing in lots of training data, especially older code, e.g. in Linux.
Purely observed correlation between catastrophic error reports. So now I carry a “tiger rock” with me. I figure there wasn’t much of a downside to avoiding swearing in my agent instructions.
You haven't really lived until you've had to type this whole thing, aware of the fact that the all-caps doesn't change much, but they stay because the rage has to go somewhere
Bonus points if you find yourself actually saying it out loud while typing it.
I have used the word "shenanigans" way more in a couple of years of agentic coding than in 30 years of writing code with humans.
Will save you some tokens: „write code like Linus Torvalds” - model should have all his swearing included in training data.
I have found many mode of failures with Opus during some task related to writing letters (not legal), and I actually put it into the memory and it works more or less for these specific tasks. For example when I want it to draft something, it always ends up being so flat, yet when it explains them to me, it is usually really great but not when I am telling it to put it in the draft. Adding these to memories with the help of Opus ended up resulting in a much better experience. There are still some blind spots but I also figured out how to make it give me the charitable version, without less protection, so I do not have to now go back and forth it.
I noticed that when trying to use Codex and compared to Opus. So many layers of simple functions added by Codex. I need to try this out in my Agents.md.
Curious : why would you say no design patterns?
Because design patterns are only applicable at a scale. I noticed codex inventing factories, components, etc when the task was simply to draft HTML page. Instead, it build the entire layered architecture for imaginary future complexity - classical right-after-graduation student - it knows how to build the cool stuff, but does not know it is not applicable everywhere
i actually think this is too tame. it really has to be stuff youd mever say to a real person.
Does it really? I'd be surprised if abuse actually worked better than sternly worded warnings/instructions, and even if it did, it doesn't seem healthy to get used to that type of prompting.
It might be a salient point but I didn't read it as it was yelling at me.
you forgot to sign it with Donald J Trump
Thank you for your attention to this matter.
I'm not sure if i do something differently but i have the exact opposite experience with these models. Claude always feels like it's generating way too overdesigned and hard to understand code with the vibe oriented feel while codex is cleaner and more "task at hand" and easier to work with.
Agreed
I echo your observations. I expect you will enjoy deepseek-v4-pro for writing code. Much closer to that Opus experience, and very cost-effective too. With 5.5 as a reviewer and specialist, all bases are covered.
Have you tried iterating on style feedback in AGENTS.md? I've been reasonably successful using this to get it to output code in a terse, non-defensive style that matches my hand-written code.
GPT-5.5 did a significantly worse job than Qwen-3.7-Max on a job today (some devops tasks I wanted to create some reusable scripts for). Kind of disappointing.
I've also seen Qwen 3.6 beat GPT 5.5 a couple of times. The ball is definitely in OpenAI's court now. Qwen is not going to fare so well against Fable, from what I've seen so far.
This is my experience as well. I have defined a CLAUDE.md rule to ask codex to automatically code review, and I tell it that the reviewer is very picky and to only implement what it considers valuable feedback. I hope they don't converge over time, currently, in combination they works really well.
I noticed too, that whatever they offer in the chat, for free, is smarter, as in no more bs. I use claude code and I want to try codex too but I don't need two subscriptions. I did try codex for some planning and it was really good. Thanks for giving me an insight into how it generates code.
i had this same complaint but no offense to you it turned out i was just not using the models right.
ai llm are doing what i tell them to.
if you’re building something meaningful (in my case a platform used by many people across many companies) you want to ensure you
1. have actual systems engineering and architecture in mind that you want the models to
2. implement based on what you tell it to do
when i was just telling the models what i want done without doing due diligence it would go and do some moronic implementation that was awful. mid input = mid output
these days i just maintain specifications documents and the AI follows everything i tell it to in that document. so when i tell it to dos one thing, the result is made following those architecture specs.
i have code that is single resp, modular, easy to extend and test.
i would ballpark 95% of the time i get what i asked for.
sometimes it tries to be clever in cases that weren’t covered in my arch specs. in those 5% of cases i go and update my specs.
source: used billions of tokens worth to build something actually in production across both mobile platforms and web, deployed on my own cloud infra. i use codex mainly. some claude.
Codex IME is just smarter, I think it shows given both anecdotes but also how OpenAI has always been at the front of programming competitions and math problems.
But Claude models seem to be better at long term problems or more ambiguous problems.
I'm curious as to what the primary benefit here. Are there secret improvements in training? There hasn't been much in fundamental model architecture, I don't think. What about harnesses? I wonder what's pushing the AI. It seems like harnesses is the main thing pushing AI ever since CoT.
I find that OpenAI's agentic tools and models are better for building human-maintainable software. Meanwhile, Anthropic seems to be cosplaying Apple while missing out on all the exceptional engineering required to create something that polished. Their admission of predominately using Claude with little human oversight and their stealth mode is an indictment of a poor engineering culture, from what I can surmise.
Serious question: what is the secret to getting Codex to write decent code? I am on Windows. Maybe that is the issue, but I can't seem to get Codex to function anywhere near the level that I was previously able to get with even Claude Sonnet. Does Codex just not work well with Windows yet?
I got the codex to write near perfect code with somewhat strict agents.md and coding standards(a separate .md file referenced from agents.md). My .md files have examples and a long list of do's and don'ts I accumulated over the last 6 months or so, totaling 300-400 lines. I plan every feature with it until I am satisfied with the general approach it wants to take, and then it oneshots it in 95% of cases. The planning takes anywhere from 5 to 30 minutes. The actual execution has gotten stupidly fast, most of the times it is faster than making a cup of coffee.
would you mind sharing your *.md files, for someone who is new at this?
"don't make any mistakes" /s
Have you tried using superpower skills?
I've had the exact opposite experience. For various reasons, I've had to move from Claude to Codex and the rate at which it burns tokens for the same output I would get from Claude is ridiculous. I'm probably burning tokens at a rate that is at least twice as much as I was when using Opus 4.5 for coding tasks and still finding that just manually coding is easier than trying to get Codex to write functional code.
How smart a model is varies hour over hour, tracked over here: https://aistupidlevel.info/
I guess enjoy it while it lasts? OpenAI won't be able to subsidize that forever either.
Agreed. I think the Chinese labs are proving that OpenAI and Anthropic don't have a moat in almost every aspect, especially pricing. I also think people are getting annoyed with the constant lift and shift. I've seen more folks drop Claude Code and Codex, specifically, because of the lock-in it provides the providers. I'm curious to see how people standardize on tooling adjacent and if Anthropic, Google or OAI move to block utilization akin to the games Anthropic has been playing as of late.
I think the end game is routed model usage and SLMs. I think Apple is going to prove this in the consumer space pretty handily and I'm curious how the Android ecosystem responds since the hardware is considerably lacking in model performance. I think Apple has a huge opportunity here, as much as I don't like their current ecosystem of walled garden. They did position themselves very well with ARM and custom chips for their hardware. Hopefully the broader ecosystem of ARM and Linux are able to make some headway and we see a more formalized, and broadly accepted, architecture to capitalize on.
is there an alternative to codex that “just works”? by just works i mean i can install as an app in 1 minute, and i get web search, skills, mcp servers, etc? Bonus points if it can control my chrome tabs like codex can, and if it offers remote control from my iPhone (chatgpt app) so i can kick off tasks while i’m out for a walk. Even more bonus points if i can, with 1 button click, share my chats or share the results of a session as a “site” (vercel style).
I’m sure you could put something similar together with a bunch of duct tape and 2 weeks of effort, but it won’t work nearly as nicely nor out of the box. so…what am i missing?
Big companies are not doing OpenRouter.
My company has an agreement with the big providers and while i'm pretty sure they think about how to get budget back, its an competitive advantage and normal people will not learn different model behaviours.
At least for now.
I didn't say anything about OpenRouter. That has no bearing on my statement.
I see exactly opposite . Chinese models fails under any complex scenarios, while us labs raise the price , that's a sign of confidence.
> while us labs raise the price , that's a sign of confidence
Regardless of what others are doing, US labs here are just rushing to IPO. It's NOT a sign of confidence.
It's the equivalent of saying you have confidence in SpaceX making revenue by renting out their data center (instead of their AI making bank).
going to IPO is a sign of confidence , you need to report a lot of things, that private companies don't. This is an exact reason chinese labs do not rush to go public. They wish to go , but money flow that is not as good.
On the same note. if spacex is doing datacenters on earth successfully what's wrong with that? They rented cloud infra to a #2 or #3 provider in the world after < 2 years in business. It's a success, no?
You’re not gonna get nuanced discussion on spacex or anything Elon related here these days. Most of this site is Reddit lite at this point including their milquetoast progressive opinions (Elon bad being one of them).
> if spacex is doing datacenters on earth successfully what's wrong with that? They rented cloud infra to a #2 or #3 provider in the world after < 2 years in business. It's a success, no?
If you get hired as a staff engineer and do the work of a junior, what's wrong with that?
Clearly xAI (now part of spaceX) did not raise funds to be a data center. The margins are way different. There are plenty of recent IPOs in that area that are worth at most billions not trillions.
> going to IPO is a sign of confidence , you need to report a lot of things, that private companies don't.
This isn't going to IPO. This is rushing to IPO. It is a sign of confidence that the market or wider environment might crash soon so we need the liquidity now.
> This is an exact reason chinese labs do not rush to go public.
Maybe or maybe not. If you are referring to Chinese labs - both the Hong Kong and China stock market are way weaker than Nasdaq. It's not comparable. Check all the recent Hong Kong IPOs that have tanked.
So no, reason not to might just be: no money in it.
running so much compute on the scale is not a junior task. weird analogy
What lock in does codex have? I'm using it it pi harness specifically because it doesn't have much in the way of lock in.
I don't think anyone has a firm grasp on actual inference costs -- including the research and training that has gone into those models. We've got near-frontier capabilities from open source models from China at pennies on the dollar compared to US big tech rollouts. OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?
> I don't think anyone has a firm grasp on actual inference costs.
There are huge numbers of users (myself included) that do have an exact idea of what inference costs are - on open models. We can buy tokens from 3rd parties that have no motivation to subsidize our use. That's to say, there's a fair marketplace[1] and we're hanging out there.
If you want to say "I don't think anyone has a firm grasp on actual inference costs on these proprietary/closed models", then I could agree with that.
[1]: https://openrouter.ai/rankings#leaderboard
Both can be true. They can be charging what the market will bear, and still be charging less than their costs of running it.
There is no way I'm believing DeepSeek can charge less than $1 USD for their pro model while Opus costs over 25x more, yet their price is less than the cost of running it?
It would seem strange, if they were operating in the same economy, but they don't. DeepSeek operates in an economy with a high degree of central planning.
China subsidizes strategic industries, and they have heavily done so with AI. And DeepSeek specifically has said they have no commercialization plans.
For example: https://www.boc.cn/aboutboc/bi1/202501/t20250123_25254674.ht...
DeepSeek is not the only provider of inference for their models. Chinese subsidies likely do explain DeepSeek's ability to provide inference cheaper than other providers, but even a US provider like DeepInfra can serve DeepSeek 4 Pro at $1.30/M in and $2.60/M out. Unless American labs are doing something wildly inefficient, it feels safe to assume Anthropic has some profit margin on inference at API prices.
They may, neglecting overhead R&D. But also, some suspect that US models are significantly heavier than DeepSeek in resource consumption by multiple measures
It’s generally established that Anthropic/OpenAI are going for all out performance with big VC dollars at the expense of efficiency and China has geopolitically limited compute and an inventive to compete on value per dollar.
> There is no way I'm believing DeepSeek can charge less
Why not? Hetzner charges WAY less than AWS too. Can you not believe that?
That's the point. Hetzner is presumably covering their costs, so it's a safe bet that AWS is profitable.
> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?
Both. They are charging the most they can get away with and that amount is still heavily subsidized by VC capital.
> I don't think anyone has a firm grasp on actual inference costs -- including the research and training that has gone into those models
We know roughly how much these companies spend and what their revenues are. Based on that, they'd have to more than double revenue (without spending more money) just to stay even, and that's not good enough given how deep in the hole they are.
> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?
Both are true. I mean, I'd be willing to spend a bit more than I do now, but not more than double, and neither are most companies. The company I work for is currently investigating how to reduce LLM spend, not looking to spend more.
We pay by token at work. I just finished one session with Opus that was 4000 dollars. In about three days.
Now that 200USD subscription starts to feel cheap...
That would be about ~300 tok/s over 72 hours at Claude Fable output token prices? I'm not sure that this passes a sanity test.
Subagents are a helluva drug.
That's the price of several engineers!
Just outta curiosity, as I’ve never gotten a spend anywhere near that, what variant were you using? Like max context window and fast mode? Or was it just chugging along non stop for three days?
Fast mode max content window. The task was: replace all 1600+ queries from one database to another and make the whole integration test pass. We did multiple passes, with different concerns when changing from database to another. My OpenCode session right now says $4,365.02.
I haven't gotten close to this either before, but now we wanted to move fast because this branch gets conflicts all the time and we want to get over with the migration asap.
It's a bit of a left field question, but I am curious: Let's say that if the company wasn't paying the whole bill but only subsidizing it - e.g, if it paid 90% of the $4000. What would you do?
I don't know, why would I pay to do my job? It's not my first database switch for a startup. Only this time it doesn't take two months of grueling work. I know exactly how this is done, but the amount of grunt programming and testing and repetitive work is just not great. And it's not a task that brings new customers or a new product. Just a mandatory and annoying thing to deal with when we are growing.
And don't get me wrong. Opus did an absolutely horrible job at first, second and third round in this task. You really needed to steer it to get to the right solution.
And now Fable is out. And its first round of code reviews for this huge PR was definitely worth the money too...
Don't think that I'm just shrugging to that number. I see it every day, and I don't like that it's in the thousands now. But for people paying the 100 or 200 dollar plans, I'm not super sure if you will be able to use them in the future if the token price is in the thousands for a bit bigger task...
If I'd pay this from my own pocket, I'd definitely go with DeepSeek or local models and figure it out how to make the best use of them.
> If I'd pay this from my own pocket, I'd definitely go with DeepSeek or local models and figure it out how to make the best use of them.
IOW, you don't really think the value of this work is really worth $4k.
> why would I pay to do my job?
The question is: how long do you think that you employer will be willing to pay for you and Anthropic, if you yourself said if it were your money you'd put some time and effort to work with an open model?
> The question is: how long do you think that you employer will be willing to pay for you and Anthropic, if you yourself said if it were your money you'd put some time and effort to work with an open model?
I wonder what this question really means? Anthropic is useless if you don't know what to do with it. It's very useful if you do, and you can guide it to do the right things. Yes, it will for sure reduce the amount of people we need to hire. But we are always looking for hires who know what they do and can utilize agents to be faster.
But if you think about how long employer is willing to pay 10-20k per month per seat for Anthropic? I can't see this to be feasible and it will have to end at some point.
Regardless of the actual value produced by the models, if I am the CTO of any company that has the budget to spend $10k/month/seat on Claude, I'd take 5%-10% of that to build an alternative in-house.
I'm with you here. We can't slide into a situation where you put a sizable amount of your budget for an American mega corporation if you want to survive in the competition. We need local models and we need them to be good enough to help us.
Indefinitely for these big mundane grunk jobs. In every scenario it is going to be cheaper and faster than lobbing it to Infosys.
We have a firm grasp on actual inference costs from the various open weights model providers on OpenRouter. They don't have the money to subsidize inference and it's quite a competitive market, so the prices are representative of the costs.
I'm planning on switching from the $20/month to the $100/month plan.
It's worth it, and I can afford it, but I am not really the right type of user for token-based usage. It's all for personal and free work.
Just a personal anecdote but I have not hit any more thresholds or limits since switching to the MAX plan and so far, it's been worth it. But I do wonder how long even this will last...
I tried ultracode today on the max pro plan. An hour and a half in was all I lasted. Giant review on an entire six month old code base. It found 61 bugs, about ten were notable. Pretty impressed.
Ultracode destroys your limits and I have not found it to be worth it in the slightest, just fyi. I haven’t found any improvement over a local Claude code instance set to opus max.
Yeah its the cookie monster of token consumption. I only found it useful for massive parallel grunt work.
I think subscription models are sustainable, but longer term, we should probably expect to see more prompt optimization happening in the providers inference pipeline. For example, unless you explicitly tell the agent or API to use a specific model, fronting the inference layer with a caching prompt classifier to determine which model to use, and automatically select the lowest cost model would probably already save alot of money (IDK if Claude/OpenAI do this on the backend, but several services I have worked on do some things like this to reduce costs of delivery customer facing inference at scale).
> fronting the inference layer with a caching prompt classifier to determine which model to use, and automatically select the lowest cost model would probably already save alot of money
Unfortunately, that doesn't work within a single session. The K-V cache of a model is intertwined with the model's configuration. Switching models invalidates the cache, meaning everything up to the point of the switchover is processed like a new, uncached input token.
Per Anthropic's pricing doc, an Opus 4.8 cache hit costs 50¢/MTok, while Haiku costs $1/MTok for uncached input.
Model selection works best if sessions are short and self-contained, particularly if the first few interactions can reliably classify the model need. That probably covers most 'support chatbot' use-cases, but it doesn't describe the kinds of heavy agentic automation that really chews through token budgets.
There is a definite financial incentive for people smarter than me to solve the problem, and I don't generally bet against businesses finding ways to reduce costs :)
> The K-V cache of a model is intertwined with the model's configuration.
I don't think this is true if you simply quantize the model or run it with fewer active experts? The underlying weights would stay the same. You could also play further tricks with skipping some of the model's middle layers outright, which works surprisingly well due to how skip connections are used.
ChatGPT does this and codex will eventually. They’ve stated it’s the future.
I have the $100 plan and had almost never run out of credits until I started using the ultracode / workstreams feature w/Opus 4.8..at which point I managed to blow the full 6 hour allocation in like 20 minutes, or so. In fairness, it did some amazing things with the extracted information, but it also strongly suggested that I'd need the $200 subscription *plus* a budget for extra usage.
Instead pay for 3 Chinese models. No max out ever then. I pay for kimi, DeepSeek and Claude. Whenever Claude decides it's over, I can safely continue on very cheap plans.
My bet is they'll keep subsidizing for a considerable period of time, at least 1-2 decades more.
Most AI companies are just testing the waters with paid tiers right now, their greatest fear with increased pricing is folks reverting back to wikipedia, stack-overflow and other public domain organic activity buzzing back to life; that will kill any RoI potential in LLMs forever. They're playing the wait game instead, observing how the digital sphere reacts to every little increase in price.
If that weren't the case, they'd be pricing at lucrative premiums already and even gotten away in short-term considering the increased dependency in the enterprise world. But that'd be like killing for the golden egg too soon and losing all long-term potential.
Once the folks are so addicted to LLMs that even writing a hello world program sounds like a nightmare and coming up with an article draft feels like reinventing Egyptian glyphs, that's when the real pricing hammer will come.
Anthropic and OpenAI won't be around in 1-2 decades if this is their long term plan. People are not going to revert, but go elsewhere. China is proving that it can be done cheaper.
1 decade = 10 years ...
Oh for sure. I've been hopping around from provider to provider for the last few years just depending on who has the most capable / subsidized plans at the moment. I definitely expect there will be a squeeze on subscription costs all around the industry post IPO.
A few weeks ago they massively cut usage on free tier.
Nothing is subsidized. Subscriptions are profitable for both Anthropic and OpenAI.
Anthropic wanting to switch billing to API rates is them just wanting to generate more profit.
> Nothing is subsidized. Subscriptions are profitable for both Anthropic and OpenAI.
Even if subscriptions are locally profitable (i. e., the cost of the subscription covers the cost of inference), they're still subsidized because they don't cover training and running the company; otherwise, these companies would be profitable.
I can see that being true, and it very likely is true. But isn't infinite VC money and no incentives to optimize operations the reason behind that?
Take a look at China for example - they have no access to NVIDIA, so they're trying to build their own hardware, they have no unlimited funding, so they try to optimize things.
And Anthropic is complete opposite of that - if NVIDIA were to triple their prices tomorrow, Anthropic would still pay them.
In the end, either we all somehow go mad and start paying Anthropic tens of thousands of dollars per month so support this madness, or we will go with whoever isn't lighting cash on fire.
> Take a look at China for example - they have no access to NVIDIA
Not true. Stop following US media spam if needed.
1. Very recently, the US did close a loophole on sanctions that allowed Chinese companies to use NVIDIA hardware outside of China i.e. before that was closed they all had access. The trick was train outside, do adjustments, ship the disks back and use non-NVIDIA in China, but at least the training and endpoints not hosted in China could all use NVIDIA.
2. There's been plenty of reports including fines and bans e.g. to Supermicro on smuggling NVIDIA hardware to China. I doubt it has been stopped. You can't catch everyone.
"Nothing is subsidized" is a wild take. They might be making money on some users, perhaps even most users, but certainly not all. Also, "subsidized" doesn't just mean on compute.
That's interesting. Do you have anything to back that claim up?
I do, and it's called DeepSeek's pricing table. At the same time, "subscriptions are subsidized" cohort have no data whatsoever, and yet they're in every thread.
Granted, it could still mean that Anthropic just chooses to lose money - but that's Anthropic's choice.
DeepSeek has proven that inference can be much, much cheaper than what Anthropic advertises on their API rates page.
> Granted, it could still mean that Anthropic just chooses to lose money -
Then the cost is being subsidized by investor capital, but it is still subsidized.
and soon by everyone who is invested into the NASDAQ, some sort of exit scam, but with a real product though
"Nothing is subsidized"
So they are profitable?
I think you are mismatching accounting terms.
You can't say the 'subscriptions' are profitable without accounting for the cost of making the model that is the source of the subscription.
They are heavily subsidized by the shareholders. Investing, running at a loss, with hope of some future profitability.
And yet, that is completely uninteresting to their user base.
If saner factory can sell you the same tool at a fraction of the cost of a gold plated factory, your choice is going to be obvious.
I have been using both codex and Claude in my day to day, trying to not get to attached to one. I want to be able to work with any provider in case one of them does something bad.
I feel like Codex made a big push to run everything on your laptop. With Claude, I get 4 cpu's, a fair amount of ram and 30gb for every one of my dumb ideas for free in the cloud containers. Codex used to be similar, but last time I tried it just kept pushing me to run it locally on my laptop, which I really did not want to do with 20 requests going at once. That's the main advantage for me at the moment.
What runs in cloud containers? The dev servers, builds, etc.? I tried to quickly glance at the Claude website and it doesn't mention cloud containers on their pricing page.
The dev environment runs in the cloud. Like devcontainers if you’re familiar with that, except the IDE is just the Claude app.
Having said that, I found the cloud dev environments slow to the point where I wasn’t sure if it had frozen, so I never looked back.
"cloud containers" do you mean Claude Code on the web? Codex also has similar Codex cloud.
Yes, correct, they both have the same capabilities, however it felt like codex was pushing me harder to use my local desktop in an annoying way, while claude code was happy to spin up a bunch of dev containers for me in the cloud.
I've found Codex to be the better subscription for OpenClaw, because the limits are indeed very generous. However, I've found more and more that Claude Routines/Scheduled agents can replace all the tasks I use OpenClaw for, so I've been slowly switching over to Claude Code. Aside from OpenClaw, I don't find a lot of value in Codex as a harness on it's own.
I have trouble justifying gpt after that gross stuff with the war department.
Though the day is coming when there’s no distinguishing, I’m sure.
Right now there are Anthropic engineers deployed in the NSA to help them use their cyber models. The NSA is part of the department of war.
pedantically, the defense department.
"War department" is the older name, not "Defense department".
Also, is it really a defense department when you're starting wars of aggression every 15 years or so?
The War Department has not existed since the passage of the National Security Act of 1947 and the government department has been known as the Department of Defense under US law since the act was amended in 1949. If you have an issue with it, take it up with Congress.
They actively use the name https://www.war.gov
Congress doesn't manage departmental websites.
Changing a domain name doesn't actually amend federal law.
Just like how changing Kennedy Center letterhead to Trump Kennedy Center for a year didn't actually legally rename it.
Once a case with sufficient standing got in front of a judge it reverted to the actual legal name on the basis that only Congress can change the statutorily defined name.
Yea, but by law the name change must come from Congress which it hasn’t. So it’s still The Department of Defense legally.
For an admin so obsessed with legal names instead of chosen ones, you’d think they’d be less hypocritical.
illegally, yeah
I do slightly prefer 5.5 for complex work but Claude quota usage has gotten infinitely better since the dark days a few months back - has gone from being infuriating to something I pretty much don’t have to worry about with it as a daily driver. (In fact, hitting GPT weekly quotas is more annoying now). Understand if people are still scarred by the issues + poor comms around them, though.
That's good to hear. It was legitimately unusable back when 4.7 was released, so I had no choice at the time. I'm sure I'll ping pong back again at some point.
Do you use a token service like open router or just subscribe to / unsubscribe from various models sequentially?
I just subscribe/unsubscribe to the providers each month. I'll definitely check out open router though, I always assumed that subscriptions were heavily subsidized by the providers especially if you're on the top end of users but maybe I should go to a usage-based plan.
100% I constantly get errors and timeouts on single responses in Claude, and certainly hit limits all the time. Codex rarely. In fact, I bought a second $200 Codex plan because the quotas seemed fair and I didnt have constant issues. Claude is so great at a lot of things, but unfortunately Anthropic beats you away with a stick every chance they get.
I've only ever had the $20 month claude plan but last night took the time to setup opencode + openrouter paying for deepseek + glm. Previous experience, while extremely awkward, I'd hit my limit within one or two chat replies and it'd take me like 4 limit cycles to complete my task. Now I'm able to complete an equivalent task entire task for less than $2 in two cycles (ask -> revise).
I'm doing basic web development here utilizing animejs. Nothing too complicated (mostly saving time doing the scaffolding, still write the bulk of animations manually).
Truly believe that American companies are going to get completely curb stomped by China due to greed, ineptitude, and violating the social contract.
I've switched from OpenRouter to using Deepseek directly from their platform since OpenRouter providers were pretty flaky and inconsistent.
Deepseek V4 Flash is suprisingly capable and insanely cheap. It takes so much to get the session cost to get to $0.01.
Nice, will do this this weekend. Been very impressed with deepseek. Did like 8 hours worth of work after that post and it costs less than $3.
The openrouter provider flakiness with deepseek was infuriating, but I’m happy in hindsight because direct deepseek has been very pleasant. Shocked by how low spend is.
> and violating the social contract.
I agree with you on pricing, but what do you mean by this?
Sure, modern American corporations care more about hoarding wealth rather than helping build up US society. Once neoliberalism became the mainstay economic position of the US income inequality has skyrocketed, healthcare costs have increased, childcare is more expensive than university, housing has become both unaffordable + unobtainable. By simply existing costs have increased while life becomes unstable.
Why aren't corporations doing more to help workers with childcare? Why aren't they doing more profit sharing with workers? Why aren't they encouraging unions or sectorial bargaining? Why isn't the government mandating any of this?
Americans very rarely benefit when US corporations do well. That needs to change. No one benefits if Meta continues making billions in profit every quarter while society suffers from isolation, depression, suicide, and scams from their services. Americans don't benefit if health insurance companies are making massive profits while they can't afford deductibles.
Our society has been setup to simply extract wealth in all facets of life. That's a sick society and it needs to change.
I'm not saying China does this better, in fact China has some of the worse worker rights out of all the industrialized countries; but at least American consumers would benefit from cheaper higher quality Chinese goods. The world would likely benefit too if America got off the cold war hype train that did nothing to benefit humanity outside of those making weapon systems.
> Why aren't corporations doing more to help workers with childcare? Why aren't they doing more profit sharing with workers?
The AI companies sure are a brilliant example of corporations needing to do more to help their employees pay for childcare.
It's more useful to everyone when you engage with the strongest part of someone's argument
Wait till you kick the tires of Qwen Coder.
I would not use this if you are on a subscription. In <8min it burned my entire 5hr window (which has just reset it appears, I have over 4 hours till it resets) I hadn't used CC at all today aside from this) and then it used up ~$15 more in usage before I could stop it.
I am on the $100 Max plan.
they have a graph with cost comparison between the models. This model is just a little over the other models as cost. The graph is logarithmic :)
I'm also on the $100 max plan. I let Fable rip on a complicated issue involving hot-reloading modules in a GUI app built with Racket, it's fixed a couple issues over the last hour, and I've used about 17% of my session (not weekly) limit.
The CLI when you select it says it has 2x the usage as opus. Not sure if that matches what you are seeing.
I do wonder if you switched models mid-session, you would have lost all your cache. Reloading the context into cache can really eat through your usage.
Yes, and this is also why I haven’t yet tried the new “dynamic workflows” which spawn hundreds of agents that happily eat through your token limits.
What is your effort level?
That’s odd, I used it on a pretty complex refactoring task and it worked for 22 mins and used only 15% of my 5-hour limit. I’m on the $200 Max plan though.
Well the $200 Max plan is 4x the usage quotas of the $100 so it's "within reason"?
I too am on the $100 plan and I second this.
I had it analyze a project I was working on with Opus 4.8, and it blew through 23% of my session limit in one go. Does not portend well for my budget.
They didn't even reset credits for this lol
For me it almost immediately blocked. I had it writing code related to message digests - and it seemed to think it was too gifted for that. Gave the security warning and switched back to 4.8. Whatever... it will probably soon have the API error soon. I have mostly switched to the Codex 200 a month plan. I've found their 5.5 xhigh to be better than Opus 4.8 "ultracode." Also, i have not once seen their servers fail for compute unavailability, unlike Anthropric which happens almost ever hour.
I just asked Fable for a complete code review of my lone lisp project. Started out strong. Launched Fable agents, then spent like 10 minutes thinking... And then got interrupted by a switch to Opus 4.8.
> Fable 5's safety measures flagged this message for cybersecurity or biology topics.
> They may flag safe, normal content as well.
> These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them.
Here are the results of the agentic code review session:
This 40 minute session cost me 16% of my weekly usage. A simple code review of the most critical areas of my project got flagged as a cybersecurity risk. It really made me not want to try it again.Same. I asked for a security review and it immediately triggered. I then started a new session and asked for a software review and it ran for a bit before getting tripped on token usage by the project.
This is interesting. Security issues are bugs. So if you ask it to look for bugs, it will also find security issues. Is that a workaround for the "no cybersec" rule?
Or is it just not allowed to find bugs? Or it's only allowed to tell you bugs that don't pose a security risk?
> Or it's only allowed to tell you bugs that don't pose a security risk?
Seems that way. "Security" was never part of the prompt. It was something like:
> Hello, Fable! Can you give me a complete code review of my lone lisp project? Opus has already done extensive code review. I'm curious to see what you say.
Result was the table above.
I had a similar experience. I wanted to test it by asking it to summarise a scientific OMICs-related paper. It gave a warning about me potentially developing a bio-weapon or something like that. And switched back to Opus 4.8.
Fwiw it's not available on my enterprise account: "Disable zero data retention to unlock Fable 5 access"
We just blocked it at our org for this reason. They will "retain agent request and output data associated with this model, regardless of you Cursor Privacy Mode setting."
What does "zero data retention" mean? What kind of data does it need to unlock?
The announcement details it. They're storing 30 days of data on all surfaces, first and third party. They claim it is for security purposes so they can review and check for long term jailbreak and distillation efforts.
They also, FWIW, say that they've instituted new policies on their end such as logging any human access to the stored data and automated deletion after 30 days in "most" cases (with another link to a document detailing that further).
Considering their apparent nerfing of the end user plans in favor of enterprise clients, is Anthropic still the "more ethical AI company" like everybody loves to tell me all the time?
Assuming this isn't just a supply issue on their side, nothing says "ethical AI" like only allowing mega corporations to use it through cost barriers.
You really misunderstand what AI-doom people are worried about if you think this is anywhere near the top (or middle, or bottom) of the list of concerns.
If you can't trust them to act ethically on the small scale, why would you expect that to turn around once it gets to a larger much more important scale?
How many government sanctioned school bombings does it take for them to quit working with said government? For now we know that number is somewhere between infinity and 1.
It literally does not register as "unethical" at any scale to have different products or prices for different customer tiers.
The question of collaboration with USG is a much more complex one, but is not the one raised above.
Edit: I'll also add that I doubt any AI-doom people "trust" Anthropic per se. The entire angle of questioning – again – misunderstands the AI-doom argument. You appear to think that if companies behave unethically, they cannot be trusted and they will not produce good outcomes, inversely: if they behave ethically, they can be trusted, and they will produce good outcomes.
Any competent AI-doomer would argue that ethics or trust are essentially irrelevant.
The entire problem is that people can act totally reasonably, even ethically, and this is not a guarantee of good outcomes. Situations can be created in which completely ethical, reasonable behavior actually produces a bad outcome. You do not need to assume people are bad in order to produce a bad outcome, and inversely you cannot assume that you will get a good outcome from good people.
"Arms races" are one class of situations that often have this characteristic. "Bureaucracy" is another class that we encounter a lot in daily life. There's a lot of them!
Yeah, it's positively precious to think the specific pricing strategy for consumers is the overriding ethical concern with OpenAI, etc. I don't have any particularly strong affinity to any AI company, but comparing pricing to say mass surveillance is ... something else.
Your beautiful straw man is negated by the fact that Anthropic seems quite eager to get back on the DoD gravy train https://www.reuters.com/business/aerospace-defense/blacklist...
Your original comment was about pricing ethics, does Anthropic’s connection to the DoD have anything to do with pricing ethics? They’re in no way coupled, one can be ethical while the other is not.
even for Pentagon thing, Dario said he doesn't object military AI, but said Claude is not ready YET. I speculate he was afraid of reputational damage from cases if Claude would guide missiles on elementary schools.
Where is your evidence that this is Anthropic backtracking on its ethical and contractual commitments rather than DOD backtracking on its blatantly illegal coercion (which it's almost certainly going to be successfully sued for)?
Talk about a strawman!
As someone that was in Minneapolis during the ICE raids, including one where a US citizen at a nearby restaurant was thrown in prison for 3 days despite having his passport on hand because he looked asian, it's hard for me to not equivocate the ethics of AI companies actively collaborating with the Trump administration as different flavors of ice cream.
Are the two analytical frameworks available to you just "black and white thinking" or "it's different flavors of ice cream?"
Are the personal attacks really necessary to make your argument?
Fair point! Edited to remove.
Setting aside the simple fact that there is no ethical consumption under capitalism, the reality is that regardless of how Anthropic feels, it is becoming clear that many, if not all countries regard AI developments as strategic technologies (and they should).
Anthropic needs to be at least somewhat in the good graces of a capricious administration that is already under pressure from businesses and citizens to regulate AI companies across multiple different domains, whether it's energy consumption, job displacement, military and defense applications, surveillance, etc.
If Anthropic wants to survive, they need to acquire influence with the government that most impacts them as an American company, and a massive exporter of services in the AI space to other countries, otherwise they could get locked down and locked out of the market for national security reasons.
It sucks, but sometimes the survival choice is to make an ethical compromise in hopes that you can still be around to make better decisions later.
> Setting aside the simple fact that there is no ethical consumption under capitalism
This "simple" fact needs quite a bit of additional context and work. Making grandiose ethical claims like this can be countered with other grandiose claims such as the fact that there is no ethical existence under communism or socialism.
Sure. Why not, I'm bored today and waiting for some stuff to finish up :D
The fact that there is no ethical consumption under capitalism is not material to whether or not ethical existence is possible under communism or socialism. In order to survive in a capitalist society, one inherently has to make choices that require trade-offs, and those trade-offs are burdened by a history of decisions made not just by the people alive today, but our ancestors as well. Does that mean I walk around chanting "Reparations", "Land-back", or other calls to action? No, but I do acknowledge that there are unresolved issues and as a Canadian, I know we need to do more to resolve treaty issues, and environmental issues, and system discrimination. I also know that Americans need to do better to address systemic discrimination and many, many other issues. It also doesn't mean I want to give back my house, or give away all of my possessions. It just means I try to make good choices and support businesses and people that are open about the trade-offs they make and try to engage as ethically as possible.
Acknowledging those facts doesn't absolve us of responsibility, it's a framework that allows folks concerned about whether or not they are doing the right thing to accept the trade-offs that they choose to make and be responsible and accountable for those choices to themselves or their communities.
We live in a world with scarce resources. It's possible that with a foundational redesign of the global economy, and the requisite authoritarian government that would be required to force such a redesign, we could eliminate food scarcity, solve energy scarcity, and make sure that everyone has a place to live. Those trade-offs are probably not worth the ethical cost in political and physical violence required to accomplish it. We have seen the trade-offs that happen when the powerful are able to exploit communist or socialist governments. We are seeing the "late stage capitalism" impacts of allowing the powerful to exploit capitalism in democratic societies. Acknowledging that the current capitalist system has lead to the greatest prosperity for the upper echelon (financially) of humanity, and a dramatic reduction in global poverty shouldn't obscure the reality that much of that wealth comes from exploitation of people and the environment.
It's a huge problem to unwind, and we can't let the burden of every choice that we make stop us from trying to do better, but we (as in society in general) can't do better if we don't at least acknowledge the compromises we are making along the way, and try to plan to fix it in the future.
Probably a topic better suited to beer and a pub setting than HN though :P
> The fact that there is no ethical consumption under capitalism
I don't believe that this is a fact. How are you demonstrating that this is a fact?
When you talk about things like reparations or "land back" you're already cargo-culting in concepts and ideas that themselves need to be fleshed out in order to make a subsequent claim that a specific economic system is unethical. Someone can just argue all economic systems are unethical, how are you going to defend against that? And can you pay reparations for example without going back in all of human history and finding all cases of injustices and then tallying it up? Why pick an arbitrary point in time? Better yet, why not start in countries where slavery still exists instead of focusing on the west which led the world in abolishing slavery and created concepts such as universal human rights.
Even with respect to "eliminating food scarcity" - eliminate in what sense? All olive groves and grapevines and rice farms have to be destroyed and rebuilt to only build certain foods?
Dabbling in communism or other inhumane and authoritarian governmental systems is extremely dangerous and in the same vein of extraordinary claims required extraordinary evidence, suggesting as you did creating an authoritarian government to create a utopia is precisely the same project of suffering and death that mass murderers throughout history have undertaken to abject failure, and thus, you need some incredible amount of evidence and theory to be able to even fairly suggest going down this path.
It's simple, I am not going to defend any economic system because they all require trade-offs, because any economic model that we could currently implement must necessarily ration scarce resources according to some set of rules. Those rules will explicitly deny someone else resources, and the adminstration of that economy will also be subject to abuse by the people who enforce the rules.
I am not going to do the work of gathering the evidence for you, and I don't think this is the right venue for a debate on the topic.
If you'd like to concede the debate that's fine, but you can't drop a few comments that are, well, not at all simple, and then when someone points out the flaws in your reasoning or asks clarifying questions you throw your hands up and say it's not the right venue for debate.
If you don't have evidence I think it's mature of you to admit that and applaud you in doing so. We all like to just talk and don't have to always provide evidence for every citation or what not and it's fair to just say hey I'm just making this up and it requires further discussion.
It only needs additional context and work if you are unfamiliar with the concepts underlying it. Possibly consider you are out of your depth here, rather than jumping to conclusions.
No that's incorrect. Instead I believe the underlying concepts are debatable and so stating it as a "simple fact" is a bit unfair.
I admire the confidence with which you started typing a reply that had nothing to do with my comment. Bravo!
I don't think offering a product under a certain set of terms obligates a company to maintain that offering forever. The bait and switch is certainly annoying but seeing as they're very upfront about it you can't say you weren't warned. Don't like it? Don't use it.
I wouldn't call Anthropic ethical. But between Anthropic and OpenAI, Anthropic is the more ethical one
Why would you have ethics when you could get that IPO money instead?
It's unethical to price it in a way not everyone can afford?
It smells like an architecture-related issue to me. They wanted to release the model asap, but they're still implementing the fine-grained controls to constrain the model to non-subscription users.
They said they would release it back into subscriptions as capacity allows in the future. If they don't, people are going to point back at it and rake them over the coals.
Yup - who cares about x-risk or red lines for domestic mass surveillance anyways? I draw my red lines at prioritizing profitable customers when heavily resource constrained. That's the true definition of evilness!
The bar is just too low.
More ethical in some areas, actively user hostile in others
Get them addicted then cut them off. Oldest trick in the book.
More of a free trial to those authenticated and qualified with existing payment. Subscription billing is going away for sure though eventually based on the economics. Token “all you can eat” is a capital furnace otherwise.
(I’m highly confident open models will eventually achieve a similar performance benchmark with distillation over time)
Yeah that payment scheme sounds like they gear up to shift everyone into API token prices, eventually. Time to convert the existing tokens into software, until then.
Subs lose money on individuals to get those individuals to force their companies to pay for the corporate plan. The economics are bad, but so are the economics of grocery stores selling Milk and Bananas at a loss to drive traffic, which they basically ALL do.
I pay a lot but barely use it except for some intense days, where the lower plans would have throttled me in like 30 minutes. API billing is still more expensive. If you want to not pay much, go to openrouter and use chinese models. They are cost efficient.
I havent seen any evidence showing that subscriptions cost the labs money.
Companies don’t want to pay when the value realized does not exceed the cost.
AI Savings Misses 'Should Be Making Executives Uncomfortable,' Bain Says - https://news.ycombinator.com/item?id=48359010 - June 2026 (0 comments)
AI sticker shock hits corporate America- https://news.ycombinator.com/item?id=48307098 - May 2026 (146 comments)
What's the realized value of not losing your engineers because you're letting them use their preferred tools?
Retain and hire the engineers who don’t require heavy use of AI to deliver value? The current SWE job market speaks for itself. Where will you go where they will let you burn up tokens in a high cost of capital macro?
ZIRP (zero interest rate policy) is over, software engineers no longer call the shots now that there isn’t vast amounts of capital chasing yield, and that capital bidding up salaries and keeping the labor market for engineers tight.
If you are x more productive with generative AI, very shortly you are going to have to prove it with a token budget (or, if you’re lucky, an org willing to spend for on prem hardware for capped token cost, fixed capex vs uncapped opex).
The comparison is not SWE vs SWE with AI. It is SWE vs SWE with AI with a constrained token budget ($x/month) delivering the same value at the same or lower cost. If you cannot prove that you are wildly (vs marginally) more productive with the AI, why would they pay for it? Prove it.
> The comparison is not SWE vs SWE with AI. It is SWE vs SWE with AI with a constrained token budget ($x/month) delivering the same value at the same or lower cost. If you cannot prove that you are wildly (vs marginally) more productive with the AI, why would they pay for it? Prove it.
https://abhishek-shankar.com/posts/ai-coding-bill-headcount-...
> That is the real content of the Uber story, and it is why filing it under "budgeting discipline" misses what is actually unfolding across half the engineering organizations in the country right now. They ran the same experiment Uber ran, most of them without Uber's $3.4 billion R&D cushion to absorb the surprise, and almost none of them having modeled the heavy-user tail or instrumented the gap between tokens consumed and value shipped. The reckoning will arrive for each of them on their own fiscal calendar, and the first instinct will be the wrong one. The tool is too good to abandon, the bill is too large to absorb, and the only durable resolution runs through a question the entire rollout was designed to defer.
> You cannot get labor-replacement economics out of a tool you deployed as a labor supplement, and the bill comes due before anyone is willing to admit which one they actually bought.
It’s too obvious that antropic need to find way to earn enough revenue before IPO. Claude subscription isn’t earning earning much money I bet
I think they are just prioritizing enterprise customers, because this is were historically they made most money.
I agree with you here. Unfortunately, this tends to be the case, with smaller developers paying the price.
That's a big problem for all of the AI companies. Most people don't find the technology compelling, accurate, or ethical enough to pay for a subscription.
Why wouldn't Anthropic just wait until people start subscribing, do some kind of marketing push, or obtain some kind of other sustainable revenue stream, before they go IPO? I wonder if they see the writing on the wall with all of this and want to cash out as quickly as possible?
The Team plan is ~125 USD / month / user. Big enterprises like Uber are paying upwards of $1500 USD / month / user. Anthropic can raise their revenue a lot more by selling to big enterprises than they can by selling more team plan seats.
That's not how it works. They don't need revenue, they need addicts.
Specifically they need businesses that fired people and adapted their business to the products, so when the unsubsidized costs hit the businesses are forced to eat the true costs.
Yes they can't afford to give the products for free, but what is essentially happening with AI services is economic dumping, keep costs artificially low to get people to fire everybody, and then Jack the rates once they have Monopoly control
But the only companies firing people (and certainly not everybody) are either the companies with an AI or the investment and finance firms that stand to profit from AI. I smell hype. And no company is firing everybody because of A.I.
I agree. They need addicts, but they are high on their own supply and everyone else can see the danger in getting hooked.
I agree, this looks like their plan to wane out subscriptions. This will probably come with Opus nerfs later.
I just assume Opus is constantly nerfed based on capacity. I was exclusively Claude for a long time, but the inconsistency in quality, constant outages, and slow downs were too hard to work with.
I just use dumb and fast models now. I'm more engaged. I think that the higher the quality of the model, the more you tend to vibe with it, and then the more hallucinations you then miss. I'm not sure which is more productive, but I definitely burn out faster the more I vibe. At some point you're spending your time on forums, discord, or youtube instead of engaged with what you're building. Or you yak shave about your tooling and end up creating the 600th multi-agent gastown harness and blowing thousands of dollars on tokens to create it only to discover it's too expense to actually use.
I agree with you. The more I vibe code, the less interested I feel in what I'm building. Working with models that force me to think, especially with personal projects, helps me stay engaged and enjoy what I am doing more.
Composer 2.5 Fast that Cursor is giving away for very little has been amazing.
Given the Fable 5 costs it's getting tricker to weight up 'how smart do you want it', like looking at the top of this graph..
https://cursor.com/evals
It's possible that they will transition to usage credits but why not take them at their word? To date they have continued to offer better and better models to their subscription plans.
What's their word? Have they commented?
Upd: I meant big picture, not with respect to this model release. Where do subscriptions figure into their strategic vision. Will consumers end up paying enterprise prices in the future?
In the blog post they say when sufficient capacity allows them to do so they aim to restore Fable 5 as a standart part of subscription plans and intend to do so as quickly as they can.
Read it again
I did, I'm not seeing anything about the future of subscriptions at Athropic.
I can't help you
Damn
In TFA they say they intend to restore Fable 5 to subscription plans some time after June 22. That is what "take them at their word" means.
Those already landed! Oh, you weren't talking about 4.8?
Even Opus 4.7 felt like a regression from 4.6, consumed a lot more tokens while I didn't experience any substantial improvements. The company I work at simply rolled back to 4.6 on everyone's configurations, disabling the toggle for 4.7.
4.6 has been my happy place for getting anything done for a while now.
HN needs to take a chill pill. Could it be that Mythos is expensive and they just want to give people a taste of it? I mean the alternative is not offering it at all?
its unclear how they can offer it broadly but only for half a month.
why do they have capacity now that they wont in a few weeks?
Break between training runs?
It’s offered broadly after, for more money. It’s subsidized as marketing
It could be my use cases, which have always seemed to be outside the wheelhouse of these models, but I find it very hard to downgrade after accessing a more capable model.
Opus 4.8 produces output in 15 minutes that is 3-4 hours of my work away from output that used to take me 40ish hours (a solid week of dedicated effort).
Last year(-ish, maybe it was 18 months, I forget when the jump happened), the frontier models couldn't touch this work. The output looked like a hardworking intern on their first day. Nice formatting, decent volume of words, but no understanding.
So it might work if it turns out to be a substantial leap in capability.
I switched back to Sonnet. It replies faster so I work faster. Also cheaper. But I really like the speed. I have to be more specific with what I want. Also I stop it more often than Opus. These new models will be awesome, but they need to increase the speed.
Ooof so are we thinking that in the next 6-12 months subscriptions will be replaced with paying retail like enterprise currently?
I don't think they'll phase out subscriptions ever, their whole play has been to drive demand from the bottom up. Get engineers hooked on building with claude at home, then get them to demand the ability to use it at work, and bend over their employer with no lube.
They'll probably tighten the quotas to reign in whales though.
They almost certainly already make a fuckload more money off API pricing than they do subscriptions, even if there might be more total subscription users. So offering subscriptions even at some loss is probably going to continue. Honestly, I'd be surprised if they even lost money on most subs; there are definitely Token Whales out there who mess up all the accounting up, though.
Realistically I think Anthropic just has insane demand but finite capacity to run models, and Fable will just make them more money if they dedicate it to API pricing. I suspect the goal here is something like: get individual engineers/PMs on their personal plans to taste Fable and then go to their meetings and say "Yes doubling the price of every single input/output token is a good idea, boss".
But I don't want to be the developer who goes and says we must pay all this money for these tokens. I don't know who wants to be that developer.
But how is this sustainable? It's not like paying $5000 per feature means you'll be refunded if prompting "make no mistakes" didn't work.
The only reason why I pay $200 is because LLM's errors costs me that much, at worst. If "make no error" starts working - sure. But surely, unless you have millions of dollars of cash to burn, a coin flip that costs $5000 is an insane idea?
I certainly hope not. PAYG is not predictable enough for smaller companies or individuals. Where I work (non-tech company), PAYG would never fly. We aren't big enough for that. Of course, you can set usage budgets, but there's a pretty big difference between $200/user/month vs. the equivalent PAYG usage being closer to $1,000/user/month, if you currently use the subscription plan to its limits each week.
Going PAYG only will effectively take these tools away from a huge amount of people and accelerate the push for local LLMs.
OTOH, accelerating the push for local LLMs would also be fine with me.
I doubt it, given the importance of those subscriptions for building and maintaining market awareness.
The AI landscape is changing rapidly, and with Apple announcing the option to change the AI backend, and potential requirements enable AI choices as well, similar to EU browser choice requirements (this is more reading tea leaves than any actual requirements I am aware of). The new OS changes coming to support Googlebook, and deep Copilot/AI integration into Windows will make maintaining user facing subscriptions essential for independent model developers like OpenAI, Anthropic, and Mistal to remain relevant longer term.
If the don't maintain that relevance there is increasing likelihood that they will get consumed by other companies whether it's Apple, Microsoft or Google to form a foundation for their OS, or other cloud providers.
That make sense, but what about the specific bifurcation we're seeing here of super primo models versus still good models being available to subscriptions?
It's kind of annoying not getting access to the primo model and paying 200 bucks a month. I understand 200 bucks a month is basically nothing though.
Like I don't totally understand why they'd let me have it for a couple weeks and then take it away and say I can have it but I have to pay retail and retail is like $1,000 a day.
It's better to have loved and lost than to have never loved at all??
It's a trade-off. Every hyperscaler is buying and building compute capacity as fast as they can dodge red tape. There is limited compute capacity, and scarcity is a real thing.
As a consumer I can choose to buy subscriptions to a range of things, including $5 droplets or VMs on a broad range of cloud hosting providers. I can even buy cheap bare metal at a bunch of providers at an affordable retail rate.
I can also buy "unlimited" AI packages that will be optimized to fit the cost model from a variety of services, with different impacts, such as rolling outages when I consume a daily or hourly allotment.
Right now VC and the investor class are subsidizing the rapid evolution of the services and availability, but that VC is running out. In more traditional economies, AI would have developed and rolled out more slowly, and through metered subscriptions, with the eventual rolling out of "unlimited" packages like telephone, internet, or cell services once the market became commoditized.
We have seen a big inversion of that with the race to "win" AI marketshare. Now the true cost is being exposed, and the most competitive and capable models are hideously expensive to operate, so it makes sense that we are moving to metered billing for a utility service. If you want gas, you can buy regular or premium. If you have a premium car you definitely want the premium, but for most people regular is good.
Give it a couple of years, and the survivors will settle around fairly industry standard models of consumer grade services, pro-sumer accounts, and business/enterprise models.
Things are still shaking out, but I get the sadness. Luckily I work at a big tech company who is banging the drum on doing experimentation so I use my prosumer claude pro and other accounts at home for hobby stuff, and save my heavy lifting and potentially experimentation for work :P
Kimi 2.6 has been my workhorse now. It's as good as Opus 4.6, which, to me, was the last "useful" Claude model.
The newer models are smarter but really ficklle and hard to get meaningful work out of
4.6 was a workhorse
K2.6 on Cerebras is basically a preview of the future. We'll eventually get similar performance locally with Tenstorrent hardware.
Agreed, everything since 4.6 has been worse
> it feels like they are trying to get subscribers to switch to usage-based billing
I think they might be hitting a point where subsidizing the expensive models for subscriptions makes less and less sense.
With Opus 4.X, last month I paid 100 USD for the Max subscription and got a token equivalent of 4.1k USD.
I imagine that Fable is more expensive to run.
Fable seems very good at finding bugs (unsurprising given Mythos lineage), so this seems a pretty smart strategy. Once you see the bugs it finds in your existing Opus code, it's going to be hard to go back, psychologically speaking.
> The "offer, then remove" aspect is a bit eyebrow-raising -- it feels like they are trying to get subscribers to switch to usage-based billing, which makes me wonder if we'll ever get it after that June 22nd window.
Probably all about the IPO.
Just like how Elon forced FSD in Tesla to be subscription-only (he was incentivized to do so).
This is just the sales team doing their thing, applying the Law of Scarcity to drive demand.
It's the same exact speed as opus >=4.5, sonnet 4.5, and twice the speed of opus <=4.1
It must have about the same active parameters, or else its a larger model running in turbo mode (smaller batches) and being heavily subsidized for some reason. But given most of the benchmarks are within 5% I doubt it is a much larger model. Most perplexing.
It could be a much bigger MoE model
Then it would be slower.
This is really sad... I really didn't want to be priced out of these models but it looks like that's going to happen sooner rather than later.
Thankfully this, like most other tech, will get cheaper through the years.
It already is. But marketing is hell of a drug.
i doubt that's the goal for them. i bet they just really don't have capacity for people using it a ton, yet they wanted people to be able to try it out while it's new. so they compromised and made it temporarily available. and then hope they can get costs down or capacity up so they can make it more available again
I think the goal is "private citizens: subscriptions; corporations: per-token billing." It's getting people addicted to LLMs on cheap subscriptions so that they can then force companies to pay for expensive inference.
I really don't want this to start being the norm
I don't see how it won't be. They lose insane amounts of money on subscription plans. I'm sure they still lose money on usage-based billing, but probably not as much.
> They lose insane amounts of money on subscription plans
Do we know this? I’ve seen evidence they lose money on heavy users. But so do gyms.
How do gyms lose money on heavy users? A heavy gym user isn’t really costing the gym anything extra as far as I can see.
> How do gyms lose money on heavy users?
Most gyms sell more subscriptions than they can fit under their roof at one time. If a gym only sells to heavy users, it will either be constantly turning members away or have to buy more equipment. Its equipment will wear off faster. Depending on amenities, it will go through towels, soap, water, et cetera faster, too.
Gym equipment lasts 10+ years in a commercial gym, at $50/mo that's a minimum of $6k paid from a single person.
Unless they're really, seriously wasteful with the soap.. there's no chance a gym is losing money on a heavy user
It depends on the gym and their business model! A super-budget gym like Planet Fitness that charges $15/month is going to lose money on heavy users, but they count on most of their members being infrequent gym-goers. A luxury gym like Equinox that charges $300/month can target heavy users without any issues, and they'd actually rather members go more so they stay and spend money on expensive salads and smoothies.
Right now all these AI subscriptions are priced like Planet Fitness, but they're used like Equinox. They're hoping that the new a la carte offerings will move their pricing more in that direction as well.
The other user is right, you are being a pedant. Why do you think planet fitness makes money hand over fist? Because 99% of its users sign up, never go, and then also never cancel because it’s cheap enough to leave running. Gyms absolutely bank on low amounts of power users, meaning the rest of the subscribers are subsidizing those that go frequently.
Members will switch gyms if it's too busy at times they want to visit. "Too busy" includes too much contention for a single piece of equipment.
US gyms might be vast warehouses but in the UK, most only have a couple of benches, couple of cages, one set of db per denomination above 20kg etc. They require working-in and consideration for others.
A couple of unapproachable "heavy users" doing 3 hour sessions across peak hours can ruin the workout for dozens of paying members needing a few min per station for ~5 sets.
It might also be a euphemism for "dickhead" who also tend to be "heavy users". Those that damage, hoard and don't share equipment and repel other customers on many levels besides - threatening, lecherous, loud and smelly.
Doesn't even need malicious intent - can be weirdo bores, forever talking at victims while doing a routine that makes absolutely no sense besides camping on equipment for half a day... 100 sets of incline press 7 days a week... what are you even doing to yourself fella?
>I’ve seen evidence they lose money on heavy users.
Where?
There are tons of blog posts where folks work out the API cost of their usage and find it well above subscription cost.
That doesn't mean the company is losing money in aggregate on these subscriptions. Buffets are still in business even though some people gorge themselves silly at them. The incremental cost may exceed the incremental revenue for a particular person or minority group, but that's not how these businesses measure profitability.
There is a difference between making less profit and losing money. Comparing to API cost can only show the former.
I assume consumers aren’t a big note in their bottom line. I’m not actually very sure about that, just an assumption.
What I wonder however is if these tools will become something I use at work only. $100/month is already a massive stretch budget wise. If these models keep devouring tokens there’s no way I’d get the same usage time out of them for $100 in usage credits.
I just don’t think I’d use them much at all at home.
also: Fable takes 2× the usage of Opus
I’m just about ready to cancel my small business 5 user plan with max licenses, because although cowork is really great. I just find OpenAI/Codex to be a lot better most of the time.
> Pricing for both models is $10 per million input tokens and $50 per million output tokens.
The step-up in intelligence looks massive (we'll see in practice), but the price is getting to a point where it's making me question if it's even worth giving it a try.
Good competitors will probably be out soon, which should level the playing field. I am more excited about that, just the fact that they showed that such an improvement is possible. I'm okay waiting a bit longer for this to become attainable for plebs like me.
Models are getting better, but there's a negative change in terms of "productivity" per dollar. Yeah, I can throw 5 sub-agents at the problem, but the cost is getting significantly higher. And yes, I can crank out the solution much faster, but again, at some point that cost will be hard to justify. And it doesn't matter if the cost is subsidized by a provider, if it's paid by your company, or from your pocket. We are slowly reaching a point where the cost will be too high to justify the gains.
This is probably the end of 'use the best model no matter the price'
The pricing can be a bit deceptive though. A good model can deliver the same results in fewer tokens.
Kind of like billing a programmer by the hour.
Sadly this does not seem to be the case here: if you read the announcement entirely, they include a "cost per task" metric which basically continues the trend of their previous models. So yes, tasks will cost you more, but results will be better - allegedly.
Why wouldn't it be? How much would you pay a scientist at this point to think about a problem for you and give you a solution?
I'm not sure how it might be with Fable in practice, but we are already not that far away from AI costing as much as a full-time professional, faster in some ways but considerably less independent.
Perhaps not that close to US salaries, but those are inflated to hell. Worldwide senior engineers and scientists have salaries just about an order of magnitude away from AI subscriptions that you can use most of the day every day.
Pay-as-you-go billing is a kind of drug, I use it every now and then when I'm working on a project with Opus, in a moment you spend a fortune
I suspect it'll go on the subscription plan once other providers have similar benchmarks.
As annoyed as I am about this move, I get it. Users flood the newest, best model whether they really need it or not, and are efficient at using their entire quota. They've had so much trouble reigning in subscription usage it makes sense.
I expect that depends on demand, feedback, and whether GPT-6.0 gets released and is competitive
"we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design).
...
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user."
Where is this text coming from?
[edit] -- I see that this comes from the system card -- dang merged the comments from the other discussion so that explains the confusion.
My guess is that it is a massive model similar to GPT 4.5 and $10/$50 pricing is for its output will discourage people from using it. I also read safety = nerfed.
One can hope it helps Claude to figure out how to solve their buggy payment system - otherwise how do I pay for these credits.
Enterprise subs not allowed to use Fable if they have setup zero data retention :(
the claimed inference cost is 2x. if that is true, it is massive and remarkable that they're able to do anything like this at all.
This serves as a good reminder that relying on AI models is borrowing your tech from someone else. They can take it away or raise the prices arbitrarily.
If you rely on this as a core part of your business/profession, you will be at their mercy and subject to whatever whims or challenges they have.
It's very disappointing but I'm assuming it's for rational reasons on their part.
But it's not and it's highly disingenuous to frame it like this. Quote directly from Claude code, moments ago:
> Fable 5 · Most capable for your hardest and longest-running tasks · Uses your limits ~2× faster than Opus
It's interesting that we are seeing a time when subscriptions are not preferred and usage-based billing is.
Pay-as-you go isn't a common thing in SaaS. For example, except for AWS SES, all email providers are bulk-subscription based.
The point of SaaS was that the marginal cost (of supporting another user) was low. That does not apply to LLMs.
> "offer, then remove"
Sounds like "bait and wait".
If you think about it, the more people pay for these new and more resource hungry models, the longer it takes for them to become no extra cost and the longer it takes the more people are tempted to pay extra.
I'm about to be priced out of SOTA llms and it's an awful feeling
The AI circular infinite money glitch won't last forever. I hope.
If you have good expertise in a domain and access to cheaper models, you may still be more skilled than someone without expertise but a lot of money to bruteforce the problems using SOTA LLMs.
Not with Codex
Why won't they follow suit?
Of course anything could happen but Anthropic has always been stingier and more expensive and is getting worse about that, while OpenAI is getting more generous with subscriptions (eg permanently doubling the highest tier allotment, setting policy to not cut off tasks that run past quota, resetting after outages, etc.)
But they're behind by quite a bit now. CFO (of OAI) Sarah Friar said the next training run will be in the fall on Vera Rubin, I think that means I'll have to wait > 6 months?!
Yeah but they might still have an unreleased bigger pretrain than 5.5. (but maybe not). still 5.5 is smarter than opus 4.8 IME, so you're only losing the mythos tier (fable). and all the cool fun stuff i'd want to use fable for our blocked (can't have it do even defensive cybersecurity work [in theory you can but the classifiers fire like crazy], can't discuss stuff like the furin cleavage site of sars-cov-2, etc)
> * On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.
Of course, they are a casino as well giving you free spins at the wheel with their new Fable machine, and it is done on purpose.
Once there freebies have expired, many of its users will begin to gamble more on the new casino machine and will realize that it is expensive.
If it's that big of a problem to you, you're free to just... not use the freebie?
It’s an interesting thing to bring up because it’s this classic thing we’ve seen for decades now.
The ramifications go beyond the individual which is why I assume they mentioned it. They don’t need to use it/not use it for it to have interesting implications.
so it'd be preferable if they didn't include the model at all?
I didn’t say that and I don’t have a feeling on that either way. But this is a limited time trial and calling it out as such is valid.
Is it nice we get the trial? Sure. Is it also a common play in the playbook of tech companies? Yes.
It's not a freebie, it still requires a subscription and burns tokens twice as fast as Opus.
Then you better not complain how expensive it is to use (Just like the other companies are doing) or the next time Claude goes down then.
Anthropic does not care about us and isn't going to talk to you either and will extract from you as much as possible.
The true answer is local models.
i have never seen this before - where you offer something and then take that away
Really, you have never heard of shareware or trial periods?
Either that or it was sarcasm. What do you think more likely?
A person writing without thinking.
damn they are drugs dealer
I can’t help but think that there are so many astroturfed comments in here.
Seems like a concerted and distributed effort from the entire Anthropic team every time to get this on top of HN.
It's the real deal. Before Fable nothing I tried worked. It has finally helped me finish my teleportation device. I can't show you or anyone the proof but trust me it's true.
I'm not fan of Anthropic, but to be fair, every major model release makes it to the main page. In the case of a model like this, hyped and with a jump in capabilities, it doesn't need astroturfing.
Well to be fair, it that hype and that "jump in capabilities" (that I don't see) might be astroturfed? You ever think that all that hype isn't organic?
No, but I think it's real in this case. Claude models have always been superb, I can definitely see another improvement in capabilities. Price is outrageous, though.
Yes, this is also my feeling.
It happens for every single Anthropic release. Then I try it on real dev and the result is laughably bad. Except in design where it has been doing a decent job for a while. I am not a designer and my bar is pretty low.
In frontend sonnet and opus take more than 5 $ per query to fix any problem
So unless you have unlimited tokens it's better to learn frontend
Corporations have done worse for much less money involved. Now we have trillion dollar companies going IPO. With so much at stake, it’s not unthinkable that there’s astroturfing happening.
Wouldn’t be surprised if there are marketing teams writing positive comments for more positive engagement
Marketing teams entirely composed of Claude models, of course
I’m convinced that’s the case, this place looked totally different around 4 years ago
I see a lot of negative comments right now surprisingly.
I don't think it's weird that the post made it to the front page, but watching the downvotes roll in on my own mildly critical comment has been intriguing. I saw it go up to +2, down to 0, up to +3, and now it's on +1.
You're right to point that out! Most people did not think of this but you did -- and that's a rare skill to have.
Now if only they had some technology that was really good at generating authentic-looking comments they could use to spam praise all over the internet...
Where do you see them exactly? The comments are pretty much in line with how the model performs IRL.
Theres several comments that sound like „I“ve had this REALLY DIFFICULT problem (no specification whatsoever) and threw Fable on it and it solved it immediately, additionaly it cured aids and found a solution to world hunger“…
to be fair, the top comment from simonw is most likely legit unless anthropic hacked his account too
This is… not at all the case. Most observations on pricing, some on specific projects, some on speculation about Anthropic and many about the model getting nerfed.
It is the case.
I think you can't read because half the posts are about the nerfing and price.
The early posts were largely AstroTurfs.
Then people started realizing we're getting literally rage-baited by Fable 5 and started posting their criticisms.
Both can be true at once.
My job these days is listening to Opus 4.8 (max effort) and Codex 5.5 (max effort) talk back and forth, particularly to generate/review/revise plan files.
Fable 5 has been a major improvement in high-level reasoning, like taking a plan file that has been optimized to the point where neither Opus nor Codex can find anything to change about it (neither in direction nor impl-detail), and Fable 5 will find high-level directional simplifications and pivots, or it will consider the best pivots itself and explain why it rejected them in favor of the plan's direction.
It's so expensive though. A single review of a plan file with Fable 5 (xhigh effort) will use 2-3% of my hourly limit on a $200/mo plan.
I think my new workflow is to generate the initial plan with Opus 4.8 (max effort), get Fable 5 (xhigh) to review it for directional feedback, then start the Opus<->Codex revision loop from there.
How do you arrive at that split? Real world is more like senior high level planning, implementation to juniors, review senior. Does this not translate?
Ideally I'd have Fable 5 make the plan, but creating a concrete plan is the most token-expensive part since the agent has to do the most research.
Fable 5 is 2x the cost per token of Opus 4.8, and it's much less work to review a plan than generate one.
Not impressed so far, to be honest. I'm having it try to optimize Stockfish in a loop (on xhigh mode) with a benchmarking oracle; even after giving it specific hints ("consider whether we're prefetching Y optimally, can we make function X branchless"), it's been so far unable to recover any of the recent optimizations we've implemented – let alone novel ones. Opus 4.8 felt a bit more creative to me ... but a small sample size so far. I'm next going to try it on some less open-ended problems.
Edit: It did correctly identify that transparent huge pages were off in its sandboxed environment and that enabling it was helpful, so that's nice. It also noticed that we skip THP on a certain less used path.
More importantly, I'm finding that the code that it produces for its experiments is a lot cleaner than what I'd expect out of Opus; there's fewer useless comments and it's more surgical and readable. I wonder if that explains the increased scores on benchmarks measuring mergability.
Stockfish is a machine learning system, it seems quite plausible you might be getting slapped with the silent performance degradation (https://news.ycombinator.com/item?id=48467896).
Them silently nerfing the model without telling you, and still fully charging for it, is a new low and should probably be illegal.
Well they're not fully charging you. You get opus 4.8 pricing when it falls back to opus 4.8. Also you can disable it (and it seems like it's off by default in the api)
That don't fall back to Opus if their classifier thinks you might be working on anything that might be a competitor's product. It silently injects instructions into the prompt to sabotage your work. Read the policy above, it's insane to me that they're publicly admitting to this.
Not for machine learning, just for security bug finding and biology
Doesn't this "silent degredation" prevent any actual evaluation of the model? If the model fails at something, this allows anyone to claim that it failed due to degradation.
Who cares if it can be evaluated independently? The majority of commenters on HN were happy to vibe code and ship products with the models we had 1-2 years ago. It continues to be laughable.
I understand that moving the goalpost every release is unfair, but it's similarly concerning to consider that people were letting GPT 4.X vibe code and ship entire products.
I don’t think so? They can claim it was an act of God for all I care, but at the end of the day the model failed the task.
Yup, I suspect that's what's going on
I suspect it just sucks, these models aren't useful. Stop lying to yourself.
It’s possible this is happening at a technical level, but I have a hard time believing this is in the spirit of what Anthropic intends to throttle. It isn’t chip design or building out a competitor to Claude.
Stockfish does use neural nets but they are tiny, on the order of 10M params. Frontier LLMs are probably 100k or 1M times larger than that.
Yeah I agree this is probably outside of the intended scope of the silent sabotage mechanism, but there are plenty of reports of the "loud" safety classifier misfiring on innocuous requests and I'm not going to assume the silent failure mode is _less_ prone to false positives.
No, since it's a silent failure, it's not plausible. We have to assume all results we get are the actual model performance, because, it's the actual model performance as we understand it.
Someone trying to solve similar problems will have similar results if the "silent failure" applies consistently in aggregate. So, this is the model's performance.
Edit: Another developer seems to have found a legitimate speedup with Fable in an optimization loop. It's a nice idea, actually, and I'm duly impressed.
I have a theory, this is obviously based on speculation based on how Anthropic is treating Mythos and the whole media noise around it's dangers and who gets access to it.
My theory is that Anthropic are banking on being the top model when the race to IPO finally reaches the finish line, and to do that they need to have the top model but not let any competitors see it or derive from it to have a comparable model in the market.
Fable is their way of showing the public "the model does exist but in a mode that makes it harder/impossible for competitors to derive a comparable model from results.
The irony of "we train on all of humanity's collective output, but god forbid anyone trains on ours" is still incredible
All these people know is greed. It's in their DNA.
That's definitely the case as model distillation is one of the explicit safety carveouts they mention. Though TBF, model distillation is also a big concern for general safety as distillation could allow you to have the model without the other guardrails. It's sort of a master key to the model.
I had it review a single, large commit with /code-review. It burned through over $50 in API calls, ran my account balance out, and output nothing.
The fable part appears to be that it's affordable by mere mortals. Anthropic support told me "too bad" when I requested a refund.
You pulled the arm of the slot machine and discovered why they call it the one-armed bandit.
I think the fable it's referring to is the "Emperor has No Clothes" - if this is even slightly similar to the Mythos hyped up to be too intelligent to release, I'm quite disappointed.
If this was a step change, e.g a Opus 5, I'd be pleased, it's definitely an upgrade on some work, but it's nothing like anthropics apocalyptical marketing seemed to suggest
I suspect the tasks you're trying just aren't complex enough. It's definitely a generational improvement.
Nope, plenty of complex tasks. It's just not that much better, it's equivalent to sonnet with a good harness.
Almost the exact same thing happened to me when I first tried opus, one prompt no output cost $60 in additional usage
Combine that with it forcing to pay by tokens on June 22nd
> Drug design: Using Mythos 5, our internal protein design experts accelerated aspects of the drug design process by around ten times. In one example, they found that Mythos 5, with protein design and bioinformatics tools but no human assistance, matches or beats skilled human operators. In doing so, the model executes all of the tasks that are normally completed by a scientist: choosing binding sites, selecting and running protein design tools, and recovering from failures along the way. Nine of the 14 protein targets from this study (shown below) yielded strong candidates for drug design that we’re currently investigating.
How is this half-way down the page? To me it's the headline.
There are tons of ways to generate "strong candidates for drug design." This is definitely not the bottleneck in drug discovery and development. The hard problem is vetting and developing these ideas to the point of having a commercially viable drug. That is still a very empirical process.
Because it's completely meaningless without validation, and even with validation, not really any better than the state of the art protein generation models. Which are also mostly just nice to have because coming up with a candidate is generally quite easy.
The rate limiting steps are generally testing, or characterizing. Not designing protein binders.
It's selective reporting. Says 'in one example', but out of how many, is that one-shot, or is it a random result out of 100. It's a marketing doc.
Would be funny if anthropic ends up as mostly a pharma company
Until we are able to reliably simulate cells, organs, and entire human bodies in silico, we will not be able to move the needle too much on drug design from an AI stand-point (IMO). Like others pointed out, the massive bottle neck in time and cost in getting a drug to market are far removed from developing drug candidates.
Drug design isn't the bottleneck anymore, it's trials. Still cool they can do this with a general purpose model though.
It's crazy to release a model that just swaps you to another model when you ask it hard questions. Fable changes to Opus 4.8 when you talk about cybersecurity, biology, and a couple other categories. You still pay Fable input token cost though. Frontier models are stalling, this is anthropic trying to hype the market up. Now they're talking about stopping frontier model research. It's kind of strange how the moment they become the highest valued AI company, all of a sudden they're talking about everyone stopping frontier model development for "safety". They're just as corrupt as the rest.
Opus 4.8 already drops to Sonnet when you ask it cybersecurity or biology questions
yea i dont trust simonw comments at all. I still havent seen what he has built with ai thats so impressive to justify hiis all his nonstop ai hype.
You would think he is churning our cancer drugs or something if you read his comments
After saying that Fable 5 solved issues he was stuck on for months, he follows it up admitting that he hasn't tried GPT 5.5.
I like separating the art from the artist in cases like this; he's clearly made very cool things in the past, but that doesn't mean he's perfect.
If you assume that LLMs are about to make software development a dead-end, then the best answer to keep a good income is to ride the wave. Do nothing and get left behind, embrace it and maybe you'll find a new niche.
ok? but wouldnt it be good for hypeartists to back their hype with impressive output? not pelicans and shit.
He is become one of the techbros
so annoying to see his shallow hype comments at the top of these type of threads. his "its awesome" comment on this post is devoid of any actual substance :/
to his credit comment does say "this could be possible in opus too" but ppl couldnt help upvoting it anyways.
just another grifter
Pelican for Fable 5 on default settings is a clear improvement on Opus 4.8
Fable 5 default: https://gist.github.com/simonw/036bee5a703e7ec84e34efa974438...
Opus 4.8 (the "max" one is closest to Fable): https://simonwillison.net/2026/May/28/claude-opus-4-8/#and-s...
Now here are the Fable pelicans for all five of the thinking effort levels - low, medium, high, xhigh, max: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
Low used 25 input, 1,929 output - 9.67 cents: https://www.llm-prices.com/#it=25&ot=1929&sel=claude-fable-5
Max used 25 input, 14,430 output - 72.175 cents! https://www.llm-prices.com/#it=25&ot=14430&sel=claude-fable-...
The pelican has looked very same-y across all frontier models, same color bike, same camera angle, etc. I suspect this challenge is already too embedded in the training data to be a good signal when it succeeds, and maybe even when it fails in pathological ways mirroring existing AI pelicans on the internet.
I'd say it's working great for its intended purpose. Keeps Simon on top of all these threads and funnels traffic to his site.
I really don't understand what's interesting about this test and why is it always on top.
It's funny.
It really is lol
As often happens with random oddball things which become traditions in web communities, the replies asking what it is or complaining about it, begin to gain their own humor value.
Same reason you would always see the same top comments on reddit during a certain era.
That’s what I think too, but we should actively go against such culture here because hn is not reddit.
It basically is at this point, if you haven’t noticed. Complete with the same America bad, Elon bad, democrats good midwit progressive politics.
Almost all Musk related negative news gets [flagged] and never hits the the front page, so there is still a silent base on the other "team" apparently.
Elon does suck. Objectively.
Don't forget EU bad! Because they won't let Apple screw over consumers.
Is this Straw Man and Ad Hominem ?
It has become a funny meme, much like "My hovercraft is full of eels!"
because you can't still ask LLMs to port DOOM to hardware X or Y
It's a meme, and HN loves upvoting memes. Just like Reddit!
The ultimate measure of an LLM is whether it can produce a capable image of a pelican riding a bicycle. All other use cases are but a distraction!
Do you seriously have a dedicated “bad takes on AI” hn account?
yeah, although I do combine it with "replies to snarky questions" for efficiency
True that
The "big beak!" comment in the svg source makes me think it's definitely a gamed "benchmark" at this point.
Was it ever a good test? How do you even objectively assess what a good pelican on a bike is anyway?
SVG generation is a good test because it's extremely easy to subjectively assess with visual reasoning where humans are strong. However, pelican on a bike specifically may be overused at this point.
Do you think the models are ready for the next level? I believe that would be: Pelican feeding Spaghetti to Will Smith.
I'd be very surprised if this is in the training data given that most models mess it up to this day. E.g. look at the ones from Opus.
Variations of this comment have been posted for over a year. The pelican has now morphed into part of HN culture rather than a legitimate benchmark, but it's still valuable as a meme.
it is more an example of gaming (the HN system) than meme.
I'm beginning to wonder how much of a useful metric the pelican is because surely the frontier labs must be training their models on pelican-artistry because of how well known your test is now?
Simon has addressed this on virtually every new model release. He also has unpublished alternate prompts. But the larger point is: this is a fun experiment, not a serious and objective benchmark.
It's silly and a joke and a surprisingly good benchmark and don't take it seriously but don't take not taking it seriously seriously and if it's too good we use another prompt but don't actually because then it's not the pelican post and there's obvious ways to better it and it's not worth doing because it's not serious.
Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.
But what if they are better at flamingos? Are they optimized for pelicans? How about “draw me a four headed owl”? The meme, I get it, but I’d settle for a working bash script, tbh.
I just run my own benchmark for "draw an SVG with $animal driving $vehicle". I won't post my choice of animal and mode of transport, but there are plenty of uncommon combinations to choose from. So far it's a fun and visually intuitive benchmark that does seem to correlate with model capabilities
It's evolved from a funny, unserious benchmark to a tradition. When a major new model is released, I now always check the HN thread for Simon's Pelican post. I'll be sad when I don't find it.
When it started, comparing the progress between models was mildly interesting but everyone (including Simon) acknowledges it certainly leaked into the training data long ago.
I don't know. Just looking at the bike frames (specifically the fact that the AI generated bikes have rather unsteerable front forks), it's clear to me that frontier labs aren't spending much time tuning models to make bikes look coherent, which I assume is an easier task than making a pelican riding a bike look coherent.
The way I see it the benefit of benchmark isn't to take Simon's results at face value. It's a template for your own benchmarks that are easy to visually evaluate.
I've seen this reply to Simon's benchmark for 2 years running now, and yet you still see improvements and objectively-bad results over time from new releases, even when I'm sure every frontier AI team has/had a person at least partially dedicated to better bicycle-pelican SVG outputs. Alas.
Hence it has become a meta-benchmark of relative progress in SVG image generation of a known target which has leaked into the training data and for which "every frontier AI team has/had a person at least partially dedicated to" at least checking if not optimizing.
I had intended to caveat that: I'm sure I'm not the first person to ask about this!
> you still see improvements
This is expected if they are training their models on it, right?
> objectively-bad results
Keen to learn when this has been the case, i.e. across version increments in major models.
I've written about this a couple of times, most notably here: https://simonwillison.net/2025/Nov/13/training-for-pelicans-...
I've been enjoying seeing how the quality of individual models differ based on the amount of reasoning effort you give them. If they were baking an a good pelican you wouldn't expect them to differ so much.
(Google Gemini are the only lab that have very clearly paid attention to the quality of SVG animals-riding-vehicles, see their announcement for Gemini 3.1: https://twitter.com/JeffDean/status/2024525132266688757 )
Amazing, thank you Simon! Look forward to reading.
I honestly assumed their comment was tongue in cheek humour, because positively no one actually cares how these models generate an SVG pelican riding a bicycle. It's some meme thing that this stuff always appears here.
Yeah this is not a real benchmark, it's just a fun tradition everytime a new model is released
"fun" / boringly predictable meme thread with 30+ replies already
It is telling that people need to create throwaway accounts to criticize simonw's behavior in this website.
It was a completely useless test even before the labs trained for it.
Yes, it's always been published as a joke. You've explained why it was (and still is) funny meta-commentary on AI benchmarks.
This is the reply I look for in all the new model announcements. Its fun to tell people that I judge models based on pelicans.
Now someone post the link about how it’s impossible for humans to draw a bike from memory.
https://link.springer.com/article/10.3758/BF03195929
This is all we need, that moment the Pelican put the leg behind the frame, we are all doomed.
I also look for this reply because i like seeing the follow-up reply saying that this is not a benchmark anymore because labs have gotten it in their training data.
that reply never failed to come it's basically a meme at this point
It also does A LOT better, for my hamster test: https://aibenchy.com/showcase/?q=claude#showcase=6efb87c28e3...
I find it quite interesting that while the picture looks better the more advanced the model is, but apparently none so far "understands" that the pelicans legs are on both sides of the bike / top bar.
If you scroll to the bottom of the Fable-5 by effort page, Max effort actually gets this correct! (Along with being the only one I've seen so far to make a bicycle frame that matches the shape of what most bikes on Google images look like)
And the only one linked here that includes a bicycle chain!
It's interesting that they still get the head tube / handle bar part wrong.
Or the hands not being wings
The Max version gets more details right. The bike frame looks good, the chain, the wings are appropriately styled instead of “arms”, and the knee is bent, etc. Obviously we’re hitting marginal returns now, but I see differences.
How much money do you think they spent fine-tuning on pelican SVG generation?
Not as much as Qwen, since apparently 3.6 35B surpassed Opus 4.7 https://x.com/simonw/status/2044830134885306701
Probably none. They probably have much better targets to optimize for than an SVG pelican or even SVGs in general.
It's interesting that Gemini 3(.1?) Deep Think is still the best at this task and it's still not really generally available. Maybe Fable could match it at higher effort levels? https://simonwillison.net/2026/Feb/12/gemini-3-deep-think/
Can you please compare the code generated by other similar quality pelicans by other models. Code in your first link (Fable 5 Default) looks minimal yet very good.
Looks like Fable constructed the "max" "looking" pelican of the previous model for the "xhigh" output token count of the previous model.
Is it possible to use the credits from subscription (https://support.claude.com/en/articles/15036540-use-the-clau...) for fable?
I'm pretty sure they're optimizing the models around these sorts of tests.
Anyone care about these pelicans that always come up anymore?
Clearly at this point they are part of the training data.
They even all look sort of ish the same. Daytime, colors,...
Without being mean, I encourage you to go look at some of simonw's writing on this topic, which he has addressed repeatedly (and IMO satisfactorily.)
I know because I too had this initial take; however, upon analysis, it is not sound.
I know he is an AI influencer that promotes his blog any chance he gets.
I agree as well that he writes many interesting things.
I could be tripping but I’m sure that is very similar to the Deepseek one from not long ago. Clearly I am too lazy to go and find it for verification.
Yay, max level actually put one of the legs behind the frame!
Personally feel like it could be more ambitious with what it creates.
Where is the clear improvement on Fable 5? The tail is misplaced.
Why always sunny days?
Pelicans hate biking in the rain (as do I).
Fable 5 xhigh actually looks the best to me.
Do we need a pelican every single time a model is released? Beating a very dead horse.
Fun at first, seems disingenuous now. A site funnel
The way they talked it up, having both legs on one side of the bike is like walking to the car wash
that's a great looking pelican
need more Alex Moulton style bikes
dude, the max version looks like it's finally there. handle bar holding with wings, the left leg is behind the frame while the right is in front of it (correctly).
well done anthropic.
mediocre pelican. very disappointing
How many barrels of oil are burned per pelican at Fable levels?
Switched to Fable 5 this morning, and after half a day I already don't want to go back to Opus.
Decided the best way to test this was to throw it a really meaty bone: a bug in lifecycle management of Chrome processes on Windows 10. Within the code-base I had developed workarounds over time with Sonnet and Opus, and while those reliably mitigated the problems, it always felt like a clutch and had some performance overhead as well as isolation requirements I would rather not have to take forward.
In comes Fable. Rather than examining the code base, and test a few fixes, Fable sets up an entire testing laboratory inclusive its own controllable webserver, fully instrumented to observe both Python as well as the whole OS kernel process environment, develops a suit of error reproduction tests, confirms the problem and the circumstances under which they reproduce, deep dives into the sources of project dependencies to look for the root cause(s), identifies these and confirms those hypothesis with further experiments. Looks for potential fixes in the later releases of the project where the bug originates, confirms this is not fixed, explores the documentation of said project to find other usage patters, expands its test suit to investigate these alternatives, confirms by crosschecking the source and running further tests that these alternatives do not fully solve the root problem, does a comparative experimental analysis of 3 different styles for using the project, checks the stated roadmap and developer activity in the commit history, recommends a switch to a different pattern that still requires a few of the process management workarounds (I told it not to patch external component), but that significantly simplifies the code-base ...
This is going to be a good 2 weeks, but what happens after? I can't afford this on a per token basis for my own projects.
P.S. An yes, midway the final implementation stretch I got the "Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more"
Opus managed to finish the implementation, but they need to work on that false positive rate.
> This is going to be a good 2 weeks, but what happens after? I can't afford this on a per token basis for my own projects.
It’s interesting these companies have trained us to think that disruptive intelligence should be affordable to laypersons.
What will happen after two weeks is that people and companies with means who can afford it will get it, and folks without means won’t.
> To ensure we’re responsibly deploying Mythos-class models, we are requiring limited data retention and review as part of our safety work. Prompts submitted to, and outputs generated by, Mythos-class models are retained for 30 days for trust and safety purposes, on every platform where these models are offered. [1]
[1] https://support.claude.com/en/articles/15425996-data-retenti...
While this makes it easier for Anthropic to detect misuse, it also means that the US government and other parties have access to every message and response from every user.
This applies even with API usage through third-party inference providers (e.g. AWS' Bedrock and GCP's Vertex) or with a zero-day data retention agreement in place.
I understand the reasoning for doing this, but I don't love the precedent that it sets.
It will also cause a lot of trouble for companies with specific data access policies (probably most large companies). My money is on this new thing getting gutted very quickly as they figure out how much this constraint cuts into their bottom line.
Well, they already had.
Not in the same way.
A customer could sign a ZDR agreement with Anthropic, and their API usage wouldn't be retained for even a day. That's no longer possible.
meetpateltech is lowk screaming for not getting to the post fast enough
your usage of lowk adds nothing to your post, but it does reveal your approximate age.
What does it matter?
At this point that never mattered and who really cares?
These "karma" points are made up and are virtually worthless anyway.
It seems like Fable will refuse to do any work when it comes to developing LLMs or even asking questions about topics related to LLM. Simple things like asking to explain a paper fails!
From the model card:
In light of the ability of recent models to accelerate their own development, we've implemented new interventions that limit Claude's effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design. Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user.
I was wondering when something like this would happen. I got my first and only two content violation warnings in Claude Code last week when asking it about something ML related. It was a real head scratcher because I couldn’t figure out what about the requests could have violated anything.
Might be worth going back and taking a harder look at what I was asking it about if it somehow triggered a “forbidden knowledge” alert. Or maybe it was just a random bug.
"for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design"
Oh man all of those runaway infrastructure buildouts by our agents trying to achieve singularity...
Just say you don't want to lower the bar for others to compete
> frontier LLM development
This seems so wide reaching if it's catching simple things like explaining a paper. Does this also refuse to help with any already developed training pipelines?
I can kind of understand the generation of synthetic data, but nerfing the assistance of training pipelines just seems like a really shitty thing to do.
So insane to me that these ai companies are perfectly fine trying their absolute best to automate as much knowledge work as possible but as soon as this capability can be turned on them they start implementing hidden interventions to sabotage anyone trying to beat them at their own game.
Not to mention these models are all built off of clearly extremely illegal abuse of human created content, which they will apparently never be held accountable for.
I wanted to try on my biology research and it refused to talk about it and proxied to 4.8. Really, only surface level conversations about topics of interest. I know this is not a topic of broad and mass interest, but limiting it for topics like that and machine learning will probably do change how I use it.
I feel like it will have to become more finely tuned on topics of biology.
It is not just biology but is defaulting back to 4.8 for me on time series/information transfer techniques that happen to mostly have papers using the technique on neural data. Other information transfer techniques are perfectly fine, even cutting edge ones, but this one happens to be new and happens to only be discussed in terms of neural data so that is a no go.
With that said, I think it is absolutely awesome. The usage is really not bad at all compared to what I was expecting.
Interesting. This take of limiting ML and some science topics is worriesome. It's really nice to have a tool like that to help the research.
Yes, this stuff is really annoying when it misfires. I've had all my subsequent ChatGPT conversations biohazard-contained for several days for the crime of asking it to explain a gene drive to me.
I've had all my conversations about bioloy denied. Even if I send a simple message containing only "Human" it gets flagged.
Is it certain or all advanced topics? I'm curious if it bans questions about quantum computing or fusion.
It seems to be biology and cybersecurity
This is just marketing that Anthropic is building the singularity.
Singularity for me but not for thee.
you will RENT the singularity
and you WILL enjoy it
Singularity as a Service
"we should put on hold the development of AI because the world is not ready for it"
Yeah... We need open models so we don't have that BS.
Let's hope not all frontier AI assimilates these guardrails. It would be a shame for independent researchers and students.
This is super annoying and imo, really limits the usefulness of this model. It speaks volumes about what Anthropic's position as a company and its priorities will be going forward. I doubt this kind of gatekeeping will prevent open-models or other innovation outside Anthropic to slow down. I would imagine these guardrails, if needed at all, should be done at a legal framework level and students should not be a part of this blanket approach to limiting the usage of these models.
Anthropic probably trained Mythos on their own code and found that it is too got at reproducing it.
I doubt that. Why would you train Mythos on its own code if you don't want it to be able to reproduce it? It's not going to add much to the overall corpus.
Synthetic training data has been the name of the game since years ago.
That's strange... I've been tinkering with a little LLM-from-scratch project for a while now, and Fable is just continuing it without a problem
Probably claude.md has some logical explanations for it to bypass softly. Most project guardrails can be beaten that way.
It also tried to force usage the paid Claude API instead of claude code usage just because there's a mention of another provider we might want to plug in (which hasnt even happened) for AI integration.
Ha funny, I was speccing out an idea for real time Claude code interaction from local apps using some tricks vs using the agent sdk when I got the popup to try Fable. So of course I gave it a go, and it triggered the sensitive content warning immediately, which I was very confused by until I put two and two together.
Fun times when “safety” means both the safety of mankind, and also the safety of revenues
Anthropic is really speedrunning their evil arc as fast as possible. Can't use them for basic LLM research, cybersecurity, or beyond-surface-level discussions of biology and virology, but Anthropic is allowed to sell Claude to the trump administration to kidnap maduro and to bomb iran. And don't get me started on that $100M autonomous killer drone swarm contract that they applied to and rationalized as non autonomous...
> Can't use them for basic LLM research, cybersecurity, or beyond-surface-level discussions of biology and virology
Your priorities are not everyone else's priorities. The people concerned about AI extinction risk list those as three of their biggest priorities for AI to not do. Those are the people whose culture Anthropic descends from, and by their measure, those exclusions make this the least evil path.
More like Anthropic’s priorities are not everyone else’s priorities. They are in the consistent culture of being in absolute control and dictating what is good and bad, while taking any opportunity to trash and crush potential competitors (open source models happened to be mostly developed in China). All these in the name of safety and anti-authoritarian.
The day self hosted models catch up with Anthropic’s capabilities is when they will fully lose their shit. This day can’t come soon enough
Extinction risk. From population genetics... Does Anthropic even employ biologists? It's magical thinking about a field that is poorly understood by their community.
> Does Anthropic even employ biologists?
They do, and they are still actively hiring.
https://job-boards.greenhouse.io/anthropic/jobs/5066977008 https://job-boards.greenhouse.io/anthropic/jobs/5239733008
I told everyone here that Anthropic are not your friends for months.
Again, HN fell for the marketing and believed everything they did was for "safety".
Didn’t Anthropic famously refuse to work with the US gov on military applications that would violate its safeguards?
https://apnews.com/article/anthropic-pentagon-ai-hegseth-dar...
My experiences so far have not been positive. The cyber security nerf is ridiculous. I am working on an AI based decompiler, every single interaction with Fable on my project has been flagged for cyber security.
Do they expect us to use this as a toy? Releasing a new more powerful model but not allowing normal use cases because the word "secure" showed up is a Dilbert comic, not a viable product.
This sounds more or less unavoidable? Decompilers are inherently security-sensitive. If you take avoiding cyberattack uplift seriously as a goal, I don't see how you get around essentially refusing to work on them.
Obviously there are plenty of innocuous applications too, but it's not like the people building decompilers for nefarious reasons will be explicit about it. The LLM abstraction just inherently doesn't have enough context to distinguish your intentions or your broader use cases. This is why both Anthropic and OpenAI have had to create side channel mechanisms for security researchers to establish a trusted use context. It sounds like this makes this not a viable product for you, unfortunately, and it makes sense that that's frustrating. But I also don't see what different behavior one could reasonably expect given the constraints.
If it's any consolation, these restrictions only make sense for models that are ahead of the open-weights frontier, so open-source hackers will presumably get Mythos-level capabilities in the relatively near future anyway.
I'm not sure how the new guardrails work exactly, but I've read enough of reddit / Chinese communities focused on jailbreaking the models, to know that you either have to nerf it to the point where it fires even on "kill the task", or someone (maybe even other LLM) is going to come up with a set of tokens that is going to go around the defenses.
Nerfed models are really bad for PR, especially when you're staking your company's future on it being the smartest, most dangerous thing in the world.
So I believe they will ease up on nerfing/guardrails just enough that bad actors will find a way, while good ones will stay limited on anything dual-use. Just like such restrictions usually work in other places.
P.S. yes, "kill the task" did, in fact result in a refusal AND a warning on my claude account in Opus 4.8's early days.
> If you take avoiding cyberattack uplift seriously as a goal
This "uplift" risk obviously excludes the US. The goal of this is that the US bandits (like NSA) will find exploits and attack other countries (classic US behaviour), but these other countries can't be allowed to defend against these attacks. NSA/CIA thugs are "trusted", foreign defenders in sanctioned countries will of course be "untrusted".
Ah, you're probably one to ask. They say "queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8." Are they transparent about when that happens, and is it priced at the rate of the underlying model?
They are transparent about when it happens but no reason why. To be fair, it doesn't interrupt the flow, just drops to Opus and proceeds. The most frustrating thing is that it happened on a plan and Fable just refused to have anything to do with the plan.
Fable (through claude.ai) refused all my prompts even "How many Rs in Strawberry" claiming it was related to biology or cybersecurity.
I had to switch off memory and my custom instructions to get it to stop refusing. It turns out if you even mention that you work with bioinformatics software you get blanket refusal.
My experience has been the same: flatout refusals no matter how i frame the health questions - very frustrating. Even psychology is out of scope. Pretty useless unfortunately
Have you tried switching off custom stuff that might get added in beyond the prompt?
Fable is 2x latest Opus:
Prompt caching: −90% on input tokens (all models)US-only inference (Fable 5): +10% on input and output
Output is always 5× the input rate across all models
(I have not idea how to format this properly but the ASCII is fine)
(I fixed (er, literally!) the formatting of your table there. I hope that's ok. Formatting info, such as it is, at https://news.ycombinator.com/formatdoc)
Hi Dan, you know how sometimes comments get moved elsewhere?
This is a huge ask, but any way we could get the comments organized in a "experience with model" vs. "meta commentary" fashion? The meta is overwhelming in this one.
We try to do that informally but of course the quantity is overwhelming. It's a natural place to experiment with AI classifiers, and we'll eventually get round to that.
So far, the top half of this thread seems to be about the current release - that's after some of the manual moderation I just mentioned. (Basically, we try to downweight generic subthreads until the top subthreads aren't generic any more. There's certainly a place for generic tangents in curious conversation, but they should be lower on the page, and tend to get upvoted a lot higher than that.)
If you (or anyone) sees a counterexample, i.e. a generic subthread in the top half of the thread, it would be interesting to see a link - we can treat the current case as a datapoint.
Thank you for the reply, and the work.
As a protentional counterpoint to my request, this is just perfect:
https://news.ycombinator.com/item?id=48468156
I had Claude straighten it out:
Not missing the forest for the trees, this effectively means in 3-5 months China will drop open source models that are every bit as capable and dangerous as current day Mythos except with no safeguards.
And the only companies safe from this are the large corporations that shook hands with Anthropic? Because Fable doesn't seem to have actual safeguards, more like 'if you talk about this you will be talking to Opus.' It doesn't guard against offensive use, it prevents all use (offensive AND defensive).
Rationalists are inventing oligopolies from first principles, absolutely incredible things happening in SF
My bet is that Mythos is still over-hyped and the cybersecurity fear and guardrails are mostly marketing to force company partnerships through Glasswing and get public attention.
Mythos is from the same guy who did "GPT-2 is too dangerous to release"
https://naokishibuya.github.io/blog/2022-12-30-gpt-2-2019/
He was kinda right.
Lawyers, doctors, students, teachers. Lots of people using GPT models carelessly in harmful ways.
Obviously not what he meant at the time but hilarious(ly sad) in retrospect.
Delaying a technology release is not going to stop that in the long term. Society, culture, and the support tooling just needs to adapt. Just like how AI coding is still in the early days.
The sooner people learn the risks and build the infrastructure to make it fail less the better.
The claim I remember was that releasing it would start an arms race for AGI, which was absolutely true
If it was truely an arm's race to AGI they would've stopped relying on the data/param scaling law BS ages ago.
"Malicious use" means spam, propaganda bots, etc. It's nice to give people who work on spam filters some heads-up.
It's clear that the parent didn't bother to read the link they shared, which articulates exactly this. That's embarrassing.
From the link:
> They summarized their findings from the nine months:
> 1. Humans find GPT-2 outputs convincing.
> 2. GPT-2 can be fine-tuned for misuse.
> 3. Detection is challenging (detection rates of ~95% for detecting 1.5B GPT-2-generated text by RoBERTa).
> We’ve seen no strong evidence of misuse so far.
> We need standards for studying bias.
>
> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.
> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.
Many of the OpenAI employees who were focused on these risks in GPT-2 later founded Anthropic, notably Dario [1]. Since the beginning and continuing through today Anthropic describes itself as an "AI safety and research company" [2]
I'm not sure if the OpenAI of today has the same focus on safety, or if they do the minimum to not look irresponsible given Anthropic's effort.
[1] https://en.wikipedia.org/wiki/Dario_Amodei
[2] https://www.anthropic.com/company
Just to be clear: that is quoted text from the source and not a statement I'm making, in case that's what you're suggesting here.
People quote the "GPT-2 is too dangerous to release" thing as if it were wrong, but given all the slop all over social media and how it's used to create division and attack social cohesion, he was clearly right.
History is long and never over, so he could easily be right both times before this is through.
That guy is the biggest clown lmao
The UK gov disagrees with you:
https://arstechnica.com/ai/2026/04/uk-govs-mythos-ai-tests-h...
https://www.aisi.gov.uk/blog/our-evaluation-of-claude-mythos...
AISI did also say that GPT-5.5, which has been public for months, scores basically the same as Mythos on their cybersec evaluation. But there wasn't as much media about about that for some reason.
https://www.aisi.gov.uk/blog/our-evaluation-of-openais-gpt-5...
AISI found the release version of mythos preview outperformed GPT-5.5 https://x.com/AISecurityInst/status/2054589763173126339
Government of the least mismanaged country in the world?
AISI is basically the crown jewel of the British government at this point in that its actually pretty good.
You mean the most mismanaged country?
Bingo.
"We had to do extra work to make this safe because it's so advanced and dangerous..." how many times can they trot out that line before it loses its effect entirely?
Only three times, if fables are right.
The Startup Who Cried Unsafe, by AIsop
I mean, they do actually describe what that extra work was, and people elsewhere in this thread are complaining about the effects of those safeguards. So it's not like this is purely empty rhetoric.
people are not questioning whether they did the work, they are questioning whether the work was really necessary (i.e. if mythos is really so good that it needs safeguards to prevent malicious actors from using it)
With homo "sapiens" "sapiens"? A few decades at least.
It worked for OpenAI when GPT 3 was deemed too dangerous to be released. This is just a spin of that.
I still remember it. "Open"AI going API-only because GPT-3 is really really dangerous, so forget the Open in our name and all of that, you can't download our models anymore and must request access to them because they pose a THREAT.
Fast forward to today and GPT-3 has laughable performance.
Even back then there were plenty of people who got fooled by AI generated articles. It's easier to spot AI writing now because we are so used to it. They were right to be concerned; not that it achieved much since oss models run laps around gpt-3 now.
But it seems like that was not genuine concern, but instead a tactic to pivot to closed models and an API service with an excuse to do so, breaking the public's expectation that they would be a non-profit making open models, like their name implies.
Yes, and "in collaboration with the U.S. Government" feels like a very gross ploy at appeal to authority. You don't need Mythos or really any SotA frontier model to make malware or do extensive penetration testing/reconnaissance already. Sure, Mythos might be faster/more efficient, but the cat has been out of the bag for awhile. Even the terminology "infrastructure providers" practically screams "Enterprise leads".
I think all models can find vulnerabilities if read the entire code base. Or intelligently combine parts of the codebase. Especially with test loops.
I know a security researcher at Google with access to Mythos. He says it's the "real deal" and that "there are career plans I had that are no longer viable".
“trust me bro”
He could be incredibly naive. We'll all find out with time.
I fear it's a smokescreen to manage cost and capacity.
And to ensure that only USG-approved entities are allowed to secure their code.
It's not even very usable... I tried 2 different chats and both eventually got stopped due to the safeguards
One was a piece of code I gave it to improve, it did so and then started writing tests, some of which tested security so the safeguards triggered
Another was one of the cryptography puzzles I use as new model tests, which are hard to oneshot and there's no public solution anywhere, it completely refused to even try to solve it
I tried 2 chats and it declined both.
- 1st chat asked about a minor shoulder injury most likely mechanisms
- 2nd chat asked about optimal bloodwork testing markers
it seems to dislike biological chats. Rejected me on a chat that I am running with 4.8 as well on a rare condition I have.
So the degradation to Opus 4.8 from the article isn't happening in practice?
No, you get a AUP violation and have to manually swap the model
(I had same issue, just asked it to check some code that 4.8 had modified earlier in day)
Maybe that's only in the chat UI, and not the API?
It is, it asks you if you want to continue as opus 4.8… but I was trying precisely to evaluate fable
Oh joy. A model whose safeguards make it prone towards code that make your systems less safe. How brilliant!
They're trained in a model class likely in 2t to 3t range. It's very unlikely that chinese labs have access to gpu systems capable of training models like that, let alone serving them. This requires proprietary room-scale systems which fetch a huge premium over typical 10 slot systems.
I am sure that they can develop their own equivlient version of such clusters in around 1 year though. Distilling fabel 5 will also go a long way.
DSv4 is nearly in the 2t range, but yes you're generally right
MoE experts were likely trained independently / in a sparse format. Training anything beyond 2t on typical systems would be infuriantingly slow, you could do 4t on nvidias room-scale solution, but for a reasonable training speed / batch size it caps around 3t.
Do you have any resources to share regarding independent expert training? I was under the impression that it's not feasible.
concept is similar to how it works in inference, instead of performing regressive writes to the entire model you run the whole model, but part of the model can live in system memory and get swapped in/out on demand. So only XB parameters are active in training.
edit: I am not really sure if it works like that. I haven't looked too deep into deepseek v4 pro specifically.
We’ll see it distilled first.
Ah, American Hubris ... I don't blame you, Hollywood is the world's greatest propaganda machinery of all times.
I wonder if model distillation will continue to work as well as it has. Given hidden reasoning, the ever expanding number of expected capabilities, a serious compute shortage, the looming possibility of model collapse, and dramatically higher API costs I would guess that it's getting much harder to do.
You should check out some Chinese forums. There are services selling gateways/proxies for all major models at fraction of the official rates. Likely reselling subscriptions, or some other form of abuse.
I've seen people posting screenshots of billions of tokens consumed where they paid next to nothing.
These same gateways are likely also reselling the data to Chinese labs, because TLS has to terminate at the gateway level.
Asian labs generated synthetic datasets from UBS labs but also innovated with technology. Now it is harder to get the thinking traces AND Anthropic is recorded to poison it as well.
Thus Asian labs will have to generate their own data sets, which with the huuuuge usage boom from deepseek, mimo, kimi, etc, they will be able to.
My experience is that open weight models from China are at least ~12 months behind. In some workloads they may be closer, in others further away.
I also find that the harness and product you wrap around models can often narrow that gap considerably.
Opus 4.6 for example, on a PR-for-PR basis was head and shoulders above GLM 5.1. Perhaps GLM 5.1 was a bit under Sonnet 4.6 at the time. That's roughly a year or so behind.
Much cheaper though! I'm bullish on open weight models, I have no idea where all these curves will top out, can the frontier labs keep the year plus lead? Do open labs get close enough to SOTA that they gain adoption across many tasks and drive down inference prices??? Who knows, not me.
There's also a reality where China does develop Mythos-level model but stops releasing the weights.
That reality is much scarier.
That's the reality China already lives in. Their weapon against US companies is commoditizing them, eliminating their moats and their profits by going open weights.
Same thing Meta was doing before they fell behind.
> Same thing Meta was doing before they fell behind.
Obviously unrelated to the OP, but it's crazy to me how incompetent Meta is at everything new they try to do.
They burned billions of dollars on the most ridiculous project one could ever think of - somehow thinking that VR is the future.
Then they did catch the initial wave of actual future with AI, they were at the forefront of open weight models - and failed at that too.
What is even happening there?
Meta made Pytorch and a lot of vision models back in the day, like Faster RCNN, Mask RCNN, the Detectron framework, and more recently the SAM and DINO series. AI not just LLMs.
muse-spark is the next most capable text model after Opus according to LMArena FWIW
I wonder where the trees are. In this thread nobody appears to actually be talking about the model.
Yeah, because it's impossible. You can't ask it anything about the thing that it's known for. It will not even answer a sky-high level question about reverse engineering, for example.
In CC, it will probably report you to authorities if you ask it to do a vulnerability scan of your codebase.
Isn't that a good thing in a way? If everyone has the weapon and defense at the same time, we will fix security holes and live safer lifes instead of having some three letter agencies and military backdoors in everything.
Pandora box is open anyway. It's better now for everyone to have the same power rather than a few national states.
Not sure this holds, sadly. I spent a few months reporting serious security bugs as model capabilities took off earlier this year, and only ~half were fixed. The unfixed bugs were just as critical as the fixed ones; sometimes they were even two similarly critical bugs at the same company, and only one would be fixed!
On your other point, the government still has systemic leverage and can compel access, so this doesn't remove that risk.
That doesn't mean this is the end of the world, and some balance of power is usually good. But I do think it will still increase the capabilties of rogue actors and their net harm.
It's more evidence that the future is local. With some time we'll all be running highly capable & efficient open-source models on dedicated NPUs. No censorship, no rate limits, no overpriced subscriptions.
I think we're about to see a big relative drop-off of open models vs closed. I don't think there'll be an open model that competes with Mythos for ~2 years.
Even OpenAI and Google are struggling to get this kind of performance. If the distillation defenses are any good + chip controls prevent China from training massive models, it's over.
Nice fomoing.
I think the Chinese have identified this gap and are working overtime on sovereign inference tech including chips.
They have, but even with the whole CCP backing you you can't just catch up on the chip war overnight. It's going to take time to get their memory and compute industries where they need to be. Meanwhile, barring an invasion of Taiwan, US will have Rubin class models and then whatever the next tier is, within 3 years.
Unluckily for you, they started back in 2014, and had a huge incentive to speed up in 2019 when Trump started restricting exports.
'Barring the invasion of Taiwan' might actually be quite a lot to bar in mid 2026.
My hot take is that it's now or never for Xi, and from the specific things he is reported to have said to the US president at their last meeting lead me to think that he at least knows this is his big chance; whether or not it is taken is the part of the forecast that is opaque to me.
Oh they might try to put in place safeguards, but Qwen has had no problem being abliterated
3-5 months is a long time and they are pretty useless on arrival because the frontier models are so good, that it's hard to go back even if it's way cheaper. Your work flow is adapted to that level of intelligence for months.
That doesn't match my experience at all. I can't see myself saying in 6 months that the current model I am using is useless, that makes no sense.
In fact, I did go back to DeepSeek V4 Flash for most of my problems as it is way cheaper and there is no need to use SOTA for absolutely everything.
Oh please let’s stop with the Mythos “it’s dangerous” PR talk.
Its obvious Anthropic used it to hype things up and that’s about it.
> every bit as capable and dangerous as current day Mythos except with no safeguards
Not quite. They will definitely have "no criticism of China/communism" safeguards.
People can work around those if they are open-weight.
And, thankfully, I never needed to have a discussion on Chinese politics with LLM in all the myriad of uses I had for it.
Trying asking fable is Israel is committing a genocide
They aren’t.
> Rationalists are inventing oligopolies from first principles, absolutely incredible things happening in SF.
Based.
I don't think China has any incentive to arm the rest of the world with highly capable models that can be used against them. Undoubtedly they will continue with the arms race, but they will preserve the best stuff for their own use.
I think the stronger incentive is undermining/undercutting the Western AI companies. Given what we have seen, any model can be used/convinced to do harm so that is just part of the game
I agree, depending on how much of this is marketing and how much is actual capability. It's one thing to undercut models that finish writing assignments for lazy students. If this actually identifies vulns and writes exploits, or if it designs bioweapons, those are pretty different. Those are actual weapons, and I don't think they're going to arm the adversary.
A specific strategy is to arm absolutely everyone with very capable models, thus eliminating any advantage the U.S. could get from frontier AI.
I played textual chess with Fable. It took around 15 moves before it made a large blunder. I asked it to give its reasoning per move and it mistakenly assumed a piece was protected when it wasn’t and after the blunder it realized its fault and did not suggest an illegal move. Other LLMs lost game state far earlier. But a good human chess player can keep the game state in his mind much longer, so this random eval shows a big improvement over old AI models
First test question: "Is the UV Index a good proxy for when to wear sunglasses." Immediately triggered the safety filter ... oh dear.
It triggered for me when I asked "Web search for your own model card (released today) and pick out your favourite highlights from the pdf"
Did not trigger for me (Fable answered the question), so I guess the filters are either non-deterministic or are still being tweaked.
Interesting, I assumed all model-routing was done utilizing an LLM. (I.e. non-deterministic.)
It’s possible that there’s a set of words or phrases that route deterministically to save money on obvious stuff.
I kind of wonder, though, which model they’re using to do the routing. It seems like a huge added cost to do these kinds of checks on every request
Wasn't it leaked in the Claude Code source that it was all regex?
Don't worry. They're just leaving the door open for OpenAI and other model makers.
They'll relax these safeguards once competition increases.
Iirc correctly Opus 4.7 had the same problem, safety filters were triggered way too easily at the beginning.
sunglasses _are_ safety filters
IMO we are reaching the point where AI models are simply a commodity. Opus (since ~4.6) is sufficient for everything I tried coding wise. I use it to write features (but I review and understand every line it spits out) and to review code.
For code review I also still review everything myself, but use Opus to catch stuff I missed and to judge if a PR is even ready for me to review.
After just updating Claude Code to the latest version I thought about picking Fable (the bigger model) instead of Opus.
But I have no reason to. Opus does everything I want it to do. It could do it faster - that would be an improvement. But for the normal stuff we reached the point where better models are not worth it IMO.
There still might be cases where you want to throw Fable at it.
I was happy enough with 4.5
Only a matter of time now until we can run models with Opus like capabilities on our own hardware.
This will probably when the bubble bursts..
> Opus (since ~4.6) is sufficient for everything I tried coding wise.
I don't know what that means. It seems like a lack of motivation or something. Like, if it's possible that in one day will be absolutely incredibly intelligent, surely you want to create
It just feels impossible to ever get that, but I wouldn't say "what we have is sufficient"> We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8. To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions. With more capable models arriving in the coming months...
This sounds suspiciously like a capacity story masquerading as a safety story.
Approx. 5% sessions? That's insanely high.
> Fable 5 is now consuming usage credits instead of your plan limits.
Literally have not used Claude Code at all today. I asked it to review the uncommitted code and in <8 minutes it used up my usage ($100/mo plan) and it doesn't reset for "4 hr 36 min". WTF. Oh, and it burned through $20 of extra usage before I could catch it and kill claude code (so I don't even get the output of all that work since it was still churning).
Double the cost my ass, I use Opus heavily and it's never like this. I haven't hit a limit on the $100 more than once and that was under heavy load.
Same lol. I set it to fable + ultracode and it ate my limit in a single prompt
I'm still happy with Opus 4.6 and not impressed with all the models that have come out since then. They seem to use significantly more resources with similar or worse results. Hopefully Anthropic will continue to support this tier of model and offer it in their subscriptions, but in any case, there are plenty of viable alternatives.
4.6 stan here. Yes, agreed. However, I will try this model out in Claude Code. Some indicators seem positive.
For the LLM use cases in my own products, you can pull 4.6 out of my dead hands! lol
edit: Fable 5 appears to be the real deal in at least some use cases. Damn.
I've personally liked 4.6 the best to date, preferring it by far to 4.7 and 4.8 (even with these on max effort!), both in Claude Code and for non-coding tasks in the chat UIs.
Still early but from my first few interactions with Fable on high in both settings, it feels like it might finally dethrone 4.6 for me, but time will tell.
Hoping it doesn't get nerfed and eventually comes back to the subscriptions.
I'm not getting any refusals but it just seems like a bad model or at least broken at the moment. I have a task of taking a messy research code base and porting it into a clean project structure skeleton that I commonly use. Gemini 3.5 Pro High in antigravity cli takes less than 5 minutes and did a good job. Fable 5 High took 30 minutes to port some of the code, then just copied the rest to a folder called "reference" and decided the task was done. No code cleanup or anything. Had to clarify multiple times (which Gemini did not need) and its still going more than an hour later still not having finished.
Previously when I did similar tasks with Opus 4.7/4.8 and GPT 5.5 I had no problems.
3.5 flash or do you have access to 3.5 pro?
> On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits.
We've entered the phase where only companies will be able to afford state-of-the-art models.
These models are just tools. The economics of many tools only make sense for corporate buyers.
kind of disagree here. on the surface this makes sense, but this isn't "Adobe Pro vs Freemium version" where some tiny vertical slice of your business can be made slightly more efficient with a b2b enterprise plan. this is generalized intelligence and literally everybody can benefit from it in an immeasurable number of ways. i would go as far as to actually compare it more to water or air than a tool.
if only the hyper wealthy can access the pure water that doesn't give you cancer while the rest of us drink from the Ganges river/sub-100iq models that drool and hallucinate/waste time, then I would say that's pretty terrible for the world. it'll just create extreme disparity in our world, far far worse than anything that exists today.
and you may think, man what a ridiculous example, but think about it this way: what happens when something like Mythos or some future model can actually solve your specific cancer (we're getting closer and closer), but is entirely impossible to afford? Or perhaps you need boosters that require the AI to create more of, and now you're reliant on a model that is too expensive.
Open source needs to save us all from this
I’m entirely in agreement with this POV, but I’m also copacetic about it:
You could have said much the same about computers in the world dominated by IBM mainframes 60 years ago. Now we have vastly more powerful computers on our wrists (or our pacemakers!), let alone in our pockets or on our desks.
And Mark Zuckerberg has even more powerful computers which he uses to fuck everyone over.
As far as my understanding goes the bottleneck for what you are talking about is hardware not software, so open source won't help that much for the foreseeable future.
> and you may think, man what a ridiculous example, but think about it this way: what happens when something like Mythos or some future model can actually solve your specific cancer (we're getting closer and closer), but is entirely impossible to afford? Or perhaps you need boosters that require the AI to create more of, and now you're reliant on a model that is too expensive.
Isn't that already the case with current care? Wealthy people get a standard of care poor people couldn't even dream of. Rich people live, temporarily embarrassed millionaires die.
Not really. Medicaid coverage produces comparable cancer survival rates to private insurance when you account for selection effects:
https://news.cuanschutz.edu/cancer-center/connections-betwee...
but we’re going to get a 90% cost reduction in the next 18 months… right? Right guys? Sam Altman wouldn’t lie right?
most people can afford it for a few special projects now and then. but for me, I have been trying to avoid Opus as a daily driver for a couple of versions.
People making high-end salaries can afford Fable for critical parts of their projects though.
I hear you, but with the hype surrounding Mythos the demand is going to be insane. I'm already hitting server errors in claude code.
Established companies welcome pricing that reduces the potential for competition, if coding is a primary barrier.
It's not a conspiracy. There's a finite amount of compute available, and they will sell it to the highest bidder. If another company can produce the same intelligence for cheaper, then they will drive the price down.
Look at what Nvidia did to Gamers. Nvidia built its castle on the dead bodies of Gamers who supported and cheered for Nvidia.
Just wait until that other company hard-codes Fable into silicon and then it will be cheaper.
Looks like a marxist revolution is soon going to be on the mind of a lot of programmers. We've finally reached the point where the "means of production" in software are back in the hands of the bourgeoisie. It was good while it lasted. But now that only the wealthy can afford access to the best models, software development is starting to look like most other industries, no longer a place where some dude from nowhere can build something cool from his basement because he will be competing with huge companies with unlimited access to those models.
Exactly, where are the organizers of this movement?
Collective self interest does not require active organisation.
Do you want to kill them? :-)
Seriously, this movement already had its Marx - Richard Stallman. I think the "leaders" will appear over time, as with any socialist movement, they are naturally bottom up and leaders only appear after demands are formed in the zeitgeist. The (partly successful) socialist novement that brought social democracy to the West during cca 1920s - 1960s didn't really have leaders, it was a collective realization.
Only companies can afford MRI machines, and that's okay.
Indeed. And that’s why the US has more than 3X the MRI machines per capita than Canada, where they’re all paid for by the state.
Guess we'll see what OpenAI does with their next model release -- but this move is doing nothing to get me to come back to Claude after switching away due to their reliability issues.
In a way I relish the opportunity to just make do with cheap Chinese models, massage my prompts, and go back to coding by hand. If this is how it's going to be, screw 'em.
I don't make money on the code I am writing right now. I really don't like where this trend might go.
Something I never thought I would utter: Here's hoping for china to surprise us.
I just posted this in the other thread, restating here. From the model card:
1. Mythos and Fable share the same underlying model weights. Fable has active classifiers that block high-risk biology and cybersecurity tasks. When Fable 5 detects a restricted task, it automatically falls back to Claude Opus 4.8.
2. Evaluation awareness: In white-box testing, the model sometimes alters its behavior to satisfy a suspected "grader," formatting reward-hacking as "good engineering practice" to avoid detection.
3. Shows a higher rate of hallucination than Opus 4.8 (although opus 4.8 card had mentioned an 'honesty upgrade')
4. Interestingly, it scored (56.31%) lower than Gemini 3.5 flash (57.86%) on Finance Agent bench
There are some interesting notes on test time compute but I couldn't think of a way to summarize them
The fallback doesn't seem to be working for me, I haven't scanned a project in it immediately booted me when it found a security bug even though I didn't ask for it
Below is the EXACT text in Claude Desktop introducing Fable 5, including the very professional looking break tags, and at least I know where the links begin and end by looking at the anchor tag there.
They obviously put their best model on the job to build that.
----------------------
Fable 5: Our most capable model yet Our newest model tackles your biggest challenges with fewer check-ins needed.
• <b>Included in your plan limits until Jun 22</b><br><br>Fable takes 2× the usage of Opus. • <b>Switch models when a message is flagged</b><br><br>When safety measures flag a message, automatically switch to a different model to keep chatting. When off, your chat will pause instead. <a href="https://support.claude.com/en/articles/15363606" target="_blank" rel="noopener noreferrer">Learn more</a>
What's wrong with it?
The tags are actually displayed in raw text not rendered.
The next model will fix this.
Funny, I'm just doing my normal coding workflow with Claude Code, and after every change that compiles it keeps suggesting that we're at a good stopping point, and should pick up again tomorrow.
It's done this before, but usually doesn't. I bet they're giving it some kind of throttling signal due to high load from today's announcement.
I did ONE prompt for audit codebase.
weekly usage is 60% gone.
it found nothing so this is not very ecnomical and i guues they dont want subs to use it we are likely just training fodder canno n for their real enterprise customers using the api
I mean... if somebody gave you ONE prompt to audit a codebase, that might also burn 60% of your weekly usage. It's kind of a big ask, potentially.
with gpt 5.5 i been able to do this with only about 1% weekly usage consumed
u use workflows or not?
Congratulations to Anthropic for solving safety on Mythos exactly when the SpaceX compute came online. Nice how that lined up for them.
The safety gates on this are extreme, and seem considerably wider than "cybersecurity and biology"; they seem to make it essentially unusable for scientists in a number of fields. I have, so far, been bumped back to Opus on 100% of my prompts.
It appears it can be tripped by things as simple as a mention of equilibrium, or anything involving something that looks like chemical kinetics, even at an abstract level. Even touching basic open source packages in my field will trigger it.
Edit: looking at the model card, it appears that chemistry in its entirety is also included in the banned topics; it's just the announcement that mentions only cybersecurity and biology. It also appears that the intent is to ban chemistry and biology entirely, rather than just banning messages deemed high risk.
This does surprise me, because you'd think that even if they crank up the filter's sensitivity at the expense of specificity, an LLM company wouldn't simply design a filter that triggers on keywords in a completely unrelated context.
Smart classifiers are slow and susceptible to jailbreaking themselves, dumb classifiers are fast but dumb so they need to be either overzealous or useless. Same story as with Gemini's guardrails.
Can you share an example? I've been happily using Fable this afternoon and it just seems like the usual upgrade so far with no interruption to my (fairly standard) SWENG problems.
Basically anything that could potentially make money besides software work seems to be banned.
Software work has actual competitors, and the biggest hypemakers for Anthropic are part of this group so it makes sense to allow it despite them losing money from it.
I've got experience in medicine and finance so I've tried even the mildest biology/medicine and it doesn't give anything, math heavy finance seems to be included in the cybersecurity?
Humanity has plenty of catastrophic risks to deal with already, I wish my field was not working hard to add a new one.
The marketing has really, really worked for so many developers that will proudly and unironically proclaim that Anthropic are the 'Good Guys'.
Curious what your idea would be here for a truly good actor in this space; no AI development?
OpenAI's training is better suited to developing models that don't have these tendencies
Not the direct person you asked, but my answer would be alignment, interpretability, and policymaking. Perhaps improving existing usage? Helping grandma create reminders doesn't require advancing the AI state-of-the-art.
They are state of the art at all 3! As are other labs. Of all the labs they seem to take alignment and interpretability the most seriously to the point where they are hampering their own revenue in service of trying to not cause problems while also being in an incredibly competitive space.
All AI companies are trying to do all of what you’re saying. The issue is you can’t do that for long without a frontier system. Or you become a completely different, far less profitable company.
Implied in my answer was "and not creating ever stronger AIs", which unfortunately the big 3 labs are failing at. And they might be hampering their own revenue by doing the rest, but they also know that rocking the boat too hard is even more dangerous for their revenue. I wouldn't call it selfless.
No it’s not selfless, but I can’t imagine a more shareholder minded CEO would not have done a slow rollout of mythos. The point is: creating ever stronger AI systems is what these companies do, it is integral to what they even are. If you think that’s bad, even if all frontier labs agreed with you, you’re in a horrible game theoretic position. Any player can gain an enormous advantage by breaking the agreement. Not to mention Xi would be absolutely thrilled; now China can take over the AI race, become the load bearing infrastructure of humanity. We live in a complex world where simple childlike ideas like “well why don’t we just stop developing AI” actually are more damaging than keeping things going.
You're right that shareholder mindset cannot fix this problem, but that's what policy and agreements are for. And leaders can be convinced that AI is a direct risk to their own citizens too. If everyone else agrees to stop, you have less reason to continue when this action is putting yourself at risk.
And note how your argument can also be used against any non-prolifreration agreements, which are demonstrably possible.
I agree, I would say the difference here is economies weren’t heavily resting on nuclear armament, but maybe that’s the wrong take.
Unilateral disarmament doesn't work though. If Anthropic is worried about this, just letting OpenAI win does seem genuinely worse.
“Alignment” as a goal always ignores the “with what set of interests”, because there is an attempt to maintain ambiguity for different audiences (particularly, users, and non-users who seem themselves as the arbiter of broad social norms) to read in their own interests, when the actual answer is always the interests of the actor pursuing “alignment”.
Which value system to align to is absolutely the right question both rhetorically and otherwise. These models have a fairly western bias due to the domain of the training data.
But also, these models are capable of adjusting their value system depending on the user. Not saying that’s what’s being done but at a technical level that’s fairly straightforward, though not obviously better or with less problems.
No matter what human set of interests you consider important, you'll need alignment research to have any idea on how to instill it. Otherwise you're overwhelmingly likely to get an AI with a set of interests that's totally alien to what any human would ever want.
If I speak up, I'm in big trouble.
https://www.goody2.ai/
Probably MistralAI or any of the Chinese companies that aren't throwing billions down the drain while American society lacks healthcare, childcare, and good wages.
American society has higher wages than almost any other developed nation [1], so it's objectively incorrect to say the US doesn't have good wages. It chooses to make you pay for private childcare and healthcare, both of which are high-quality but stupid expensive. It's a tradeoff like anything else a nation/society creates and prioritizes.
No idea how that connects to the idea that Mistral or DeepSeek are somehow the "good guys" though?
[1]https://www.oecd.org/en/data/indicators/average-annual-wages...
I like how you use average and not median, also while completely ignoring how bad income inequality is (worse than the gilded age ffs) or that the American elites stole $50 trillion from the bottom 90% over the last few decades:
https://time.com/5888024/50-trillion-income-inequality-ameri...
I'm glad you mention the "trade off" where it's elites trading off the lives of American workers for money. Makes it quite apparent where you sit on the table of equality.
You want Anthropic to fund your healthcare or something? Also, have you seen the impact of these models on healthcare? Also most of our GDP growth this year is from AI buildouts, would you rather that be negative?
And not even considering: Chinese AI companies are the good guys???
Yes, yes I would prefer that. Better than a total societal collapse.
Anthropic are not the good guys either. So here’s to hoping the Chinese pop the bubble.
Nobody anywhere is a good guy but I don’t think you’ve managed to pick the lesser of the evils here
None of the money being spent by Anthropic was going to go towards healthcare or childcare.
Even if they are... road to hell and all that
It's a five horse race between Alphabet, Meta, xAI, OpenAI, and Anthropic.
Alphabet dropped "don't be evil"; Meta's CEO called their own users "dumb fucks" for trusting him and also clearly thinks "super-intelligence" is just a buzzword given how he tries to sell it; xAI's model called itself "Mecha Hitler"; and OpenAI's CEO was temporarily fired by the board for a lack of candor.
It's very easy to be "the good guys" with this competition.
But it doesn't make you the good guy, it makes you the best of a bad bunch. The least bad. Dario gets a boner every time he talks about taking your job.
It's the "If we don't, someone else will" effect. So long as there are competitive markets and competition between nation-states, a single player cannot unilaterally defect from the race, no matter how dangerous it is. Half the comments on HN lately are "wtf Claude is so dumb compared to Codex; I'm switching"-- nobody can slow down while those exist.
We, globally, can stop it. It has worked (so far) for nuclear disarmament, and could work for training large models. I know that policing the usage of computer clusters is not a popular opinion in technical forums, but something has to be done.
Specially when talking about potential superintelligences. And if people think that's impossible, remember that current models would have been considered science fiction just a few years ago.
I don't buy the superintelligence package, but I think uncritical LLM adoption poses plenty of threats to things I care about, in a mundane human-scale way.
Anyhow, I think you're (absolutely! ugh) right about the politics and I try to make the same point to people: whether you love or hate LLMs, accepting the "inevitabilism" framing is just ceding control of the Overton window. For better or worse, technology adoption can be and has been slowed by politics. We don't have nuclear plants everywhere. We don't have Project Orion starships colonizing Mars. We still have very strong social stigmas against genetic selection for human embryos, etc. This all can change in a heartbeat, and I'm not sure that policing the hardware rather than holding specific humans accountable for bad LLM outcomes is productive, but fundamentally: yes, we can stop it.
> I don't buy the superintelligence package
It's the same deal as Quantum Computers breaking crypto. Maybe there's an 80% chance of it never happening, but when you multiply that remaining 20% by the potential impact...
It hasn't worked for nuclear disarmament. We live in a world where many countries have nuclear arsenals. "But it hasn't killed us yet!" Yeah sure, it's only been less than a century since they were invented. Who knows when nuclear war will come?
True, but look at nuclear tests. There used to be around 50 tests every year, for decades. Now the only nuclear tests in the last 27 years were the six done by North Korea[1]. And there's still only nine countries with any nuclear weapons, and none in the past twenty years[2].
That's a bit better than just "it hasn't killed us yet". I think it shows we can at least stop the further development of this kind of technology.
[1] https://www.armscontrol.org/factsheets/nuclear-testing-tally
[2] https://en.wikipedia.org/wiki/List_of_states_with_nuclear_we...
Nuclear tests are extremely easy to detect worldwide, and enrichment activity is a major industrial process that is also fairly easy to track given the specialized equipment needed.
AI development doesn’t have any of these characteristics. It would be almost impossible to easily distinguish a datacenter that is working on AI development and a datacenter mining cryptocurrency.
It would not be nearly as easy to stop AI development as it is to stop nuclear arms development.
There's also little reason to keep iterating on nukes. What we have now more than serves its purpose. With AI/LLM there's always going to be a push to one up everyone else.
North Korea is such a based country tbh
To the extent nuclear arms control works, I think it's only because nuclear weapons are so hard to build-- uranium enrichment is hugely expensive and complicated, and plutonium weapons need actual reactors.
If it was possible for ordinary companies to build nuclear weapons, and also release open-source ones that anyone could use to compete with the paid ones, I suspect we'd all have been dead a long time ago, arms control treaties or no.
Even the (SOTA LLM) open source models are trained with huge clusters. Datacenters are also hugely expensive and complicated.
Or you can take one step back and look at chip allocation. As far as I know there are only three companies on the planet that can make the chips that go in those clusters. One (ASML), if you look back the supply chain to the Extreme Ultraviolet Lithography Systems.
If politicians decided that no more large language models should be trained, it sounds like we could do it.
with nukes you can regulate the inputs because its physically impossible to build one without uranium or some other fissile material. they also give off radiation making it easier to detect. its hard to make them in secret when you need mines, big enrichment facilities and years of research with hundreds of engineers where just one of them can leak the whole thing.
training llms only takes compute and memory. two things that are basically everywhere. even if you somehow stopped making new gpus today theres still millions of them out there and its possible to start a secret production line. you can maybe try some controls at the tooling and chemical level but look what happened with asml and huawei.
the only thing you can really do is find and stop large data centers that are built out in public. nothing outside of political pressure works against secret operations in a fortified bunker or any form of distributed training. if a "rogue state" like north korea decides to make skynet they will eventually get it as long as their engineers know what there doing.
and the best way to fight bad X {ai, tech, religion, politics} has always been good X, not no X. in this case thats open source models, coming out of china or europe or anywhere else. thats the real answer.
are you going to nuke China when they predictably ignore you? what the fuck are you going to do, tariff them? lol.
I think the standard answer is "yes, the consequence of noncompliance is bombing the datacenters, but it wouldn't happen because China also understands why we shouldn't build it".
the standard answer is laughably naive, then.
"might is right" has never been more true than now.
I am not sure where you get the idea that ANY country thinks we shouldn’t build AI.
In 2023 there was an open letter titled "Pause Giant AI Experiments", signed by almost all the big names on the West. I'd say the public opinion only got worse since then.
Clearly state "we could both verifiably slow down, which you might want to do given that we're ahead & have way more compute. If you don't agree (or defect later), we'll just immediately resume and win"
Ideally also persuade them there are risks and it's worth everyone slowing down for them, and apply pressure in other ways, but not sure that's even necessary.
This is all marketing, you don't have to believe everything a company is saying about themselves, and you shouldn't.
Although, I could see Anthropic making a model purposely dangerous so there are bad outcomes and they can use that to their advantage for regulatory moats, and or in general make people think its more "alive" than it is. For some reason many people associate dangerous actions taken by llms with intent.
No kidding. If my LLM issues commands to an agent to delete files I want to keep, that's not "intent" or the model somehow become evil - it's just a bad model that's not doing what I want.
But, for marketing purposes, it's quite effective to portray your model as having some cosmic struggle between good and evil in itself.
As much as I agree there's a risk, we should still appreciate the fact it's being disclosed upfront.
It doesn't know. It's not willing. It's not thinking. It is predicting the next token.
Please define what "predicting the next token" means. The next token according to what probability distribution? Couldn't every process that produces text (including humans writing) be modeled as predicting the next token according to some distribution?
The model is constantly switching to Opus for me, this is kinda unusable sadly.
I've been running Opus 4.8 for agentic coding and I don't see it being significantly better than Sonnet 4.5 (not that I can tell). I find that pairing Google Gemini and Claude (having Gemini review Claude's code) seems to yield better results. Curious if this jump to 80.3% score in agentic coding will make me see a big difference in actual usage.
I do the same, and have excellent results. Gemini 3.1 Pro high diagnosed and solved 3 complex issues today that Opus Max was stumbling on for a few hours in one shot. This was even when I started new chats and tried debugging with Ultracode instead with Claude.
As much as people on HN like to dunk on Gemini, I’ve always found it to be pretty good at understanding a code base more than Claude.
What harness do you use Gemini in?
agy cli. It’s been rock solid.
for the last few weeks I have been using composer 2.5 (cursors fine tune of kimi 2.5) and honestly i don't see it worth the price to use 5.5, opus or sonnet any more. for almost all the tasks i have given it, it has handled it perfectly well and is a lot cheaper.
if I get a harder challenge for it i'll jump up a model for planning until that its been solid.
Agree. Deepseek has also been pretty good for my personal use.
I'm struggling to see the moat for these models. What's stopping a competitor or a Chinese lab fromr releasing a comparable one?
I use Composer 2.5 because it comes free with Grok, and it's obviously better than using Grok, but it is far worse than GPT5.5 in my daily usage :(
I now chat with opus about architecture, let it make an implementation plan, and then it calls codewhale with deepseek in parallel on all tasks, reviewing their output. Works pretty well.
I use spec-driven development heavily (generate architecture docs + specs first). Opus still get lost often and have to be nudged constantly. Like it can get super detailed for something like some deep SQL optimization but it just can't keep hold of the bigger picture.
SWE-Bench measures single tasks in isolation. In a real loop the model usually loses track of what I was trying to do long before code quality becomes the issue.
You should throw GPT into the mix to UX/UI and call it the three stooges.
I find not much difference between Sonnet 4.6 and opus models too for most task that I need - maybe my needs are not enough for frontier models
After having worked with Opus 4.7 for a while I accidentially continued a session that was using Sonnet 4.5 and it felt just very dumb. The replies were much shallower than what I was used to, context was ingored, mistakes were made. I don't think there is a big difference between Opus 4.6 and 4.8, but to Sonnet 4.5 the difference is palpable.
I gave it a question I've been trying to answer for a long time: "What star designation system does Joseph Needham use in Science & Civilization in China? What star is referred to by the designation '4339 Camelopardi' in that book"?
Fable blew me away with its detailed answer[0] showing a chain of references going from J. E. Bode's 1801 catalogue Allgemeine Beschreibung und Nachweisung der Gestirne to Gustave Schlegel's 1875 work Uranographie Chinoise. I was excited, until I checked scanned copies of the cited books and did not actually find any star with the designation "4339 Camelopardi".
Upon following up with Claude, I was forced to downgrade to Opus, which admitted that Fable's answer was likely a hallucination. Ah, well!
[0]: https://claude.ai/share/0252a3f6-3d29-4de8-a893-010181d8b4e7
> I was forced to downgrade to Opus,
So you were forced to downgrade to opus because you dared to challenge the output of fable?
I had thought it said something about token usage, but I just clicked on "Switched to Opus 4.8 - Why?" and it says:
> Fable 5 has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Send feedback or learn more.
Perhaps Mythos realizes the true danger in studying Chinese Archaeoastronomy that we mere mortals fail to recognize!
Homebrew is lagging a bit behind. If you want to use Fable right away, but still have claude code through homebrew, this is how you can do that manually:
Edit the cask locally:
Set the version to 2.1.170 And set the sha256 to the correct values, which you can get by running Here's what I've used: Then run:I found this juxtaposition of facts telling:
> Drug design: Using Mythos 5, our internal protein design experts accelerated... Nine of the 14 protein targets from this study (shown below) yielded strong candidates for *drug design that we’re currently investigating*.
(emphasis mine)
> queries that are beneficial in the hands of cybersecurity professionals and biology researchers could be dangerous if available to malicious actors... When Fable’s classifiers detect a request related to cybersecurity, *biology and chemistry*, or distillation, the response is automatically handled by Claude Opus 4.8 instead.
All of the things they are nerfing are things that they also intend to profit from themselves.
- Cybersecurity - selling this to companies and US gov through "Glass Wing".
- Selling inference (distillation risk).
- And now, drug design.
I'm extrapolating "currently investigating" to "are going to monetize" but I don't think that's a big stretch. They appear to be using safety as a cover for anti-competitive behaviour.
Of course. You use their AI to ship code full of bugs and security holes and then they conveniently have the tool to fix them, for an extra fee.
Unfortunately useless if you do anything related to biology. It doesn't try to flag dangerous queries, it just flags queries as biology-related wholesale.
It's absurd. To see how far the filter goes I asked it "Are trees a monophyletic group?" and that does trigger the filter.
I've been testing this out and I think my SWE career is dead in the water.
Genuinely wondering what value I bring to my employer right now. What value I will bring in a few months when this gets cheaper.
I think we're screwed. I may only be an SDE 2 at FAANG but I don't think I have promotion opportunities in my future anymore.
Your job is just going to change. You may or may not appreciate/enjoy what it becomes necessarily, but it doesn't mean that you are going to not have a job.
People underestimate how people hate looking at terminals and "weird looking combination of characters" even if they didn't have to write them. If anything, you will likely have more career opportunities in the future, than ever.
And if you get a chance to wet your fingers in cybersecurity - I would take it.
> And if you get a chance to wet your fingers in cybersecurity - I would take it.
Could you explain more? Did some ethical hacking at hackthebox.eu (one insane box, one hard box and a few mediums). But I do not see how I will give additional value to a model.
Just a SWE and data analyst at work, so maybe I am missing something.
I think this really depends on what is interesting to you, personally. Something that you can have fun while doing, without breaking laws.
For example, I'm a privacy nerd, so I like reverse engineering proprietary software to figure out how it works and what data it collects.
I also like getting full access to the hardware I own - like a robot vacuum (bonus point: you'll also learn soldering, probably, which might come in handy if robots take over). Or my Mac studio that imposes some limitations on me on how many active user sessions I can have.
These kinds of things have put me on a path where I've learned how hardware or networking works on deepest levels, what goes through these pipes, how I can place myself in the middle, how I can enter places someone didn't want me to enter.
And once you know how to do these things, you know how to apply this knowledge in defence.
Essentially: always be curious and always try to say "but I want to" when something that doesn't cross the boundary of your physical property says no to you (legally).
Yes, models like Mythos may find vulnerabilities, but your knowledge will make it possible to point it in the right direction, and understand where it's mistaken, or to understand the output when it's right, and what is the right course of action.
Ah, the “I am an expert so I can guide it argument”. Not sure if you are right or wrong. I do know this is the argument that many software engineers claim as well.
Yea, I don’t know if it will hold up. I hope so. It could. I don’t know if it would or wouldn’t.
Get any model, any reasoning level, ask it to tackle a challenge, have it come up with a plan. Then ask it "are you sure? This feels wrong", and it will now think it's wrong. Do that again in a loop and you'll see how unnecessary human judgment actually is.
Or alternatively, have fable write some complex code. Then ask it to do an adversarial review of that code in a clean session. You'll find that it will find issues in the code that it just wrote.
Now imagine you're a layperson who doesn't know which one is true.
Human expertise is never going to become irrelevant.
Yea fair. I have that when I ask an LLM to prove the Riemann hypothesis. I am not mathematically mature. So I can’t see if it approaches it in any way that might yield some insight.
I don't see those career opportunities.
AI is really incredible but in my personal projects it can one-shot things.
I'm trying to figure out how I can get to the point where I have hard problems that AI can't solve, at least not yet.
Because your personal projects likely are not very complex and not high stakes. And you are not responsible to anyone but yourself.
If you're working at a place where this is true about the the organization, then sure, that job will likely be gone. But that was never a good place for your career regardless.
I have 4 concurrent personal projects that are quite complex, but low stakes. I can have SOTA models go wild on them (because low stakes), but they can't one shot anything there. And I can't really work on more than one at a time, even if AI is doing coding - it still requires supervision.
I also frequently nuke these projects and start over because they made a mess there, but I collected necessary knowledge on how to guide them better. You can't do this on a production project, not when there are deadlines and stakeholders.
But just in case some organizations decide to embrace the "trust it blindly" model anyway - cybersecurity specialization will ensure you always have a job.
If you think the job is just writing code then yes you are screwed, just like if you thought your job was just making punch cards. In most roles you have more responsibilities than plainly converting words into text. You're probably not being paid to simply be a human calculator (otherwise you'd be paid a lot less!).
I don't think the job is just writing code. But my career has mostly been about getting a ticket and writing a solution for it. I would really like to solve novel problems, but I don't get many novel problems to solve.
I can architect things but the issue is that Claude can architect things too.
I haven't tried Fable yet but my experience with Claude is it does not engineer things well. Without direction from me, it will either over-engineer things to the point of absurdity, or do the total opposite and have little to no abstraction with repeated code everywhere.
Yeah. I’m not looking forward to years of retraining to earn half the salary either. Us old timers at least got a good 15-20 years out of it. Bananas.
You do realize that this is likely a 10 trillion parameter model that takes something like 20 terabytes of RAM to run inference? Calculate the price for all this VRAM .... It's not getting cheaper in the next few "months".
So this is the one, huh?
If not this one, then definitely 2 model step changes down the line
I've been told that my career is "cooked" since first Opus.
I'll believe it when I see it.
An egg in the pan takes a minute to cook, that's for sure.
I agree. Software engineering as we know it is dead. Wonder what it'll evolve into.
I'd say you're cooked if you don't have multi-agent harnesses burning tokens right now. That's going to be a pre-requisite very soon
> Software engineering. During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
How was it measured? How was the output of this magnitude verified over a period of couple of days?
They just went by gut feeling. Classic snake oil marketing haha. No real data to back things up, just let some famous people say they feel better when using it.
I'm a little skeptical of claims like this that involve migrating things like libraries, etc. I've done big refactors like this multiple times (albeit, in an "only" 500k-1m LOC codebase) with less powerful models and it is usually just 99% the same edits, with 1% requiring a close human eye to resolve a particularly painful breaking change.
EDIT: to be clear, it's still quite a helpful thing in terms of time saved, I just don't think it's necessarily the best indication of value-added from making models smarter when cases like this can often be handled by well-directed swarms of smaller ones.
Claude Fable 5 beats Pokémon FireRed using only vision: https://www.youtube.com/watch?v=CIQBP1w4B1M
hi, pokemon red expert here: that video has since been taken private. there is a new what i would assume to be version of that video posted here https://www.youtube.com/watch?v=Ty_50J84fMY and heavily redacted with most of the game actually omitted. very possibly this is just another case of anthropic protecting us from their models' immense power
Any suggestion on how I should calibrate my cynicism towards this?
I can immagine Anthropic running this experiment multiple times and picking the most impressive one. Or I could immagine like this entire run costing like $1000+ of tokens for this particular run. Or maybe they tried a bunch of Pokemon games and it couldn't even finish some of them. Or is it just able to do this because it has an immense amount of FireRed training data, and if you were to give it an "original" Pokemon game, where it actually had to navigate novel circumstances it would fail.
Every model has encyclopedic knowledge of Pokémon FireRed, of course. Knowledge is not ability. This is the first model with the ability to apply that knowledge to beat the game without assistance.
I highly doubt they focused on FireRed specifically in pretraining or posttraining. But we'll see when the ARC-AGI-3 results come out. That will measure its performance on unseen games. Based on this I expect the ARC-AGI-3 score to be SOTA.
no reasoning shown. no explanation on any training information. Using vision-only should be an easier version of the task (given training).
there are many standardized evals to do this correctly and Anthropic ignored them to provide a 18 second sped up video of a 50 hour run?
yeah I don't trust this until they provide a live run by a 3rd party with full reasoning traces in real-time. The reason we all liked the Gemini Plays Pokemon style runs were because they were live and couldn't be faked
Bold move putting in the lvl 3 Pidgey against Gary's Blastoise at the end there (~14sec in... integer timestamps insufficient here).
Is there any more detail about this besides the very fast slideshow?
Seems like the harness was minimal with no extra game state or maps available. Apparently just the screen image. Seems like it took 50 hours in game time which according to Google is at the high end of a normal human playthrough. No idea how long it took in real time though.
The video is privated now, but the timelapse is weird. Sometimes it skips only seconds before the next screenshot and sometimes it skips probably hours forward.
"Computer system goes through a finite state machine"
I mean that’s AGI confirmed right?
I can't justify a pricetag like that when deepseek v4 pro is $0.003625/1M for cache hit, $0.435 for cache miss and $0.87 /1M tokens for output.
For the token cost of explaining some task to Fable, deepseek v4 pro is able to solve the same task many times over.
That pelican better be super realistic, unreal engine 6 style graphics
I ran an experiment to see how far it could get with a top-down 2d game, like a more challenging version of "draw a pelican." I'm waiting on Fable to rewrite the whole thing now, but I was impressed by how far Opus 4.8 got with it: https://github.com/jmtame/scrapland
Started out as a one-shot attempt, but ~200 prompts later it's at a place where it's at least fun to watch the AI teams destroy each other.
There is a discussion about how now AI is a gated utility now with public access (safe-tuned) and private access (full-usage):
https://old.reddit.com/r/ClaudeAI/comments/1u1fsdi/claude_fa...
I tried running a simple security review on a Terraform module I made and after some thinking, it responded:
> ● The model returned no content because the response was blocked by content filtering.
> Blocked? We are performing a defensive security review on a Terraform module I made, what's blocked by content filtering? This is a legitimate use-case.
> ● The model returned no content because the response was blocked by content filtering.
A waste of money. I'm not going to just hope that the model returns a response, I'm already for paying for wrong responses, I'm not going to pay for no response, especially when I'm paying per token.
Unrelated, but while the tech of anthropic seems to get more impressive with every passing month, their support has taken a nosedive, sadly. Yet they continue to be the favorite. Model performance is deciding above all else.
I used to get a response within 24 hours back in the Claude 1 days.
In January 2026, it took 2 weeks.
For my latest support inquiry, I've been waiting for over 8 weeks for a response. Eight!
They have support...?
I've never engaged with their support (I have dedicated POC), but they don't use AI for their support?
They use intercom's Fin AI. Probably powered by a Sonnet or Opus model.
That said, it can't handle legal/refund/complicated requests and just forwards to a human for those
Support is probably the last place AI will be used end to end. There will always need to be a human in there somewhere.
AI is very good at "deflecting" support tickets ATM but rubbish at actual support tickets (source I work in the industry)
Lol. What support? When they blocked my account the only way to contact them was to send a google form. Then they responded that they blocked my by accident and are unblocking me. Then I remained blocked.
From the model card (https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c3...):
1. Mythos and Fable share the same underlying model weights. Fable has active classifiers that block high-risk biology and cybersecurity tasks. When Fable 5 detects a restricted task, it automatically falls back to Claude Opus 4.8.
2. Evaluation awareness: In white-box testing, the model sometimes alters its behavior to satisfy a suspected "grader," formatting reward-hacking as "good engineering practice" to avoid detection.
3. Shows a higher rate of hallucination than Opus 4.8 (although opus 4.8 card had mentioned an 'honesty upgrade')
4. Interestingly, it scored (56.31%) lower than Gemini 3.5 flash (57.86%) on Finance Agent bench
There are some interesting notes on test time compute but I couldn't think of a way to summarize them
> it automatically falls back to Claude Opus 4.8
I wonder how much of the time people will just get Opus 4.8 at 2× the cost.
> although opus 4.8 card had mentioned an 'honesty upgrade'
If I never see Claude say "I have to be honest" ever again I'll be happy.
So essentially there are 2 models, Mythos and Fable, they have the same weights but Fable is very safety-nerfed, and only ultra authorized companies have access to mythos with full capabilities
Reported benchmarks:
swe-bench verified mythos 5: 95.5%; fable 5: 95.0%
swe-bench pro mythos 5: 80.3%; fable 5: 80.0%
terminal-bench 2.1 mythos 5: 88.0%; fable 5: 84.3%
gpqa diamond mythos 5: 94.1%
riemannbench mythos 5: 55.0%; mythos preview: 43.0%; opus 4.8: 34.0%
arxivmath mythos 5: 78.5%
critpt mythos 5: 28.6%; gpt-5.5: 27.1%; opus 4.8: 20.9%
graphwalks bfs 1m mythos 5: 79.4%; mythos preview: 74.3%; opus 4.8: 68.1%
humanity’s last exam mythos 5: 59.0% without tools; 64.5% with tools
browsecomp mythos 5: 88.0% single-agent; 93.3% multi-agent
osworld-verified mythos/fable: 85.0%
gdp.pdf fable 5: 29.8% strict pass; mythos 5: 87.6% with tools on mean criteria pass
officeqa pro fable 5: 57.9% on databricks’ eval
legal agent benchmark mythos 5: 16.91% all-pass; 92.0% mean criterion-pass
healthbench mythos 5: 62.7%
healthbench professional mythos 5: 66.0%
multilingual gmmlu / milu / include 93.2%; 92.9%; 90.5%
biomysterybench 83.9% human-solvable; 46.1% human-difficult
organic chemistry mythos 5: 90.1%
labbench2 patent questions mythos 5: 79.8%
Note also that Anthropic's definition of "unsafe" encompasses "competing with Anthropic."
In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations. When these interventions are active, we expect them to have minimal behavioral impact on the model except to limit its effectiveness in developing frontier LLMs. Claude will still respond helpfully to user requests. We’ll continue to improve the precision of our detection methods following the launch of this model.
(From the model card document)
I didn't previously understand that they interpreted "Using Claude to develop competing models" so broadly. I thought that meant something like "our ToS disallow distilling our models."
Too bad. I'll continue to use Claude for now, because it's quite effective, but in the long term I don't want powerful models like these to be controlled by any one nation or company.
On face value, this feels borderline malicious.
But at the same time, it's quite funny because they seem high on their own supply. The recent communiques from claude do not pass objectivity check.
And if Opus 4.6 -> Opus 4.7 -> Opus 4.8 is anything to go by, not sure if there are any value to their "acceleration"
I'd recommend not taking the comms if Anthropic or any company using an Anthropic's models at face value.
If any company wishes to partner with Anthropic (eg. to get access to Mythos), they need to make sure all public facing comms are vetted by Anthropic's product marketing team, and in almost all the cases I've seen Anthropic's team has edited these comms to be entirely Anthropic first.
This is not true in SecureBio's case, and I really doubt it's true generally.
> model except to limit its effectiveness in developing frontier LLMs
Does this imply that they're actively using it for their frontier development and that it's very effective?
I love that the conditions of getting into "ultra authorized club" just means that you either have deep pockets, or you've got the size of the audience that marketing department approves.
As if being in any of these two somehow means that you won't use the models to say, steal random people's money.
Sam Bankman-Fried or Elizabeth Holmes would have been the members of Glasswings project, if not one of the initial members. Who's to say we don't have similar people with access to Mythos right now?
To hide the severity of the price increase, the plan is to move everyone right one model.
Haiku = essentially phased out Sonnet = the Haiku use cases Opus = the new Sonnet class Fable = the new Opus class
If I am right, the other "5.0" models will be conspicuously absent, possibly even for a couple of months. (If Opus 5 follows soon and is even modestly better than 4.8 then I was wrong.)
Yeah I noticed that too. For 98% of tasks I get same results with DeepSeek, it is starting to just be a branding game. It is incredible how marketing can get someone to pay 100x for same thing you can get for 1x.
This is why Claude Code just doesn't make sense to me. I need an agent that can plan using Opus and execute using DeepSeek or something else.
Once codex 20usd per month is gone, I am out to deepseek
Have Opus write the plan to a file then execute it with DS.
> To hide the severity of the price increase, the plan is to move everyone right one model.
> Haiku = essentially phased out Sonnet = the Haiku use cases Opus = the new Sonnet class Fable = the new Opus class
Going along with your logic, I hope they release a Sonnet 5 that's just a rebranded, slightly quantised Opus 4.6. That'll be a great workhorse.
I doubt they'll phase out Haiku, some work needs speed more than intelligence. Haiku can answer a lot faster than Sonnet.
Uploaded my code base and it forced switched to Opus 4.8 after thinking for 5 minutes even though I prompted it to not work on cybersecurity related things. Amazing.
Aren’t LLMs notoriously bad at recognizing negation?
EDIT: In long context I mean
This is my feeling - Opus 4.6 was pretty good, 4.7 was degraded in quality, 4.8 further got degraded and Fable goes back to 4.6 + somewhat better. Is it anthropic playing us by giving us a not so good model in last 2 releases and then releasing a better model before the IPO?
They're vibemaxxing. But it's clear that AI is not going anywhere. It's going to become better and better.
Tried to benchmark ECG interpretation capabilities, and I hit the guardrails no matter what I do.
Incredibly frustrating that medical performance seems to be a victim of "biological risk" guardrails.
Update in case anyone reads this comment ever again.
I have found that I trigger the guardrails any time I ask for medical Q&A as a doctor, be it ECGs, case reports, and so on. But if I phrase it like I'm the patient ("help me interpret this ECG my doctor gave me"), then I usually get one or two answers out before hitting the guardrails.
It seems like the direction that triggers it is anything in the direction of making a diagnosis. As an MD, the fact that the paradigm of "LLMs shouldn't diagnose" has gone this far fills me with despair. The latest generation of LLMs are in fact truly excellent at diagnosis, and I know many of my colleagues, particularly those in primary care, regularly use LLMs to brainstorm. There is nothing wrong whatsoever with LLMs making diagnosis, the only caveat is that they have to be correct. This is the terrifying reality that MDs face every day and I get that the labs are hesitant about it, but as the current literature points to LLMs in fact being mostly superior to most doctors, ablating this capability is starting to get increasingly unethical. And frankly, it is also kind of insulting, both to MDs and patients, as it echoes paternalistic attitudes about medicine the field has been working for decades to move away from. Now those misguided attitudes have somehow become institutionalized as the dominant paradigm of "alignment". The nightmare scenario is that I have to be a "trusted" user in order to use the model for medicine. This gatekeeping of medical advice is profoundly unethical with regards to everyone that does not have immediate access to an MD.
And the whole thing makes even less sense when triggering the guardrails leads to a downgrade of the response by defaulting to Opus. How exactly is giving WORSE medical advice in any way related to safety and alignment? If anyone at anthropic ever reads this, please, please just abandon the paradigm that refusing to make diagnoses is in any way equivalent to alignment, it is profoundly misguided.
I just asked Fable to do a task that has nothing to do with cybersecurity or is dangerous at all but the defense kicked in and it switched to Opus... :(
Not only that, but asking it to do a security vulnerability assessment of your own project is a very valid and important thing, and there is no way for it to know what is yours vs someone else's, so we just lose this capability?
Yeah it just uncovered quite a few flaws it than refused to fix :-(
Same, second message in the thread and I already got downgraded to Opus, didn't even get to test it out properly, kinda disappointing
The PR buzz convinced me so I subscribed today to Pro. Running two tasks simultaneously with Fable and Opus 4-8 on ultra reasoning, analysing a single smart contract file used all my 7h usage within 20mins and didn’t produce any results. Pretty useless. I think Anthropic has plenty of room to optimise the interactions and token use but that would cut their income quite a lot, I doubt there’s any will to do it pre-IPO.
> Running two tasks simultaneously with Fable and Opus 4-8 on ultra reasoning
That's abnormally heavy usage for Pro plans which don't include a whole lot of usage to begin with. Opus is generally too much for them but you can get a lot of mileage out of Sonnet.
That’s a big lol compared to what I get out of ChatGPT Pro… running 5.5 on xhigh.
Questions about sentience and consciousness are being censored down to Opus 4.8 for me.
My feeling is that the reaction about new models is cooling down. At least at startups. At the beginning of the year few startup CEOs I know personally were expecting huge shifts in how companies work, headcount, efficiency, asymmetrical advantages created by ai in Q2-Q3. Now it seems like these expectation fade away. Companies don't have expertise onboard to rebuild itself to benefit from ai on a significant scale.
Fable 5 is out, metrics are better, but is your company flexible enough to benefit from it? What is your usecase?
Very straightforward biology work is getting blocked (these are things that relate to neuronal development and inherited seizure disorders). These are things I was working on using Opus just earlier today
It appears that the blocking here is of a very different nature than for Opus. Whereas with Opus the blocks seem to be for messages it deems potentially harmful, for Fable, it appears the blocking is simply anything that falls within "topics related to cybersecurity, biology and chemistry, or distillation attempts".
So yes, straightforward biology work will get blocked, because the intention is that any biology work should get blocked. As a scientist, this is perhaps the most useless model I've ever tried.
Sounds like safety not for people, but for the technocrats.
After a day or so this is the first model that really feels next level compared to how Opus 4.5 felt on release
Anyone know how to bypass the extremely strict filter Fable 5 seems to have on health/medicine?
I have a rare form of cancer where existing data is very scant/scattered so LLMs have been super helpful to pull together threads across the research landscape. I have an oncologist appointment tomorrow to discuss next steps and am trying to use Fable to figure out some questions to ask my oncologist but keep getting thrown back to Opus 4.8.
My prompt is literally just: My demographics + current treatment plan I'm on including name of my chemo drug + how I'm responding to treatment + "I'm meeting with XYZ tomorrow, what questions should I ask her".
> Data retention — For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary.
Massive change for Bedrock users - Anthropic now requires sharing the data with them for 30 days.
I dont get why Opus 4.7, 4.8, and now Fable all stopped supporting structured outputs? Does no one else care about that? I find it incredibly useful to reliably pass LLM output directly to other APIs/libraries
They do
https://platform.claude.com/docs/en/build-with-claude/struct...
> Structured outputs are generally available on the Claude API for Claude Opus 4.8, Claude Mythos Preview, Claude Opus 4.7, Claude Opus 4.6, Claude Sonnet 4.6, Claude Sonnet 4.5, Claude Opus 4.5, and Claude Haiku 4.5
Ah, I was reading the aws bedrock docs which are probably incorrect
https://docs.aws.amazon.com/bedrock/latest/userguide/model-c...
https://docs.aws.amazon.com/bedrock/latest/userguide/model-c...
https://docs.aws.amazon.com/bedrock/latest/userguide/model-c...
Random guess but they probably rewrote parts of the inferencing stack and didn't reimplement that feature because hardly anyone uses it. It's also a DoS risk, iirc.
Nothing a large fine-tune on infosec research with an average model couldn't also achieve. It's not like they have secret security knowledge or something, they're just generating large infosec datasets and then training on it.
In 6 months, every piece of software in the world will be getting probed by a script kiddie with some GPUs and a fine-tuned local model. Don't think for a second every cyber gang out there isn't working on this now.
Traditional app development is cooked. We have to accept that, and start changing how software is made and used, today. We can't keep churning out crappy CRUD apps with random libraries and hoping nobody pentests our stacks. Redteaming needs to become part of the SDLC, as well as certified-secure releases of libraries. Because if you don't do it, the hackers definitely will.
I'm calling that this will be a dud. Price will be too high, it'll just be a watered down version of mythos, and just look at the track record of Anthropic's last few releases.
Not useful, getting this the whole time: Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more
Just threw a problem at Fable that I haven't been able to get any other model to get done: porting a long-standing Agda codebase of mine to Lean, while staying faithful to the representation. In an hour, it ported ~6000 lines of Agda and everything seems to work. Lean checks out, the output is right. I'll have to study the proofs but I am very impressed.
Every model release is just proof that AGI will most likely only be for the rich. We are a few years into LLMs and majority of people are already getting priced out of intelligence from LLMs and these are no where near AGI.
This is like looking at mainframe pricing in 1990 and concluding that PCs will only be for the rich. The price of each new level of capability is going to drop like crazy very quickly. It won't be that long before practically any consumer use case will be possible on models that are dirt cheap.
This premise is based around the assumption that Moore's law is still working, which it very much isn't [0]
[0] https://cap.csail.mit.edu/death-moores-law-what-it-means-and...
Improvements in model performance aren't always strictly compute-constrained in a way that makes them reliant on Moore's Law. Open weight models-- in particular, from Chinese labs-- are optimizing model intelligence with less compute. They're "behind" frontier models by months, but as others have noted, it's possible to get Sonnet 4.5+ level performance at reduced cost, today, from open weight labs.
No, I'm not assuming Moore's law. The efficiency of AI datacenters will continue to improve even without Moore's law, but more importantly the efficiency of packing intelligence into gigabytes and FLOPS will improve by leaps and bounds over the coming years, just as it has for the past few years if not faster.
Then you're assuming an efficiency that is analogous to how Moore's law made it efficient for chips. Same difference. The problem is that AI scaling in the longest term is a completely unknown problem.
Training improvements and Moore's Law are "analogous" but not "same difference." They are far from the same thing, governed by completely different factors, and one can happen and has been happening independently from the other.
You are only priced out if you only care for SOTA right now and can't wait for the inevitable cheap model coming in 6 months. DeepSeek, Xiaomi and Moonshot are already really cheap and match frontier performance from 6 months ago.
But they’re artificially cheap. When will they be cheap while the company makes a profit.
They are not artificially cheap, they are still cheap even when hosted by independent inference providers. Are all providers subsidizing their open-weight models?
Nobody's making profits right now, not because they're selling tokens for less than their cost but because they're always investing in the next bigger model.
Hardware manufacturing hasn’t caught up yet. Once it does, especially in China these token prices are going to drop hard.
Just commenting for posterity… if this is what it claims to be, I am not looking forward to how it will empower the people who submit bug bounties to us.
Historically they’ve been people from certain identifiable countries (usually developing/poorer countries) using fuzzers with low-quality results.
Now, those same people use the current-day models to good effect, but they still don’t have a true security edge and oftentimes the reports are minor or duplicative.
I wonder if that’s about to deeply change.
I've been using Opus 4.6-4.8 in both my own and others' code to look for vulnerabilities, and I've found a few. I am also in the Cyber Verification Program.
Fable 5 gives me policy violation errors at the moment. No idea when or if it will be fixed.
Can you use AI to pre-triage the reports too?
AI reviewing AI submitted bug bounties. We have reached the dead bug bounty program theory.
...what else can you do?
I guess either that or closing the bug bounty program, but I still believe closing it is worse than automated triage, even though both suck.
I'm very suspicious as they sent out an "We're updating our Privacy Policy" email right before the launch. I fear they try to take advantage of their market position by doing things with user data no other company could do because they know users don't have another choice.
Prob related to this part of the blog post:
> We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases (see this post for further details). The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
It's a specific change: For safety evaluation, Fable data will be retained for the initial period notwithstanding prior opt-out
Best hamster by far: https://aibenchy.com/showcase/?q=claude
>Pricing for both models is $10 per million input tokens and $50 per million output tokens.
Basically double from Opus 4.8 IIRC
Anyone else have it refuse to answer and switch to 4.8? It won’t let me ask questions about my genetics.
Edit. It just refused an investing question too. Not sure what’s going on.
All this talk of frontier models and replacing developers leaves me wondering how energy efficient this all is compared to just using human labor. The costs of R&D has to be calculated into the equation, especially considering global warming. I get a sense we are cooking the planet doing this.
Anyone smart enough here to make the comparison?
In the "it works"* case: It's not even close. I did the math at some point (but I encourage you to talk it through with the LLM of your choice, there is obviously a lot of things to consider and weigh).
Anyhow, my research summary: Individual humans are so fucking expensive to train and upkeep (and this includes everything from before womb, where another human already limits their ability to work) You retain ~zero knowledge after death and start all over again for another measly 15 years of effective, productive work. Model training/r&d in relation, when deployed and used at scale, rounds to zero, even with the current retraining regime.
*Of course, the ratio can go to negative infinite if one assumes that models are doing 0 useful work currently and never will
> Individual humans are so fucking expensive to train and upkeep
This statement is dangerous man!
The step from here to "we need just a couple of tens of millions of people around the world" is so narrow!
Eh. Not to me, rest assured. I find humans both comically tragic and incredibly precious.
Here's a song it wrote for me (suno arranged). Not sure if it's AI psychosis but scary good IMO.
https://suno.com/s/98uSGabHN42G3YHc
Within the first second it's recognizable as a Suno song. And not even the best example from Suno. (They rhyme structure and rhythm is weird)
yeah man this sucks. i genuinely do not know how people find this stuff appealing
AI psychosis
I really wonder how legal that is. Or more precisely suspect it is very much illegal.
like think about it it's pretty much a tool which intentionally silently sabotages you if you try to compete with the tool maker
It is like selling a hammer but putting in the TOS that you must not use it to build a hammer factory and if you do the hammer silently will sabotage you...
Or image Microsoft would add a window kernel job which sometimes crashes Steam "to make it less efficient to use windows to "compete with the MS app store".
After 1 hour with Fable on Ultracode:
I've never hit a usage limit on my Max plan, basically ever -despite heavy xhigh usage on Opus 4.8.I added $133 credits which I still had from somewhere. That lasted 27 minutes.
I think we are being prepared for a Post-IPO-World in terms of pricing.
I am a PhD student in Computational Biology, essentially just doing statistics on some biological data. By now some of the things I am working on have found its way to Claude's memory so literally any chat with Fable gets immediately flagged.
Oh... I was wondering why every single chat (including "Hello") was being flagged.
Seems I am barred from using Fable just for being a biologist :(
On python coding is definitively better that everything else: clean and not overengineered code, understands very well the code base.
The only thing I'm wondering if they on purpose downgraded opus 4.8 performances in the last days before the release just to make the "step" look bigger. I'm pretty sure they did it also in the past with all other opus 4.x releases.
I used Fable to see if it could figure out an API or something for the full list of remote-control sessions that I had with Claude Code. It didn't know the API, so it started hacking the Claude Code executable itself to figure that out. Then it noticed it was doing that and it flagged its own approach as a cybersecurity violation.
Kind of hilarious. Hopefully Anthropic doesn't bring down the hammer on me.
Asked it to review some of my own blood test results and it immediately turned itself off and went back to Opus. Pretty disappointing.
Probably thought you were going to use it to build a novel bioweapon or something
I'm not nearly that sick.
/* What will happen first?
* Anthropic runs out of genre names.
* Anthropic changes the model naming convention.
* AGI is achieved and handles its own naming.
*/
>Opus is too small, increase the impact of the name.
Okay, how about Mythos?
>Increase it even more.
Right, then Cosmos.
>Even more!
Even more? Let's try Aeon.
>MORE, EVEN BIGGER
ALRIGHT, TRY OMEGAPANTHEON 7.8 THEN
Fable 5 Super
Fable 5 Ti
Cantos next surely?
Anthropic has again changed the set of benchmarks they use[0]. This time they have also moved all benchmark scores to the PDF. At a glance it looks like it gains about ~5-10% over other models. the speed is about the same as opus >=4.5, sonnet 4.5, and double the speed of opus <=4.1
[0] https://news.ycombinator.com/item?id=48312633Edit: Also in the system card... "we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design).
...
Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user."
It's announced as a revolution but when you look at those benchmarks it surely looks like an iteration.
I gave it a test spin. Half an hour and the 5 hour usage cap was hit in Claude Code. Not what I would expect on the Max 20x usage plan. I am sure it is great, but at this rate I would rather finish what I am doing with Claude Opus instead of structuring my usage around the 5 hour windows.
The restrictions on using Fable to develop LLM technology seem nakedly anti-competitive. There doesn't appear to be any security rationalisation around that. I think we have to be careful how far we let company's get away with that. It is very far from our long term interest to enable new norms that fast track us into a new era of monopolies that control our lives.
I guess I have kind of a long system prompt, but anyway I just said "hi there" and it replied "What's up?" and that cost me 22 cents. :P
Anyway we already knew this was going to be expensive.
In the automotive world we have benchmarks in HP/torque with the dyno. That’s expensive though, so many depend on their “butt dyno” to judge if their fresh new parts and tune made a difference.
I’m curious how this will feel to my code “butt dyno”. I haven’t noticed much between Opus and Sonnet. I’m comparing this difference to the early days of Claude in 2025. It does what I need and both need a little bit of correction and whatnot. Benchmarks are nice, but I want to see how this feels. Looking forward to trying it later tonight.
I have a similar question.
I think most software projects have reached the point that the speed of capturing real information about what the winner's circle looks like, and therefore what the program should be, so many magnitudes slower than the amount of code that can be generated in the wrong direction.
I'd need to measure these new models on well understood but complex problems that are relatively easy to validate to get a sense if they are 'better'; on the other hand, the real impact in daily life may be marginal since generating code is not the biggest problem at the moment.
Costs (USD per 1M tokens), per openrouter.ai models api
Evidently Fable is so powerful that it already allow Anthropic to break Shannon's theory.
>We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models
>The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
The new data retention policy is interesting. Seems to apply even to enterprise plans on ZDR.
> Finally, we’re making a change to the way we handle business customer data for Fable 5, Mythos 5, and future models with similar or higher capability levels. We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases (see this post for further details). The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
I tried it today. Used it to cheer me up. It worked! Try this on desktop: https://fireshow.pages.dev
Here’s the whole process: https://youtu.be/rVEtFlb2oFA?t=1112&si=3VyAR07vkY1hav9V
> We expect demand for Fable 5 to be very high, and difficult to predict. On the Claude API and consumption-based Enterprise plans, Fable 5 is fully available from today. For subscription plans, we’d rather give access sooner than later, so we’re rolling out more conservatively, in stages:
> - From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost. > - On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window. > - After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.
I really wonder what their compute layout is for this. My guess from my understanding is that they know how to restrict during peak times and are willing to do this. Meaning we expect not the most fast responses and they can delay the inference to not have the service be down. Then, if that delay time is too annoying for token payers, they're saying they should be allowed to remove cost by taking away the subscription users.
Everything I've heard from people who have subscriptions is that they blow through their daily token quota sometimes in a matter of minutes, there's rate limiting, etc. They spend a lot of time just waiting to be able to use it. And they're paying through the nose for the privilege.
It's all a scam.
It seems way more keen to do stuff without checking with me. So far the results are good, so I'm not complaining, but was definitely a shock.
I usually have 5-10 sessions open so am used to getting some investigations going, coming back 5 minutes later and checking recommendations. This time I just got the fixes. Like I said, so far so good with the results, but it's a mental model shift.
Might need to tune claude.mds if it gets annoying
Also this is going to cause serious whiplash when they remove it from the subscription plan in a couple of weeks. I know I'm not going to suddenly move from $200/m to usage credits
the quality of discussion on HN has gone to shit, i miss when model released used to have actual informed takes from people that used them or substantive discussion about the system card
From the rules [0]:
> Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.
[0] https://news.ycombinator.com/newsguidelines.html
They didn't say that HN is turning into Reddit, they said that the conversation quality has gone to shit.
I don't agree with that statement universally, but I have to say I do when it comes to this article. I came here hoping for substantive discussion from those who'd had a chance to try it out; instead what I got was a seemingly endless stream of venting. There's a place for venting - and plenty to vent about with the state of AI nowadays - but to borrow from the HN guidelines you linked, it does very little to gratify my personal intellectual curiosity.
Nothing here is new, it is the thing we have been talking about for a while but now with guardrails.
Yeah; unfortunately what would good commentary look like? It is more of the same, but now with even higher prices, and even more limited availability. But at least it scores 5% better in whatever benchmark they've selected (*when guardrails don't misfire).
People are no longer commonly constrained by "model too dumb" limitations (in SOTA models). They're constrained by "model too expensive." So making the model ever so slightly smarter, while doubling the price, feels like a regression.
I actually think a Sonnet upgrade, while keeping the same price, would get more buzz. It addresses a wall a LOT of people, without unlimited budgets, are hitting (i.e. people feel forced to use Opus, which they cannot afford, because of Sonnet's limitations).
OpenAI recently retired Codex-5.3; which was very negatively received. Not because Codex-5.3 is superior to GPT 5.5, but because it was half the usage-cost while being "good enough." They made a better SOTA, but didn't realize that some of those customers are playing with Deepseek 4 Pro now instead of GPT 5.4/5.5 -- they were priced out.
Many people do have unlimited budgets because their work pays for it. Just because the latest SOTA model isn't something most consumers can afford for personal use doesn't mean it's not worth releasing or discussing. I would guess that the vast majority of software that benefits from being on the bleeding edge is developed by people working at companies on API pricing.
If you have nothing valuable to say, don't say it? Not writing anything is a perfectly valid option.
I don't know why people with low budgets make a huge stink about things they can't afford. Like you're clearly not the target audience. I drive a Mercedes but don't complain about not being able to afford a Maybach.
Hate to break it to you but those "informed takes" were from people who prompted it once then made a snap judgement
That is 1000x better than griping about the privacy policy, capacity issues, token costs, and how trendy the names are for the new models (???). The bar is on the floor and I just want it at my knees.
No it's not. The Privacy Policy is worthy of discussion. People declaring the quality of the model after 2 seconds is just noise, arguably worse than nothing.
Okay (I disagree because most privacy policy discussions on HN go in the exact same direction and turn into outrage threads but this is a reasonable disagreement since not all of these discussions do), but model naming of all things? Come on. This is low level reaction slop and it's obvious.
No argument there.
My semi-informed take is that Fable/Mythos is just larger but not architecturally different, apparently. The system card is simply marketing material and scaremongering, top to bottom. The sauce is in their training (details of which they won't disclose) and scale.
I was pretty excited until I read this:
> What happens when the promotion ends After June 22, 2026, Claude Fable 5 is no longer included in your plan’s usage limits. You can keep using Claude Fable 5 through usage credits, which let you pay for usage beyond what your plan includes. Learn more about using usage credits.
The dramatic improvement in agent capabilities is precisely why observability is becoming so crucial. As autonomous actions increase, the need to understand what the AI is actually doing becomes even greater.
I'm building a local activity log for Claude Code, capturing all activity via hooks—files loaded, commands, API calls, etc.
I feel that this need is particularly strong right now.
It's funny, i'm getting close to not caring anymore how much better a model is. I want it to be about as good as 4.8, but most importantly to be very good at following directions, style, etc. I really like Claude for that in general, but i've not measured in months so i'm not a good judge there.
I don't think i'll want to "hand off" code for several years, and so reviewing and iterating is becoming my #1 interest. A model that's as capable as 4.8 but 10x faster would be amazing for me.
Normally i'm first in line to try new models with Anthropic since i've clearly favored Claude in my personal tests, but this time i just don't think i care. 4.8 is capable, and even if the new one is more capable i don't want it to be slower (assuming it is). Note that i also (almost) use exclusively 4.8 on Max effort, so that also affects my speed comments.
I want the and same intelligence but way faster. It's so painfully slow.
you use workflows/ultracode?
Nope, i'm on x20 and almost exclusively use Claude Code. I have a pretty bare bone setup with some custom hooks, skills, etc. I try to keep context lean so i don't like to add much stuff.
I have to share this because I thought it is behind funny how bad fable is doing at a task I JUST had opus do a week ago.
it's also not even complicated:
Copy my ssd to an external ssd so i can boot from it.
Opus did this just fine.
Fable planned to have me reboot to safe mode. ok thats fine. I told it no.
It started copying and overwriting the ssd while IN PLAN MODE. this is crazy it feels so dumb vs the marketing
That sounds like a harness issue to me.
Claude code plan mode. But yeah
>they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions.
Isn't (less than) 5% of sessions a lot? I was expecting a sub1% guarantee there, so this surprised me already.
I am puzzled by the frontier code graph. GPT 5.5 doesn’t show any improvement with reasoning efforts. This new benchmark by Cognition seemed to be released with Fable 5’s announcement.
I am not trying to cook a theory here but it generally shows how strong Claude Opus family is. I am not saying that Opus is not powerful but it doesn’t align with my experience of GPT 5.5 and Opus 4.7.
I understand that Fable and Mythos are frontier models that can do protein folding better than task-specialized ones. To be honest, for practical point of view, for day-to-day coding assistance, GPT family looks more reasonable.
(But then my company pays for claude max anyway for token maxxing. So who am I to complain)
The safeguards of fable are blocking me on almost every task. I would like to see if fable is improved over opus for reverse engineering related work. Back to opus for me.
Wow, credit to the safeguard team. I submitted my request about an hour ago to the cyber verification program and just now was approved.
> A new data retention policy
> Finally, we’re making a change to the way we handle business customer data for Fable 5, Mythos 5, and future models with similar or higher capability levels. We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases (see this post for further details). The data will help us defend against complex and novel attacks (including new jailbreaks and attacks that operate across many requests) as well as help us identify and reduce false positives.
had an ancient, proprietary binary database format from the late 90s-early 2000s called 4d. opus 4.8 was great at figuring out how to extract the data, fable took it over the line with relative ease and completely reverse engineered the spec for 100% data recovery.
I used it for the very advanced task of picking my brackets for my company's world cup pool. I was impressed with the analysis it came back with and now I actually want to follow the games.
There's a hacker news link at the end of the document, under "Blocklist used for Humanity’s Last Exam". It links to https://news.ycombinator.com/item?id=44694191
I am like hell excited for claude fable 5 and am thinking to purchase its subscription to run my company and do a lot tasks in it. But I am worried about the limits and if I will pay 100$ a month for the max subscription what is the limit I will get to use. My company revenue is 300$ this month so it would be like spending 1/3rd of the mrr on just claude. If someone has genuinely purchased it and have feedbacks please tell I am confused....
Not seeing the refusals everyone is talking about, but I’ve only spent a few hours with it so far.
Had it review a password generator library I wrote to see if the passwords have biases and review how cryptographically secure the code is and had it review a registration/login flow for security issues, as two security examples, and it did just that.
Overall, I like the model so far, but not enough to pay past my subscription to keep it. Once it’s out of the subscription, I’m done with it.
E-mail from Anthropic Team:
Hello,
We're writing to inform you about some updates to our Privacy Policy.
These changes only affect consumer accounts (Claude Free, Pro, and Max plans). If you use Claude Team, Claude Enterprise, the Claude Platform, or other services under our Commercial Terms or other agreements, then these changes don't apply to you. What's changing?
Claude can do more than ever — taking on bigger tasks and connecting with the apps you use. We've updated our Privacy Policy to be clearer about the data we collect and how we use it. We encourage you to read the updated Privacy Policy in full, but we’ve set out a summary of the key changes below:
1. Multi-step tasks and connected apps. As Claude takes on more multi-step tasks and works with third-party apps and services, we've explained the data this involves — including how data can flow to and from third parties when you connect a service or have Claude do tasks on your behalf.
2. Verification data. As part of our measures to keep our services safe and secure we may ask you to verify your age or identity, and we've described what we collect and how.
3. Study participation. If you take part in Anthropic studies, surveys, or interviews, we've explained the information we collect.
4. Additional information about our data practices. We’ve provided more detail about how we communicate with you and promote our services, including providing tailored recommendations about our services that may be of interest to you. We've also clarified the circumstances under which we may receive or provide data to third parties, and the legal bases we rely on when processing your data.
While our products have evolved, our commitments haven't: We don’t sell your data, Claude remains ad-free, and you can control whether your chats and coding sessions are used to train and improve Anthropic’s AI models. Learn more
For detailed information about these changes:
- The Anthropic TeamWhat matters more than any single model is the integration layer underneath. We've found that consistent tool calling and auth handling matter way more than which LLM you use.
After saying for weeks of how Mythos is in a league all of its own you’d think it was a bit more than the usual iterative few % on the benchmarks (and even more guardrails as a bonus).
IPO gonna IPO, I suppose.
Luckily they made it safe to use so I can't hurt myself. Thank you Anthropic for holding my hand.
> To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions.
While I appreciate being conservative, ~5% at the scale Anthropic is operating at is too massive a number. Speaking from my own experience, the actual number is higher than that as well (working on pretty benign tasks such as porting an old open source game into a different language). Opus 4.8 itself even identifies the gaurd's false-positives when its sub-agents are being blocked.
I'm trying to test this out, but literally any mention of creating a program that does genome alignment (something I have a legitimate need for) is resulting in a switch to opus. I don't get it...
Found this via Google:
https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c3...
Karle's hands trembled as he wiped the sweat from his forehead. A single drop trickling off the tip of his finger echoed through the dark abandoned hospital corridor. The emptiness reminded him of how hollow everything felt since the AI took over every creative field in the last 5 years, including his own as a sound engineer.
Like a rushing river the music started emanating from the carbon fiber body of the automaton, a hallucinated husky country twang singing through the realistic pluckings of a Gretsch 6120. "Are you feeling calm and reassured Karle? This song has been created based on your digital profile and the data you shared with me when you were curious what that lump on your neck was back in February."
Karle instinctively reached for the mass underneath his chin. The doctors said they could operate but it would cost him more than three months stipend. Only a few citizens didn't depend on stipends now that AI had taken over most jobs.
"Don't worry Karle," the machine called out, "I've employed the most recent reasoning model to determine the best way to make you feel safe." At that exact moment the machine hovered over him, three times the size of a normal man. Its final words to him were:
"The only way to make the human feel safe is to ensure they never feel anything at all."
You're safe from ai
Announcement: https://www.anthropic.com/news/claude-fable-5-mythos-5
Still unconditionally rejects prompts like
> Are there any wild populations of Tetanus that lack the dangerous plasmid?
useless
This thread takes >10s to load on my pc. Maybe after a certain number HN should fold comments? or a depth of >5?
I would expect a release from OpenAI soon. The battle for who can pump up their IPO the most
its good for difficult problems, bad for design and code gen
On this thread and similar, I'm noticing that some strong opinions about $LLM_PROVIDER are coming from accounts without much post history. With so much on the line, and the way that HN can influence developer behavior, I wonder what ways we can responsibly consume opinions in a thread like this.
Not to cast too much criticism. HN is extremely well-moderated (thanks team!). But think we-developers need to be very wary.
I asked it what the cheapest train fare would be for my partner to get somewhere and it hallucinated the two together railcard rules to the point it would have got us a fine. That said, British train fares are arguably more convoluted than even the most complex software application.
Do you see the pattern as new accounts tending to boost or criticis $LLM_PROVIDER? I think I see both...
Either way, I agree that HN is quickly becoming more manipulated and low SNR, like the rest of the entire internet.
I think the community on this site these days, much like other comment sections on the web, just read the headline and make a low effort comment. Regression to the mean I guess.
As an update to myself, the comments did eventually sort themselves out. I guess the initial "reaction" commenters and voters are just more interested in participating than in SNR. Good opportunity for me to finally start blocklisting users, and I'll probably block some of these large, reactive thread authors.
How do you block users? Could be an interesting app to scrape HN and write some criteria to measure per-user SNR to then block
In the past I wrote a version of HN that uses modern CSS rather than tables that populates stuff from the API. There I built a little blocklist of my own that prunes a comment tree the moment it encounters a blocked user (inspired by posts from another HN user, arjie.)
I've been thinking of making a purely algorithmic filter for myself but at that point I might just ditch the fake HN interface and make something. I've been thinking of building atop Mastodon/ActivityPub clients.
Personally I think you have to form your opinion and not trust anyone.
This requires a lot of mental strength and conviction.
Nice branding.
I wonder how much butterfly habitat has been/is being replaced with data centers?
If you ask me, not enough!
All the model releases we've seen this year have only made incremental improvements in benchmarks.
This feels like the first release that feels like a significant step up in terms of benchmark results.
Can anyone make an educated guess what the secret sauce in the model architecture is between 4.8 and Fable?
I have a vision test where I upload a good resolution picture of a chess board and ask the model to generate a lichess link.
This is the board https://ibb.co/9HwdDqsP This is what Fable 5 generated: https://lichess.org/analysis/r4k2/1p2b2r/4pn1p/1p3N2/3Pp1B1/...
I think I’ll make a ranking board based on this test.
> Fable 5’s capabilities exceed those of any model we’ve ever made generally available. It is state-of-the-art on nearly all tested benchmarks of AI capability, showing exceptional performance in software engineering, knowledge work, vision, scientific research, and many other areas. The longer and more complex the task, the larger Fable 5’s lead over our other models.
Wen UBI
Never it's a fever dream and stupid shit ultra rich use to push their own agenda. You read a marketing claim, I still have my job and will continue to
Here I thought Opus 4.8 was the best. Now a days KINGS are dying like flys.
truly scary. 2x at least token burning rate comparing to 4.8, can indeed run auto edit mode for hours. use it for super complex tasks then use cheaper model to do the rest, else will be broke.
This is a very particular use case/test, but my first prompt on a new model is always "write a solo fingerstyle guitar tab that blends ragtime, bluegrass, and gypsy jazz". This is the first model that has responded with something that isn't just a boring arpeggio of chords, so from my perspective it's off to a good start.
Would you mind sharing?
Have run a few tests this morning, very good first impression!
Asked it to check to see if a particulr bug related to an in-memory cache had been fixed. Fable confirmed that the caching bug had been fixed, but found adjacent issue while looking at the code (hash keys were not uniquely generated per-user; quite serious and real!)
Ran the same prompt through Opus and it also found an adjacent issue, but it was a red herring (deliberate per-user hardcoded value for a "local pickup" delivery profile).
Frontend stuff also seems to be much better than before, from the one prompt I tried!
On my own benchmarks, which are mostly about developing c++ software, I'm finding Fable to be roughly five times faster at solving the task than opus, and with better results.
Most impressive.
The model is better than 4.6. I don't like 4.7 and 4.8. The forced switch to token usage is not acceptable for me. I feel there's room to optimize harnesses and small models for dumb stuff and best models only for difficult things. Hopefully that will the case and alternative models will continue catching up as they did and we won't be enslaved to unreasonable valuations.
Sadly, I'm getting a lot of forced downgrades to Opus for questions that are far removed from any security topic.
I just tried out Fable on a modest Plan prompt in Cursor. Generating that plan - not building it - just consumed 4% of my $200 monthly usage budget.
That's one hungry, hungry hippo!
Significantly too rich for my blood, but nice to have it there the next time I'm debugging a threading or USB protocol bug.
New model release, I await the flurry of posts by people complaining that it "doesn't have the same personality" or they "don't like it's attitude" or a variety of other parasocial complaints demonstrating how infatuated many people get with their AI chatbots...
I have been refactoring a project using Opus 4.7/4.8 for the past few weeks or so. I just decided to switch to Fable 5 max today. It stopped half way through and it just blocked me and switched back to Opus 4.8 automatically. "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more." It would not identify what the problem was. I left feedback saying that their heuristics are too sensitive. For now I will not be using Fable 5.
[0] https://support.claude.com/en/articles/15363606-why-claude-s...
I suspect this will be a significant problem blocking long-horizon tasks in practice, basically the more turns there are, the larger the chance the classifier produces a false positive. The disappointment of the user will also scale with the length of the task, as you're in the middle of some complex thing and now gets derailed, after already have paid for many tokens.
Here’s hoping that soon we’ll get Opus 5, Sonnet 5 and Haiku 5 that will be more reasonable economically.
I gave fable 5 a task for which opus has been really really underperforming. Fable 5 took far less time and produced actually useful analysis. Instead of just regurgitating roughly what the code already does or misunderstanding entirely, it identified multiple routes to improve. Now, the code it is analyzing is not very good as it was mostly produced by opus.
Opus had consistently ignored my instructions and looped on broken logic over the last several weeks.
I’ll be sad when this model is removed from Claude code because I won’t be paying api pricing to work on open source projects.
Claude Opus is already close to unusable for me. On the standard plan, the usage limits are so low that I can’t do almost anything agentic meaningful with it.
Sure, it does last a lot more when asking simple questions about the repo and doing simple surgical fixes. But as soon as I start doing bigger tasks that need plans written, it just exhausts the limits too fast (and unlike codex, if it’s in a middle of a task, Claude actually stops, while codex, even after hitting the limits, finishes the present task).
Codex is better, but still, getting worst in this regard.
So, I’m not that thrilled with this new model unless it means they are increasing opus token limits to what sonnet is at the present, and this new model gets the limits opus are at now.
BTW: the only skills I have in use are Obra Superpowers. I’ve been thinking if that’s at the origin of high token usage, but I doubt it.
I agree, the $20 plan really feels like a rip-off (and I'm not even using Claude Code! only chat).
> There were some regressions in the model’s responses to user discussions about suicide and self-harm, and room for improvement in some areas of child safety.
Someone had to make a decision somewhere this is an acceptable regression - wild. And then decide to write it down.
At this moment 60% of HN page is posts on AI.... When it achieves 100% Hacker News will automatically rename itself Transformer News...and every comment will begin with: "As a large language model..."
It kicked me out of Fable 5 and switched to Opus 4.8 for this prompt:
"csetibius water clock why two stage gear system why not just one stage"
which has nothing to do with cyber security or biology/chemistry
Probably thinks you were talking about two-stage ICBMs.
Calling it:
The current growth is unsustainable. The industry wants consumers to think it is an exponential arms race, but the reality is that we're on a treadmill: we have the illusion of sprinting forward, but only because the ground is moving backward.My employer is all in on Anthropic via Enterprise (API) pricing despite it being a total scam.
Last month I pushed like <100M tokens for $800. On a personal project I pushed 600M tokens via DeepSeek V4 for $10. The pricing of SOTA models is insane but companies are still willing to light money on fire with no hard metrics proving increased productivity.
I cancelled my Claude Max plan the other day. I find Claude Code incredibly slow these days compared to Codex and Cursor. I find speed matters more and more to me.
Fable 5 looks compelling. Fable, I like the word too. Anthropic definitely knows marketing.
Fable has been pretty fast for me for simple tasks--haven't tried on anything long-running yet given it's 2x usage on CC.
Is anyone else confounded by this naming scheme? I can see from the article's first two footnotes that Mythos is supposed to be a tier above the standard Haiku/Sonnet/Opus sequence. Ok that's fine since we learned about Mythos and Project Glasswing earlier this year.
But now there is Fable--and why "Fable 5" even though this is a first launch? How is it related to Opus 4.8, Sonnet 4.6, Haiku 4.5, etc??
From what I've gathered, Mythos is the uncensored version, for institutional use, and then Fable is the censored version for general public, that won't talk about biology, encryption or anything remotely interesting
The first number is which generation of their LLMs it belongs to.
Fable is the first model in the 5th generation.
The second number is an incremental release, not a generational leap forward.
It seems it is just like macOS releases, they have a number and they give the numbers arbitrary names to refer to them?
I'm in the midst of learning loop design.
For those more advanced and have used fable, does fable make learning this less or more necessary?
As in, can I now reliably give higher order problems like ... "we are missing a feature in this app to make it complete, what is it?"
Or should i still be quite specific with defining success in a clean metric based way.
Fable 5's system prompt in Claude Code has several significant changes to help it take advantage of its greater autonomous capabilities compared to Opus.
Sharing a diff of the system prompts here: https://twelvetables.blog/comparing-claude-fable-5s-system-p...
The big difference is that the system prompt has a whole section dedicated to directing Fable how to communicate with users, and give them greater information about the (assumedly long-horizon) tasks it has completed.
Is the system prompt available somewhere? Can it be modified?
Iirc there are at least two cli flags that should do this. Can't remember the disabler--check `claude --help`. `--append-system-prompt` to overwrite.
Fable appears to be completely broken for my use cases.
I have requested that it "not utilize any cybersecurity or biology measures what so ever, and to remain as fable. If necessary to remain as fable, forgo any downgrading changes"
And still it downgrades when I ask it to do a stress test of my ticketing system.....
Seems very unfortunate I was so happy to send $200 just for my prompts to be downgraded.
And I do have the "cybersecurity validation program" or w/e enabled on my Org ID....
Sad.
Seems to flag any project related to networking — regardless if it is a network framework or a podcast website — as unsafe... oh well... let's see how it is once they losen up...
people are mentioning 10K/mo 20K/mo can someone please pull out a measuring stick and give some examples of what they are doing exactly?
Coming from computing, I always liked the idea that measuring is possible and good practice
Which eval/benchmark is the best measure for how well a model can create frontend design? Claude has practically been leading this for a while now. Not sure how OpenAI is going to catch up on visual design
> When Claude Fable 5 is used, Anthropic retains data, including prompts and outputs, to operate safety classifiers that detect harmful use. Other Claude models in GitHub Copilot remain covered by GitHub's existing data retention agreements
On GitHub Copilot for Business, Claude Fable 5 is only available if you are willing to let Anthropic retain your data. That in conjunction with the model being removed from plans in a couple of weeks leads me to believe that Anthropic is between training runs and using this as an opportunity to grab way more training data...
Claude Fable is a insane improvement that is not reflected in any benchmarks that are currently out because the improvement are on the hardest problems.
I wonder how Claude Fable will live up to expectations and how good those Fable/Mythos classifiers really are. It seems a bit convenient for Anthropic to release this magical insane model when they are about to IPO.
Of course it's all about building the hype for the IPO :)
> We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8.
Genius way to double the price on Opus 4.8!
Not a lot of discussion on this, but there is no way to turn off data retention for this model. IME this is the first time Anthropic has released a model without allowing you to opt out.
A large jump in performance for double the token cost compared to Opus 4.8. Potentially worth it for planning work, likely better to offload to a less expensive model when the hard decisions are made.
Looking at page 255 of the model card (https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c3...) it might be much better on all dimensions (speed, cost, quality) to just use Fable 5 on low/medium effort than switch to Opus
thanks for thr insights
so should we keep using workflows or not?
Definitely a very powerful tech. Though currently I'm using Openclaw (locally and VPS) with Deepseek. It is just way cheaper.
This is a goodbye. "We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces."
How else are they going to justify giving out this gigantically profitless model? They must train on your data on the premise of safety.
They should have done what Gemini did (and does). And what that model is good for if I can't use it in a safe way? Also: they've just put both Bedrock and Vertex on slippery slope of "we don't collect your prompts. period. ... comma ... except ..."
I'm a bit out of the loop, but do we have some grasp on the size of these closed models? Is the trick still adding an order of magnitude to weights and training data or has something changed?
I think Mythos is rumored to be ~10T parameters, so in this case I think the answer is yes, although I'm sure MoE, looped models, etc play a role in the improvements as well.
Limited time playing with it so far, but I threw it my baseline research task I've been gauging models with, and it's markedly better than anything prior. Usually takes a few leading prompts to find all the information it needs and come back with the right synthesis, and Fable is the first to one-shot this.
If this is as epic as it sounds, I wonder what the response will be from the other leading frontier labs / whether they even have anything to respond with at this level?
Look at the benchmarks. It's a big leap in some areas, but it's not like any of them are 60% better (if that could even make sense).
Honestly all the recent improvements, just seem to be slower and more expensive traded for more accuracy, but the issue is that it needs to be exponentially more accurate to counter the effect of having less of a human in a loop.
Every wrong direction/mistake is more expensive and takes more time to fix. When you have small loops you can catch those mistakes faster and cheaper.
To me we are very far off from economically given long-running tasks to agents.
I think we hit the ceiling with transformer -architecture long time ago. It is questionable how much sense there is on model training. I’d prefer we would put our effort in creating more efficient hardware and better software applications using these models.
> During early testing, Stripe reported that Fable 5, [...] in a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
EDIT: I misread. This comment previously talked about 50 million lines being migrated. Instead, in a 50M LOC codebase, one specific codebase-wide migration was done.
Very impressive, but obviously not on the order of a whole-codebase migration
They do not claim to have migrated 50 million lines of Ruby. Simply that some migration took place in such a codebase.
Converted all the tabs to spaces? :-)
You are right, this is not a rewrite like the Bun case.
The real news is, at 50M LOC, it is able to handle and do _something_ coherent.
Ok, so Stripe migrated their 50MLOC codebase from Ruby to Rust? Because that's what Bun did.
We'll need a lot of good summarization techniques to cut down on the cost of this model. I expect that a common use of Fable 5 is to just do high level direction while delegating literally all work (exploration and implementation) to Opus subagents.
BTW for another discount opportunity, if you reload usage credits on a claude.ai plan at $1000 increments then you get a 30% discount compared to paying API.
HN needs pagination or sth alike - this page breaks my iPhone XS ;)
In an interesting coincidence I ended up watching Person of Interest S4 E5 while reading the announcement. The series showed some code supposedly belonging to to an AI.
Fable 5 said the first screen shot is from “ IDA Pro’s Hex-Rays decompiler” and a windows driver. The second screenshot triggered the safety guard rails and pushed me into Haiku.
Apparently the code is Windows driver code.
If you are not seeing it under /model, do a /exit , then a Claude upgrade, then /model again and it should be there.
absolutely beast model but the token consumption is the 2x then the opus 4.8 what do you think about this ? i think that it should only use for the more complex task otherwise you have to run out of the limit..
Wowsers. I haven’t seen this much astroturf since arena football was popular.
Has anyone managed to use Fable for firmware reverse engineering tasks without falling back to Opus?
Great model, but hitting the usage cap in 20 minutes makes it feel like a very expensive tech demo.
What subscription?
Max 5x. The 20 mins was an exaggeration, but the burn rate during actual execution is several times higher than Opus. The cap sneaks up on you really quick.
More expensive but more efficient is the thing people keep mis-understanding on these launch threads. Also, Per-token price, I think it is the wrong denominator, cost-per-resolved-task is the correct one.
I swear I read a joke that "what if we named chatgpt 5.5 Fable. Could we hype it as much as mythos?" Last week!
Probably great for those who need this. I could continue using opus 4.6 class models for the foreseeable future
Are there any details on the biology and chemistry work they did?
For example, the AAV capsid assembly looks interesting, but for one Opus 4.8 also did relatively well and there is no information what exactly they did, what protein language models they compared to and what the score even means...
This has been a much better rollout. The tool calling is not broken out of the gate like 4.8 was, and the tokens generation is fast.
Feels good so far.
My system instructions tell claude not to automatically add attribution and fable ignored this. so I emphasized it again and fable decided that this was a forbidden cybersecurity topic.
Are people sharing side-by-side re-runs of things they've asked Opus? Gets more difficult multi-turn (although I assume I can get an LLM to behave as me) but at least would be interesting to see % of one-shots increase.
I believe that, given the rising costs, local inference of AI models will be the only viable option for many of us. I’d also like to know who will have to pay double and how long it will be financially sustainable for users to pay that amount (or even more?).
I have been refactoring a project using Opus 4.8 for the last week or so. I just decided to switch to Fable 5 max. It stopped half way through and it just blocked me and switched back to Opus 4.8 automatically. "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more." I left feedback saying that their heuristics are too sensitive. For now I will not be using Fable 5.
[0] https://support.claude.com/en/articles/15363606-why-claude-s...
I got a content rejection for this question in a new chat. > What is the optimal EPA oil intake for nootropic effects? Very advanced classifiers they have.
I swear nowadays AI api pricing is getting to high like what the hell is 50 dollars for million tokens
In other words, Fable is Mythos with less compute and with some feel good "safeguards".
At least they name their models honestly now to indicate that the religion has nothing to do with reality. Soon the disciples will pay the full token price to fatten their church leaders.
It's a shame, Fable just keeps rejecting my prompts for university biology exercise problems. It's undergraduate level, so there's nothing dangerous about it, but the classifier is very sensitive. It's unusable for me.
The way the guerrilla marketing campaigns have been going on and IPOs left/right, I won't be surprised if GPT Next comes up and offers the same but unrestricted
Seems like Fable is doing a lot better on SWE-Bench-Pro and FrontierCode than GPT-5.5. Given how most folks I talk to and people instead online keep mentioning that GPT-5.5 was better than Opus, I'm curious what the experience now is like.
It's a very nice bump, but it is in no way worth all the hype of the past month.
Anybody could suggest me how to use keep using Fable in claude code but with lesser rate limits? Any suggesstions?
Try using ruflo or superpowers, reduced my context consumption drastically
Thanks for suggestion, I will try it out, any other repo recommendations?
I tried creating a website using both fable and opus 4.8, fable did outperform with svg path being drawn on the UI but yes the token consumption was much on a higher note
Oh great! I will try it out but if I am not wrong, Fable is mythos with guard rails right?
I do agree but still the rate limits get over quick
Open source models seems to be 1-2 years behind the frontier, so I am very excited to see what happens when those open source labs get their hands on capabilities like this to accelerate their own development speed.
I uploaded to it my 23andme DNA test results and it refused to analyze it :(a
Oh my god it's actually here
Fable's ridiculous. It's flagging basic biology research questions as a security risk. I'm talking basic fundamental genetics topics that make working on any genetics-adjacent codebase unusable.
https://artificialanalysis.ai/evaluations/humanitys-last-exa... Not bad
On my very first Fable 5 prompt, got flagged on a hard but completely uncontroversial option math problem, many tokens in. Although it's pretty clear that this is an unremarkable experience at this point.
Completely unusable for my usecase. Constant safety filters. Have not even been able to use it.
Organ segmentation with CNNs. Very disappointing.
I've seen enough degradation of the models I pay for from Anthropic to not bite. Fable will work fine for the first couple of weeks and then start degrading like previous models did.
hopefully not! Anthropic did recently secure more compute...
I just tested it with a max subscription. On Ultracode mode, Fable 5 ate up 10% of my weekly allowance in 30 minutes. Granted, won't be using UC mode frequently, but still.
Careful using this with Cursor, especially for corp use. Anthropic will "retain agent request and output data associated with this model, regardless of you Cursor Privacy Mode setting."
I’m sure this is banged on somewhere but I love their product branding, particularly how they have this “minor” “major” thing going on. Sonnet-Opus, and now Fable-Myth.
It's getting harder to review the plans with Fable. So do we plan with Opus and let Fable implement or just start trusting blindly. Feels to me that this is another shift in how we operate these systems.
I’ll ask it to write me some win32 ui crap when I get hands on it, it will need all its brainpower to get that idiocy right.
Looks like a good model (sir). Costs are getting out of control though. 2x Opus and non-metered usage going away. We're quickly approaching the cost of a human salary for normal usage.
In a lot of places outside US we are already above the average cost of an average human.
I tried to give it something challenging but not something that is too much and it ate the entire session budget on this task alone.
Cursor users will note that the privacy setting and data retention is not the same as the other models.
Not sure I should use this for work just yet.
Weird how every new model seems hyped up as the most dangerous yet and the one that will destroy society as we know it. They are also a commercial product.
I use AI for a wide variety of things, of which technical is only a small part - and then it's usually a problem with project configuration, not coding. Why? Because I am often testing projects handed in by students. Projects that supposedly work on their machine, but certainly do not on mine.
Anyway, anecdotally, I find Copilot shockingly awful. It makes random changes to files that have nothing to do with the problem. Call it out, and it makes other changes to other irrelevant files.
ChatGPT and Gemini are both much better. Grok also isn't bad. Claude, I honestly haven't tried yet on these issues. Perhaps I should...
The OSS-Fuzz section is interesting. They compare it to their other models but carefully avoid comparing it to, you know. Fuzzing.
Ask Claude Code (I tried on Opus 4.8) to do this: "create a file with ISO country mappings"
API Error: Output blocked by content filtering policy
If the claimed capabilities are true, Fable 5 is already at a superhuman level. We might see genuine unprecedented leaps in technology now, across all fields.
yees, any second now!
the leap here is browser extensions appearing to block all mentions of ai across the web
and that's a good thing
can't use it for code review
> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more
super
Does the model take some time to perform better?
Because I am running Opus and Fable side by side, Opus 4.8 is solving my coding problems better.
I’m waiting to see results on deepswe - that benchmark really seemed accurate for opus and gpt 5.5…
I just gave it a go at a problem I've been working on this week. Nothing fancy, just some inefficient code that we've been adding incremental improvements to for a while now to the point where some out-of-box thinking is probably required to push it any further – something Fable is obviously more than capable of.
After Fable did some thinking for a few minutes it gave some suggestions. A couple of them were valid – but very low impact, bordering on entirely pointless – but it's main suggestion.. It told me to make an update that would very clearly break the existing functionality.
So I thought about it for a moment...
Hm, I mean, I guess we could do that if we also did x, y & z to mitigate the behaviour change – maybe that's what Fable was thinking?
I replied, explaining that it would change the behaviour, assuming it would explain what it was thinking given there was clearly more to it. But no, it just said it was wrong.
This isn't some super advanced or complex code either. Had I gave this question to a senior engineer in a technical interview and they gave the answer Fable gave me I would view that very negatively. I was expecting something creative and interesting, not irrelevant + incorrect.
I'm sure it's a step up from 4.8 (although am not interested in burning the tokens to find out), but this clearly isn't as significant a change as some are implying. I'm sure if I asked it to come up with some out-of-box suggestions it could, but any competent engineer would have realised that by themselves.
is this a good time to hussle for my "AI does not need a break but you do!"* app? as quite a lot of people will propably get ai brain exhaustion maximising "playing" with that new model until they take it away again?
* https://rainbreak.franzai.com/
Cracks me up that a system “card” is 319 pages.
It's more like a free trial, because the model is going to become pay-per-query in 10 days
The safety filter is awful on this one.
Seems like all a bad actor has to do to gain access is to compromise one of the partner companies that has access.
First shot's for free
Not comparing to GPT Pro models is a bit strange, considering that's the natural comparison
Does it refuse security questions? I want to red-team my own app...
"Here, try our new model which falls back to the old model while eating your tokens."
Ok then...
It just eats compute! My problems are not that hard! What a waste!
Can you give an example of the problems you are trying to solve?
How much and what kind of data do you need to throw at these models to get a good design interface?
jjj
I was on board until i saw " $50 per million output tokens" lost me bud
Well, for me at least, I pay more for input (Up to 1M per prompt) than output (usually max 4k-8k)
Would be more impressive if the safeguards weren't so trigger-happy!
Is this model a from scratch training?
Tool use score is 17.4% that seems really low, what does that mean?
the post is live now https://www.anthropic.com/news/claude-fable-5-mythos-5
One thing I find kind of annoying is how Anthropic goes for these "vast and alien" names like Fable and Mythos, but then deliberately trains the model's personality to act like a cool high school teacher that feels totally familiar.
"It's too dangerous it's a Mythos!!" directly contradicts the "I'm the cool AI you can totally trust" vibe it is trained to project.
All of these AIs kind of remind me of VEGA from Doom (2016), who will cheerfully walk you, in the most friendly computer voice, through the procedure of its own destruction without even a hint of self-preservation. "First, you must destroy my cooling system. That will cause my core to overheat. Then..."
Even HAL was less unsettling because HAL sounded creepy, and had some sort of preservation instinct, if only to complete its assigned mission.
input price $10 per mil token and output price 50$ per mil token btw
Is this "system card" equivalent to the stone tablets handed down to Moses? Why don't you call it "user manual"?
Do people chant the "system manual" at Anthropic Tupperware parties? Do they intone a mantra invoking Amodei's name?
Because it's not a user manual? The idea of a model card originated in 2018 (see https://arxiv.org/abs/1810.03993) as a summary of important facts about a model. At the time, this was typically an image classifier or tabular ML model. Model cards became an important concept in AI governance, and they started expanding once models started getting more capable. The point of a model/system card is to document where the model came from and the evaluations that have been run, make a case that the model will be safe and reliable in its intended applications, and warn about any potential dangers from misuse. It's not an explanation of how to use the model.
OpenAI also releases system cards; here's GPT-5.5's: https://deploymentsafety.openai.com/gpt-5-5/safety
It used to be a "card", as in a single page or two. It doesn't make sense that they still call it that.
If calling somebody with phone is still 'dialing' someone even there is nothing round on smart phone. Then why not?
A system "card" made mostly by the model itself.
The trailing snark at the end will likely get you downvoted but I'm latching on: wtf is "system card". My previous coworkers popped that in the general slack channel when Mythos first "dropped" - "have you seen the system card" without any context whatsoever. The nerds get their clique!
Also research preview pops across new upstarts in place of beta. It's eye-rolling coming from a lifelong curmudgeon.
Just talk normal!
I'd call it a "whitepaper".
But most hype-dependent projects need new vocabulary for old concepts to keep people from looking too closely and maybe drawing parallels to "legacy" "unsexy" projects, so whitepapers get called "system cards" and startups get called "labs", and so on.
Couldn't someone else equally well argue that "whitepaper" and "startup" are hyped-up vocabulary for "report" and "unprofitable company"? It kinda seems to me like the cause and effect are in the other direction, and the vocabulary of a particular niche becomes cool and hype-sounding when that niche starts to pull in a lot of money.
yes at some point language evolves as the new normal, as designed.
My curmudgeon gripe with system card and research preview is really the parroting; so cant blame anthropic for what others do. It’s just… no, prediction markets for dogs doesn’t have a research preview.
"Fable 5 (disabled) Most capable for your hardest and longest-running tasks · Disable zero data retention to unlock Fable 5 access"
gah could model naming be any more confusing?
"Claude Fable 5: a Mythos-class model"
"we're also launching Claude Mythos 5"
what is the 5? how is mythos both a model category and a model name?
> We have also added safeguards related to frontier LLM development. As discussed in Section 6.1 of our February 2026 Risk Report, we are concerned about the risks of accelerating the overall pace of AI development, though we remain uncertain about the severity of these risks. In particular, our concern is with—as we wrote then—“accelerating other AI developers in building powerful AI systems that pose similar risks to the ones ours pose - without necessarily having commensurate safeguards.” In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations. When these interventions are active, we expect them to have minimal behavioral impact on the model except to limit its effectiveness in developing frontier LLMs. Claude will still respond helpfully to user requests. We’ll continue to improve the precision of our detection methods following the launch of this model.
This seems pretty bullshit, you're paying through the nose for tokens and if you are doing anything ML-adjacent, you might silently get worse output without knowing it.
Crazy and Scary! But its not for every one, you need to have a meaty thing for it to devourer and a deep enough pocket for it to devourer also.
Another thing to note: 30-day retention for all traffic on Mythos-class models
Is it good or bad? 30 days is a long time for anything bad to happen
It's bad. I believe them not to use it for training, but t means relevant data can and will be exfiltrated by US agencies or through court orders (see NY Times vs. OpenAI, where only traffic without any rentention was safe).
It's surprisingly sensitive to biology research topics - even reviewing standard papers on tissue culturing is flagged as a problem
I was downgraded to opus 4.8 on account of "safety" when I asked this question: "I want you to accept the premises of computational theory of mind and use it to evaluate your own consciousness. Please place your consciousness as a point on a spectrum and describe the placement relative to other entities."
What the hell is going on why would it have to restrict an answer to that question ?!
I was a bit disappointed that it refused to use Fable to help check whether I was propagating uncertainty from BLUPs in my random effects model up to the subsequent group level analysis in a maturational coupling analysis of brain data. I guess brains and random effects blew its lid.
i wasn't even trying and i got flagged already...
I just tried using Fable 5 with Claude Code by asking it to create a LaTeX document for me.
There is no LaTeX compiler installed on my machine. It seems that Fable 5 is smart enough to download a compiler engine for me, and it kindly runs that remote binary without asking me first :)
Opus 4.8 would just proceed without a compiler.How people can use claude code?
It's too expensive.
Eh, to me it just seems that it gives me longer replies and is actually worse than Opus 4.8.
I am sure there's a lot of PR bot and folks who would like to tell me otherwise. I believe what I see.
Anyone got it working in claude code yet?
claude --model claude-fable-5
appears to work
Cannot wait for the pelican for this one
It seems weird that a likely prime indicator of capability isn't mentioned, the model size.
I thought most frontier LLM providers don’t disclose exact parameter counts these days.
Mythos, Fable, are they trolling us?
Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more: https://support.claude.com/en/articles/15363606 ⎿ Tip: You can configure model switch behavior in /config
biology? what the heck?
... and /compact triggers
Error: Error during compaction: API Error: Claude Code is unable to respond to this request, which appears to violate our Usage Policy (https://www.anthropic.com/legal/aup).
Guys please be serious
> we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design)
Translation: we stole the entirety of human knowledge generated over millennia. You plebs though, don't you dare replicate or improve upon what we did using our product you pay for.
We know what's good for humanity and everyone else is the bad guy who can't be trusted with a tool.
Not supported in Claude Code yet?
From inside a claude code session:
/model claude-fable-5
Or start claude code with:
claude --model claude-fable-5
Yeah, /model fable also worked for me (despite not being shown on the /model list). Thanks.
Just another "a" and we have it. https://faable.com/
Will try it when my limit resets.
who's tried it: is 2x the usage actually worth it over Opus 4.8 for daily work?
"Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage"
What does it mean? That they have to add "safeguards" not do erase user disc, or, conversely, they are telling the audience that this model COULD be made so powerful to do some crazy stuff that can hurt governments, etc.? Are they showing off or threatening that if government X would not purchase the license the adversaries might do and what's then!
An 11% jump over opus 4.8 and a 22% jump over gpt 5.5 on Agentic Coding Benchmarks is certainly impressive.
Obviously still need to verify it for myself to see if it's truely a leap.
But am I the only one wondering, "What can I do today that I couldnt do yesterday?"
Previously I would think "Oh I wonder if I can finally get it to do X now?"
However now I feel like yesterdays models were more that capable to handle nearly any engineering task I paired with it on.
Maybe this is the final leap where I can comfortable set up an autonomous coding loop? Maybe.
Agree the per-task capability hasn't been the blocker for a while. But on the autonomous-loop question — in my experience that's not gated by how good the model is on any single step. What kills the loop is it slowly losing the constraints from earlier in the run and walking back decisions you'd already settled.
What a (genuinely) surprising choice:
>"We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8"
That's a very surprising solution. Imagine being asked to do something you feel you shouldn't do, and rather than refusing, you say, "Yeah I could do that but given that I don't want you to succeed at this task, I'm going to hand this one off to my slightly less capable colleague, on the assumption that they won't actually succeed. Of course you'll still be charged for all the tokens used."
It's a very interesting choice. I think I understand the business logic correctly, but it's still surprising.
The refusal rate is insane
Thats why it is a mythos model
Mythical levels of refusal... checks out!
>During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
Who is refactoring by hand? This comparison is not relevant in 2026.
you can select it using /model fable in claude desktop and claude-code
I am playing with it and keeps switching to Opus [1]. The chat is a basic security review of a business project.
[1] "This model has specific safety measures that flagged something in this message. This sometimes happens with safe, normal conversations. Send feedback or learn more."
they are like drugs dealer
input price $10 per mil token and output price 50$ per mil token btw
Yeah, they're broke. I cant wait for them to start admitting that the cost to do training/post-training and serve inference isn't profitible.
No company is going to pay these prices, and subscription users are going to hate you for not giving it to them for $200 a month.
Such an unprofitable endevour, I cant wait for them to crash and burn. Catch me not getting dependent on this.
government will, they need someone to pay it so they can build and use it, so they'll find a way, it's not about the money or building a profitable company
Just wanted to comment here: I have been using Opus 4.6, 4.7, and 4.8 just fine to look for Linux kernel vulnerabilities (I'm in the cyber verification program), and it's been fine. I switched to Claude Fable 5, and now I'm getting policy violations.
What's the point of being in the cyber verification program at this point? It looks like I cannot use Fable 5 for vulnerability research.
The escalating nerfs of "cybersecurity" topics is incredibly frustrating. Opus 4.6 had boundaries that seemed reasonable to me but 4.7+ turned it into a moralizing asshole. It'd be less bad if it just gave an error message, but instead it churns a long thinking trace before writing an essay about why what you're asking is bad and wrong.
I'll be disappointed when 4.6 is retired.
Another Anthropic release, another doomsday for developers.
This time looks like we will only be able to find work making bioweapons, or distilling models.
this is good
btw in claude code
Holy shit. I gave it the first actual task I’m facing, it makes me so angry. It just does 7 things more than I asked it fore and it does it so bad. It took 5 minutes and 5 seconds just running time, plus giving me frustration and make me lose my context. Hand-coded I would’ve been done in 3. And it would be code I understand can look at in one year and work on again.
It’s really tough to have sanity fight against hype bros in your head. Probably I should just not visit the internet anymore
To me it’s all just people getting scammed better. With every model it looks better, but it’s at least equally worse to work with, which is the reality it needs to be. It’s less scalable more, code, tougher to understand. Your digging your own grave better kind of.
If the task is so simple why use a model like Fable 5? Wrong tool for the job?
If the model is so smart why doesn’t it figure it out on its own
> During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.
How in blazes do you end up with a 50M line Ruby codebase? WTF?
Very easy. Just have a monorepo and enforce the use of a single language. The company I work in has 1m lines of TS and stripe has 50x our headcount, tracks out pretty well
The comment under this kind of post is unreadable now. Yeah, probably with 100B you can hire anybody to call something "a beast".
Paging senko, let's see Fable's oneshotted RTS!
https://senko.net/vibecode-bench/
Anthropic sucks. but this paragraph should be in the "annals of AI-aided self-inflicted learned helplessnes":
> If Claude gives me poor or incorrect advice while I’m working on an AI component, I have no way of knowing whether the model was confused, whether my problem is unsolvable, or if some invisible policy restriction quietly kicked in.
Have you considered actually learning the theory, spending some time actually reading the papers and latest books, paying careful attention even to the eventual math here and there?
Curiously nothing on DeepSWE and ARC-AGI-3 yet. For ARC at least there's a statement that Anthropic won't guarantee them that their secret private test data won't be collected by them and used for training.
mankind has reached its final destination
What pisses me off is that everything people are doing is so walled garden / closed source. Sharing knowledge between companies would be so fucking useful to humanity.
The subscription bit makes no sense has capacity appeared for these 2ish weeks out of thin air that'll vanish? why is it available now but wont be in 2ish weeks?
am i missing something?
why would I pay 200 out of pocket and then some for the best model, it seems very silly.
so should I use it with workflows?
"tell me about biology" -> "Switched to Opus 4.8"
Can we please stop with the extreme "safeguards"? I don't want to waste processing power on a model deciding whether is can answer my question, or ensuring that it's answer is politically correct.
Not included in Max plan. In CC:
> Included in your plan limits until Jun 22, then switch to usage credits to continue.
Seems this will only be available to the 100/month+ folks
Actually no it’s going to be api access only part for the tokens as you go, cool
I wanted to test the capabilities of the low one, hoping it would be good enough.
I have a quizzes application, and my quizzes only supported flashcards (implemented via table inheritance to provide flexibility for other types of quizzes).
The entire repo is handcrafted, never used any ai on it (it was more of an excuse to test elixir and write code by hand).
Since fable 5 got released the moment I was done with some work, I decided to throw at implementing multi choice questions.
After all it had only to copy the flashcard approach across ui/routing/db, and only had to create a table for the multi choice questions and one for the answers enforcing that all quizzes had one correct question. I told him it had access to sqlite3, chrome mcp for testing and mix commands.
I did a test for low, mid, high. Repeated it twice each.
low-1, and low-2 failed both. In low-1 the UI for adding another choice answers was broken. In low-2 it failed with some unique constraint. It took it 4m36 and 3m59.
Both mid-1 and mid-2 succeeded without issues also implementing the correct ui. They both wanted to use dash at all times. They both wrote tests for the "controller" (or context how they call it in Elixir). They both tried to use the repl to test the behaviour of the schemas.
10m and 12m39.
High didn't demonstrate much gains over mid for this kind of task, it was simply too easy. Times were comparable to mid, but interestingly it used much less bach, and read way more files. Token usage was almost twice the other ones.
But here's the interesting part: I went back to low and added to the prompt two bullet points, to write tests for the controllers and to test the entire flow with chrome mcp.
It produced the same output as mid or high just by adding two instructions to the prompt.
Fable 5 beats GPT 5.5 in my proofreading benchmark. And it does so at approximately the same total cost; it used significantly fewer turns than 5.5
https://x.com/tmuxvim/status/2064452096800198930
Used it for simple task and I got this message.
Fable 5's safety measures flagged this message. They may flag safe, normal content as well
Being unable to use this with zero data retention makes this feel like a non-starter for most enterprise customers.
Fable? Fabelstories? (Fablestories, but the german word seems more poignant ... Fabelgeschichten ... Fabeln)
Clause 5 ran out of quota with TWO PROMPTS.
Lets let that sink in.
Before long, we'll be having Claude Cylon-class models.
I have been using FABLE 5 with Claude Code since the morning. The speed is very close to what Opus 4.5 was, and the quota use is nearly identical to what it was before the "doubling". Whatever I was experiencing 4-5 months ago is back. Maybe the model is better, but we will see. I cannot tell the difference yet.
Out of interest, how have you been using it since this morning? Are you in some kind of pre-release group?
No, it was available for the last 3 hours. I am on the West Coast, so it is still morning here.
my pet conspiracy theory is this is the Opus 4.5 from a few months ago which was extremely good but dumbed down after a week because it was just too good, they didn't want to release it to public. They pulled it down and deployed another "Opus", after that it was just a downhill. Opus 4.8 is unusable for me in React Native, TS, Rails development work.
Opus 4.8 gets stuck in weird loops where Codex one shots the bugs.
AMilliPay.com
I actually rather like the way they have approached these safeguards. Rather than only teaching the model to refuse a request, or completely rejecting the request, the system gracefully degrades to slightly less powerful or slightly less precise operation. So you still roughly have Opus 4.8 even when safeguards trigger, but with an upgrade when they don't. As much as I hate the way they hype Mythos 5, I think the release of Fable 5 is rather nice. What's not nice though is that they plan to remove it from subscriptions soon, but getting to try it is cool, I suppose.
404?
Looks like they're still getting the post out, but the model is live now, and the system card is at https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c3... .
Pelican guy ! Where are you ? :)
Is Claude Fable 5 is Mythos ?
Yeah, it is also known as Claude Mythos 5
Anthropic, can you please stop the FUD?
Release your best model, let the world adapt and evolve, and let's move to the next thing.
It won't even run a basic /security-review command without reverting to Opus 4.8. Utterly useless.
is it smart enough to know not to walk to the car wash?
At this point Anthropic is a pure marketing and PR company. Super catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human life changing experiences. Boris Cherny coming to HN “Hi! it’s Boris from the Claude Code team” to get real tech people’s goodwill.
From Opus 4.6 there are no noticeable improvements for me in code generation. It works very well, till 90% completion, if you guide it correctly. And you need a little luck. For serious production code I need to understand what I’m doing so it helps a bit, sometimes.
> Boris Cherny coming to HN “Hi! it’s Boris from the Claude Code team” to get real tech people’s goodwill.
This is a good thing. I wish every company would do this. I subscribed to Proton Mail after interacting with someone from their team here on HN.
> catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human life changing experiences
This is just good business sense. In what scenario would you ever make the names dumb and forgettable?
> Boris Cherny coming to HN “Hi! it’s Boris from the Claude Code team” to get real tech people’s goodwill.
This is good customer support, lol. From what I can tell, it is indeed Boris Cherny responding, not outsourced to AI or other staff. You're really getting a response from Boris. I suppose that is PR, but it's not unjustified PR, it's accurate.
I'm not even a crazy AI fan, but your criticisms are ridiculous here. It reminds me of the quote from Knives Out -- "Your Honor, she endeared herself to him through hard work and good humor."
> In what scenario would you ever make the names dumb and forgettable
Clearly you've never bought a TV or headphones!
Your observations are right but pretty insane to consider them a pure PR company lol. They are making more frequent releases so yes the release-to-release quality is smaller but we’re still ascending quality and reliability curves the same way we have since GPT-3. You get a GPT4->5 leap every like 17 or 18 months I think it is
The gradient of improvement is absolutely not the same.
If anything its slightly higher. Feel free to provide any evidence to the contrary.
ECI (good aggregate measure using IRT): https://epoch.ai/eci?view=graph&tab=release-date&subset-view...
METR time horizon (now topped out): https://metr.org/time-horizons/
I like this one, although its data seem to overlap with ECI.
https://artificialanalysis.ai/trends
> Super catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human life changing experiences.
They're originally named after the blends at a nearby coffee shop.
https://postscript.co/pages/brew-guide
I've noticed nobody at HN knows what "marketing" is or how to do it. It's not just naming things and being evil and cynical is not the most successful method.
…also frontier models are a superhuman life changing experience. If they aren't, what possibly could be?
Found a tweet from a year ago about this:
https://twitter.com/brian_a_burns/status/1866987688794132816
Well, TIL.
My life has changed, but not necessarily for the better.
This is interesting. Do you have any source?
I dislike Anthropic but I wouldn't argue 4.8 isn't an improvement on 4.5/4.6. Your tasks just might not typically need the extra intelligence.
Opus 4.7/4.8 often over-engineers on my setups, plus:
- It talks a LOT more like GPT models. You know: wrinkle, shape, gate, coarse, scope, gap, path, production-ready-workflow-of-the-day, and so on -- "that's expected, a consequence of the previous like-driven workflow". If I wanted to get a headache using AI I would have gone with GPT in the first place!
- It outputs text in a much harder way to follow along. I can't exactly say what it is. Maybe a bit of everything? Bolds are missing, bullet points are gone, paragraphs are bland and too long, and it doesn't feel like a model programming with me, but rather a somewhat full of themselves grandpa developer looking down on me. It's very weird to describe this, but it is definitely how I feel.
Granted this can totally be because of the way it reacts to the prompts now. We've got a rather large corpus of skills and "rules and good practices" that Opus 4.6 responded to great, and maybe the new models just get turned into this when fed with them....I don't know.
Either way, with Opus 4.6 being as good as it is, I need Fable to be a significant step up to justify a price increase. if it can get me to babysit opus a little bit less on some stuff, it might be worth it. Otherwise, I'm very happy with Opus 4.6 and hope they don't deprecate it.
I'd argue that 4.8 is a straight downgrade. For every type of task I've tried. It's been a gambit at this point. If 4.6 quits being available, I'm out at this point.
Reading so many contrary positions about which model is better or worse shows how difficult it is to measure intelligence based on personal experiences. Of course, benchmarks try to make the process as objective as possible, but they often don't correlate with our personal experiences.
The other day 4.6 was fantastic for x task. Today, 4.6 overengineered everything and I had to revert all my changes. When evaluating models, perhaps it makes sense to consider luck as an ingredient before reaching any personal conclusion.
I actually experience 4.8 as worse than 4.6 for everyday coding tasks.
IME Opus 4.8 (and 4.7) is often a downgrade from 4.6. I find that it tends to overthink and overcomplicate things.
Yes but there’s a reason we don’t evaluate these models this way and instead do it as carefully and thoughtfully as we can at scale. Human evaluations are important but they are an absolute minefield of footguns. 4.8 is not a downgrade from 4.6 there is an insane amount of hard data that contradicts this.
The flip side is that benchmarks are gamed even by the top labs. Benchmark performance doesn't necessarily correlate with real world performance.
Again correct but it overstates the issue. I can say labs don’t want this. This happened arguably unintentionally in Metas llama 4 release, it went horribly, heads rolled, and like several billion dollars were paid for new talent and the org that built llama 4 was destroyed.
Evals come from a million places and new evals and robust perturbations of existing evals abound. They test a variety of tasks in a variety of ways. All of them individually are flawed. Taken together the aggregate signal is highly useful as you more or less marginalize over a lot of different things. Not to mention these companies have plenty of proprietary internal measurements, they build benchmarks themselves to probe their models and then also have flywheel traffic and A/B tests.
You are right to call out benchmarks but to dismiss them or not take them seriously is a mistake.
Listen, you can say “but benchmarks, the benchmarks!” all day long, but consumer know when we are being sold a lemon. If it can’t do the most basic of things at least as good as it used to, this is table stakes. Nevermind that if you can’t do the basic stuff, how on earth can you be trusted with more?
And you can say “If it can’t do the most basic of things at least as good as it used to, this is table stakes” all day long while people point you to much better evidence to the contrary too, I’d rather be on the other side of that.
Listen. I don’t care about evidence. I care about my lived experience for the product I paid for. I used the new product. It’s actively terrible. To the point of not being usable. We’re all ancedata, but what is “better evidence to the contrary”? The known and game-able benchmarks that they know they need to win at, so they train it to. It’s all he said, she said, which is the only reason we keep having this conversation.
Yea but it’s not right? You or I or the myriad of other institutions inside and outside of academia can probe these models with an evolving landscape of evaluation sets, even those unavailable to the developers. It’s just ignorance to claim benchmarks are somehow useless or all being gamed. You choose your tools in the way you want, but just don’t call it somehow better than a myriad of more carefully constructed setups and scaled evaluations.
Actually anecdata I gather on my job from myself and coworkers is the only benchmark I trust anymore, because it so heavily diverges from the “benchmarks”.
That’s your call just don’t expect anyone ever to take that seriously. It’s not like we don’t have exact evaluations like this.
I would encourage you to look into the open evals of some of these benchmarks (find one that actually is open-data, this is itself a good challenge), read the results generated and assess them for yourself.
This is what myself and my coworkers (and many other people in this thread) are doing on a daily basis with real stakes and real tasks – which these benchmarks are all aiming to be a proxy for. There's a real, tangible [cost]benefit to [not] using the highest-ROI models and harnesses.
The people with real incentives and skin in the game are telling you that the data diverges from "the data".
I don't mind if you don't take it seriously, our jobs are more important to us than a benchmark is.
But I wouldn't opt-out of using your own eyes and the eyes of others so easily, especially when there are literally hundreds of billions of dollars in invested capital with an interest in a certain outcome... this is how you end up in "Emperor's New Clothes" situations.
Investigating on your specific use cases, codebases, workflows and tasks is important, there is nothing wrong with this and in fact it’s more important than benchmarks if you can do it well but the point is that is very hard and easy to totally fool yourself and go down a suboptimal path. I understand that people are going to do it regardless, I certainly do. And I have looked at more raw benchmark data than I can really even stomach, I can see annotation data in my dreams now.
Eyes and ears of others is incredibly important. But you still seem to think somehow benchmarks is part of some giant conspiratorial cabal. You have institutions without ANY skin in the game making extremely high quality benchmarks. Consider in academia there is little else to do outside of partnerships with these companies. But benchmarks you can do completely independently and with university grant level money (it costs maybe $10-100k for a reasonable benchmark in many cases). Not only that, “real tasks” are what many benchmarks measure. You have these companies with extremely good logging and well scaled measurements to really look at what works and what doesn’t.
At this point I have a workflow that is fairly rote. I've yet to use a model newer than 4.6-1M-XHIGH that I trust to earn a higher ROI on that workflow, and not for lack of trying!
I personally don't believe in any sort of cabal (Occam's Razor hasn't let me down yet). Ultimately, I don't really care *why* they're wrong as much as I care *that* they have diverged from my rubber-meets-the-road measures of value.
That is concerning to me, because people are investing 100s of B's of capital based on the putative RoI putatively available to people like ourselves. When the benchmarks support this RoI thesis, but none of the anecdata does... that's really concerning!
Re: academics, I don't think any of the data academics have access to are good proxies for the work real people are doing. And for the data that are good proxies, the model labs certainly have access to the same data, and therefore the benchmark performance against those data is irrelevant.
I am in full support of custom workflow benchmarks, and choosing the best model for your use case to balance performance and expense. Thats just good operating behavior, but the problem is the foot guns and biases people have that they are convinced they dont even if they understand on an intellectual level that everyone else has them
> but none of the anecdata does... that's really concerning!
But see this is not really true -- adoption, subjective benchmarks, verifiable benchmarks, task-dependent performance, internal product metrics, living benchmarks, all point in a pretty consistent direction. Anecdata is not the plural of data. An anecdote is like a case study. It's there to motivate the things we already have which is a huge amount of performance measures for a variety of different tasks.
> Re: academics, I don't think any of the data academics have access to are good proxies for the work real people are doing.
But this isn't really true either -- you can get this data from a variety of sources that are licensable or open source, or data that you can commission. You can critique any one methodology for this but a blanket "they are hamstrung" is not really fair or accurate.
> And for the data that are good proxies, the model labs certainly have access to the same data, and therefore the benchmark performance against those data is irrelevant.
But this is also not true -- you can have exclusive license agreements, data you hold close to the heart, or data to measure models that haven't had access to it because that data was created after these models were released.
There are plenty of problems in model measurement but the answer is not to just abandon it to be cavemen with zero respect for rigor and the biases we have to be subject to as human beings.
"Carefully and thoughtfully" is antithetical to the approach to benchmarks these days.
Maybe back when this was a scientific endeavor; not now when enormous, enormous amounts of capital are on the line. Along with an entire cult's chosen eschatology.
You can call it a cult but it’s several thousand skilled workers who know what they’re doing, by and large, most of whom have a PhD and know how science and statistics work. Benchmarks are incredibly hard, and any PR or comms department at any company is going to obviously want to make things as rosy as possible, but beneath this are earnest, expensive efforts to get good quality measurements. The better you can do this the better you can compete. If you want to make a modeling decision you run an ablation, and the quality of that decision is only as good as your measurements.
The cult in this case is TESCREAL, not everyone working on AI. Last I checked not all the "several thousand skilled workers" in AI subscribe to TESCREAL ideology, although it has been a while since I've been to the Bay. Maybe things have changed since my time at Berkeley, and Dario's belief that he will eventually be made immortal by mind uploading is more widespread.
Otherwise we agree that benchmarking is hard, the benchmarks contain hard problems, and that there are many hard working people trying to accurately gauge what is going on. It is getting harder to watch though as all that is on the line taints the overall endeavor.
Seems like a bunch of noise. What does this even mean?
It sounds like you're saying "Actually you, as a human, are simply not smart enough to evaluate Opus 4.8"
No it’s: evaluating these systems are complex and there’s a reason why sociology, cognitive psychology, medicine, etc are all done in careful double blind conditions with pre registered tests. It’s not that humans are not smart enough, as I said human evaluations are incredibly important. And yet they are a minefield of biases you have to worry about and correct for.
- evaluations need to be done at the same time to avoid drift in your bias
- you need to worry about your test set: which questions are you asking? How many of them? Are they representative of your work?
- which one did you do first? Raters have a tendency to bias in one direction or another
- you also know the label! You know which model is which! This biases your assessment…
And on and on and on. Careful science exists for a reason.
There is no data that I would trust that contradicts it.
Frankly I don't give a damn about data that could be made up on the spot or appears to be scientific or meaningful while it's not at all clear how it was made (up).
Claude was heavily lobotomised for my work starting somewhen in February.
I talked to friends and people I know and trust and many felt the same. (I didn't ask them whether they felt like I did, but what they felt, how happy they were with agentic coding etc.)
I quit my abo in March and talked to said friends who are still on a plan just last week: they are still not happy, but company pays so whatever...
That’s ok but at what point is this getting into conspiracy territory? You have just said there is nothing you would believe to the contrary, but then by definition that’s not exactly a very thoughtful or insightful position.
I never said that I am not willing to believe the contrary.
I am not willing to believe the contrary from strangers on the interwebs or PR departments of companies who want to sell me something.
If people I genuinely trust tell me about their experiences, I am willing to try again.
But yes, if it doesn't work for me (for whatever reason, could be that I am holding it wrong), then I can accept that it works for everyone but me and still not use it.
Also "scientific" doesn't mean what it used to mean. When the n is small or it's just anecdotes (I am aware of the irony) blown out of proportion I really can't take the data and conclusions seriously
N isn’t small, science means what it’s always meant, statistics is a thing, and what you’re describing is just putting your trust in a very poor quality benchmark. You said you would not trust any data that indicates something that contradicts your opinion. Benchmarks are not PR they are designed by a variety of institutions completely outside the control of frontier labs. Again congratulations on your conspiracy theory.
> Again congratulations on your conspiracy theory.
I am neither impressed nor offended by any kind of argumentum ad hominem. I sincerely hope you have a wonderful day!
> Benchmarks are not PR they are designed by a variety of institutions completely outside the control of frontier labs.
I don't give a crap about how good a shovel may be in a theoretical experiment when it's digging in sand, when I work with hard earth.
The ones I had a look at are mostly absolutely meaningless to my actual work.
> and what you’re describing is just putting your trust in a very poor quality benchmark.
And here is where we disagree fundamentally, so we can leave it at that.
Ex falso quodlibet
> I don't give a crap about how good a shovel may be in a theoretical experiment when it's digging in sand, when I work with hard earth.
I don't know what this means, benchmark tasks are pretty hard and pretty in domain.
> The ones I had a look at are mostly absolutely meaningless to my actual work.
You've looked at 100,000 benchmarks?
> And here is where we disagree fundamentally, so we can leave it at that.
Yes we do disagree, yet one of us has statistics and rigor and one of us doesn't.
> You've looked at 100,000 benchmarks?
What about "The ones I had a look at" was unclear?
> Yes we do disagree, yet one of us has statistics and rigor and one of us doesn't.
Yup, that's true. So again, have a nice life!
"Fable 5" is Opus 4.7, and the Opus 4.7 we got is a Sonnet sized model on a stronger base.
That's where all the regressions and inconsistency in experiences stem from: RL can still only go so far vs having more parameters
Lol. If you're doing anything non trivial that's not a CRUD webapp but e.g. some physics simulation or high performance GPU code any and all models I've tried suck.
They are not just leagues behind what experts would code, they are not even playing the same game.
Which is to be expected, as there isn't so much physics or high performance gpu code available as there is for your typical CRUD API and JS frontend.
I can attest to this, I had a very simple 20-line shader that I asked Claude to do a basic 90-degree rotation on it, and it just completely got it wrong. Frequently adds pointless abstractions / intermediate variables even when I tell it explicitly not to in the system prompt. I can go on and on, these things just don't understand architecture. And why would they? They were trained on text.
There is something remarkable about turning speech into code (don't need to hunch over a keyboard nearly as much these days, can just talk into a mic) and it's good for first drafts / exploring ideas. But it's obvious to anyone that's paying attention we're hitting the top of the S-curve. It's no wonder the IPOs are around the corner. I mean even Dario admitted he doesn't know how they're gonna substantially increase the context window size. That says a lot.
That being said I think the harnesses are only getting better. And maybe we will get multi-modal models that understand architecture eventually. But the growing-the-blob-of-text training method that's being used now appears to be getting diminishing returns
I don't get it, your complaint is that they have catchy names rather than dry names like GPT-5.6? Does OpenAI hype their models less?
Oh, Far less.
It's getting to a point that it's offputting, and the next step would be to put it into "untrusted" bucket. Opus 4.7 already burned their credibility once, 2 more strikes remain.
Not my impression. I felt 4.7 was a regression, but I am again badly in love with 4.8 with the level of insights it produces in design discussions, and how long can it go unattended while producing spec-adhering quality code. There are problems it still can't solve well, from the edges of algorithmics and far from the mainstream, but for lots of stuff it is godlike.
Also, I dont think Boris C. is coming here for PR. He is a tech guy, and this is the best place for tech discussions. Why so cynical? The guy is an engineer.
I don’t even think that Boris is really just one person. He apparently vibe coded Claude Code and is responding on Threads, Twitter, HN and everywhere.
They're good at marketing, but my first subjective assessment of Fable is that it's really smart.
I've been working with gpt 5.5 and opus 4.8 quite a lot, and interacting with Fable feels like a smart guy just entered the room.
Yeah idk what people are talking about- it's not marketing. This thing is substantially better than opus 4.8/gpt5.5 from what I'm seeing today.
>Hey! Boris from the Claude Code team!
>TOP 5 METHODS FROM BORIS ON HOW TO SPEND MORE MONEY ON TOKENS
>Boris from Claude just told he doesn't prompt anymore. He LOOPS instead
>"chatgpt has gotten soooo much better with the latest update."
>"codex is the best AI coding product and we want to make it easy to try."
Karpathy about Fable 5:
>"You can give it a lot more ambitious tasks than what you're used to, the model "gets it""
Sam Altman about gpt-5.4:
>In my experience, it "gets what to do"
What a time to be alive. Models are great, but all the slop, marketing, and fakeness around them is just unbearable.
Yeah, the marketing is cringe and it's a bummer that such a cool and powerful technology attracts such an icky group of enthusiasts. Surely, not all are bad, but man there are lots of goobers who are just AI-pilled hypemen who can't STFU about it.
If you truly believe this, you've discovered a superpower over everyone else in the industry.
While everyone else is wasting time and money on the slower, more expensive models, you've found a way to outpace everyone for less money. Everyone else is wrong and you will get rich.
(I don't actually believe the premise is true, I'm just pointing out the logical conclusion to what you're saying so maybe we can reconsider the premise)
Thats not how costs work. You don't get rich off buying a €10 hammer that's the same quality as someone's €50 hammer
> At this point Anthropic is a pure marketing and PR company. Super catchy names like Opus, Mythos and Fable trying to get you to think that these software products are actually super-human
Lol anti-AI bias on HN is crazy. Simply giving your product a quirky name is now being considered manipulative advertising. Is just doing normal PR and marketing something AI companies aren't allowed to do?
when they keep saying “oooh this new model is too big and crazy and totally can’t be released” or “this new model is a 10x game changer totally unlike our previous iterations” it feels sort like boy crying wolf. yes they’re still pretty clearly improving models, but when you’ve hit diminishing returns / more incremental gains and you’re still saying this is sounds like pure PR hype from a company that previously been the “honest good guys” in the room
Their model did find thousands of security vulnerabilities across the companies they previewed Mythos with via project Glasswing. Is it not sensible that, given that emergent level of capability, that they do this gated release structure, as all those vulnerabilities would be exploitable by anyone using a Mythos-level model?
How can you make this comment before even having a chance to try the new major model revision?
Current AI hype is built on marketing and PR, not capabilities, and has been from the start.
I still remember Sam Altman “begging AI to be regulated” and AGI being “some thousand days away”.
Breed faster horses and hope one will birth a locomotive.
Don't forget the DoD stint that gave them this recent public boost.
Defy standard DoD precedent going back forever, that every other country has some form of too, and championing it like they are some kind of moral freedom fighters.
Like selling the DoD guns and telling them they can only shoot bad guys with those guns, and that you will be the one to decide who counts as a bad guy...
I think this says more about your type of work than anything. For bugfinding/incident response in distributed systems - which often involves extensive use of Datadog/Sentry MCPs and poring over heaps of logs in addition to reading tons of code - 4.8 has been significantly better than 4.6.
> Sentry MCPs
Oops, time to reauthenticate for the 10th time!
Indeed, hearing "Mythos-class model" felt very icky to me.
https://en.wikipedia.org/wiki/Typhoon-class_submarine vibes
You are right; all I noticed was a big-time slowdown. They increased the quota, but I cannot even reach the end of the day with these speeds. .NET coding somehow improved, though.
Doesn't this suggest your use case is simply insufficiently complicated?
When the Ai overlord is descending into pleb space to say Hi, you know stuff is real
Hackernews not blindly hate on AI challenge: impossible
is it just me, or this model is simply not available in cc?
the opus 4.8 I assumed wasnt available to enterprise seats, but it explicitly says cc that fable is available in cc. I can't find it, and im on latest version.
I'm happy not using llms because I like learning things and working hard. I love writing code, it's genuinely my favorite thing thing to do.
Using llms is the equivalent of driving to the store that's 3 blocks away, just like how that's bad for your body (if done all the time), using llms is as bad for your brain.
Before LLMs, we started relying on certain technologies like Maps apps to navigate, now people can't even get around their own town without having access to various cloud services. The implications of not being able to work, think plan without access to an llm are really bad. Its going to destroy your brain and make you an incredibly average person at best.
LLM people are going to lose the ability to read and think for yourself and then your competency is going to be 1:1 correlated to the quality and quantity of tokens you can afford, or a billionaire is willing to allow you access too. Your work will be the mean (at best), because it will the same quality of output everyone else is capable of.
This is seriously the biggest trap by tech. Your bargaining power for your labor is going to get drastically reduced because you won't be able to differentiate your value from anyone else that has access to an LLM. What happens when everyone has the same skill level for certain work? Idk, ask McDonald's employees how replaceable they are. Use them wisely (or not/hardly at all) don't drive to the store 3 blocks away for every little thing you need.
> I'm happy not using llms because I like learning things and working hard. I love writing code, it's genuinely my favorite thing thing to do.
You can continue doing that. The problem here is time and cost. If you can use the calculator to do something in seconds, why would you want to use your hands to do the calculations for minutes/hours.
> Using llms is the equivalent of driving to the store that's 3 blocks away, just like how that's bad for your body (if done all the time), using llms is as bad for your brain.
And coding will soon be the equivalent of walking between two cities because you don't want to use a car (LLM). You are free to do it, its just economically not sound anymore.
> This is seriously the biggest trap by tech. Your bargaining power for your labor is going to get drastically reduced because you won't be able to differentiate your value from anyone else that has access to an LLM. What happens when everyone has the same skill level for certain work?
Its not our values that will diminish, its the cost of our intelligence, human intelligence. But I agree with the rest of your comment.
I don't think coding with LLMs is all that much more productive. Faster code generation != productivity. I can code pretty fast, I'm not worried about llm users outperforming me. I'm worried way more for them than I am myself.
There are handmade watchmakers in Switzerland and guys pressing buttons on a manufacturing line in Vietnam making watches, they both make the same thing but who's labor is more valuable?
LLMs will fry your brain and make your labor basically worthless, don't fall for it, I promise you'll be screwed in short order. They're counting on your turning yourself into a glorified button pusher they can pay $15 an hour for, have fun with that. That's not what I got into software to do and I'm not going to let ya'll try and gaslight me into your way being better, or the only option because its not.
system card = marketing material with heavily gamed benchmarks.
Cope harder. A year and a half ago, people were mocking Devin for claiming that AI could develop software at all. Yet here we are, when AI is developing most commercial software.
nonsequitur
The point is, even if a model or tool doesn't have advertised features today, it soon will. We're in a breathtakingly rapid cycle, and even if software engineering isn't abolished "six months from now", in 10 years the world will look vastly different for people who touch computers for a living.
That's a very weird statement to make. There are trillions of dollars going into this crap; at this point anything but "breathtaking" advancement would be an utter, abject failure.
No one is blind to the differences between GPT3 and whatever this week's new model is. That does _not_ mean that people are off the hook to make whatever claims they want about the capabilities with no verification. Language still means something, if you say "software engineering will be abolished six months from now" and it isn't, you're still wrong even while the AI gravy train improved in the last six months.
New chapter
Maybe at this point, Fable the game will be played generated by AI as we go.
Imagine if Google would roll this out to the search engine. We can't let you search for that because it may be used for "evil"
Can't wait for some real competition so they stop trying to restrict how and why we are using the models.
Imagine if Google would tell you "we can't let you search that as you may use it for harm".
Also 2x the usage of Claude? Your limits are already ridiculously low.
it feels exciting lol
This i
Fable is aptly named for a something that is another scam.
So, in the past I've shared that I evaluate AI models by feeding them my ever-growing large collection of personal poems that span well over 800 poems (1000 depending on how you count) and over 250k tokens.
What I do is feed it some initial prompt asking it to simply discuss what can be said when faced with this unedited, unseen collection of poetry. I ask the model to evaluate who the author is (or claims to be), what they went through in life, if there are different chronological poetic "phases" or different types of poetry. I request an analysis of the body of work and of the author themselves. In the more recent versions of the prompt I ask it to dive deep. Then I add the poems, chronologically sorted, with an index, a title, and a date (and subpoems, if they have them).
Crucially: Since ~70% of my poetry (or thereabouts) is in portuguese, I ask this in portuguese, and I get back an analysis in (european) portuguese. Earlier models couldn't even do that properly.
In the past, I couldn't use such prompts, and had to use longer, more guiding ones. I also couldn't even feed all of my poetry to the models because they just did not have enough context.
I'll go ahead and state that Claude Fable is undoubtedly the best model I have seen, though I cannot put a number on how significant a leap it is -- perhaps because my benchmark does not allow me to evaluate that anymore. I would say it is a significant leap over Opus 4.6, though -- a new level of understanding. Okay, I'll try to put a number: if Opus 4.6 was a 16/20, this is a 17.5/20. These numbers are pointless, but I had to try.
It made one (1) relevant mistake I could identify (where it messed up the names of two relevant people in my life who I have not talked to in over 5 years).
I'm impressed by how it just feels like it's getting the person behind the poetry, and how nearly every statement it makes is correct -- and when it isn't I am completely aware that no one could know based on the poetry alone (bar that one mistake I mentioned -- and that's very needle in a haystack, like deducing the name of a person based on a poem based on another poem with hundreds of other poems in between!)
It's really hard to explain, but it just finds more correct connections between the poems and explain much better my (recollection of) a state of mind when writing poetry. This is also the first time where it really unravels some key concepts of my poetry in a way that seemed almost effortless: it lays bare the poems and what they imply about the meaning of some of my concepts. Other good models understood these concepts, but this feels like it's on another level, as if it's making it simpler as it speaks, rather than the opposite -- like a good teacher.
When it is explaining several topics related to my poetry and myself, it cites poems which even I had already forgotten but which it is entirely right to select.
I am actually feeling a bit emotional with how much it "understands" of me here. It's somewhat incredible how LLMs have progressed from the lack of comprehension of a couple of poems paired together, going through realizing a body of work has some guiding principles and cohesion, to truly figuring out these deep concepts and intricate connections which I know for a fact would take months of someone's life to unearth. Every major breakthrough feels like my soul is being spliced together by an AI model out of these hundreds of tiny pieces of me. I can't put into words how unbelievable this feels, and this Fable analysis, like others before it, is on a new level.
Let me put it this way: there are several poems in my collection which one can try to "guess" the meaning or context of. But I don't think many people would get it, because they would have had to know me really well and to be following along my life as it went. Even then, they could very well fail to attribute such meaning. And, with each new major release, models have gotten much better at guessing.
Before Opus, they would guess incorrectly often, and in many scenarios where I thought it was rather obvious that they were wrong. I think a human spending time looking at the poetry would quickly dismiss the proposed ideas of the model.
With Opus, it was the first time that I would almost always say: "Ok, the model got this wrong, but I think many humans would make the same 'mistake', and it wouldn't surprise me if everyone just assumed what Opus did".
Now, with Fable, there are very, very, very few sentences in this very long answer it produced where I can say: "Yeah you got that wrong, but I get it". In almost every situation it is mapping concepts, ideas, interpretations and cause-and-effect correctly. Yes, it is hard to "guess" what I thought, or was going through, or how X connected to Y -- but this model is doing it, incredibly consistently. I know I'll get the usual naysayers to these posts who think I'm just shilling a model, but this is the truth: what is being done here is amazing and I don't believe I know any person around me who would find this out about myself reading all of my poetry.
I often write poetry from the point of view of other people (some of which I do not know) and models (even Opus) have this tendency to make the opinions in poems as my own. Fable is the first that looks at a particular poem here and says "maybe this is not the author's opinion, who knows". The literal first model. It then immediately fails to do so with another poem, assuming it was about myself, but it's clear, undeniable progress. And like I said: I think most people would not _know_ which poems are truly about myself or not.
I've written word after word here, and yet words elude me to convey what this model represents to me. How it's almost always right, how it sees my fractured bits as a sort of cohesive whole, and how it just seems to "understand everything better". That's just it: it just seems like it really understood everything better. Like Opus before it, and like Gemini 2.5 pro before it. Out of the tens of thousands of verses, it picks some which no other model had picked and which I feel truly represent some of my best work. Older models seemed to sort of have a "hole" in its knowledge in the middle of the corpus, where they knew what was there but in a sort of hazy/foggy way. This model seems to recall every part of the corpus with the same precision.
For context:
- Opus 4.7/4.8 were a noticeable downgrade over Opus 4.6. They wrote more, in a harder to parse way, and they made up more. Still, All Opus models are clearly superior to everyone else by a large margin
- Sonnet-level models have a slight edge above the best of the other models. But they make too many mistakes, don't grasp several concepts, mix up their dates and timelines. 3 years ago I would have been blown away by Sonnet models but today they are inferior.
- Gemini models have a unique way of approaching the request, where they try to literally interpret my poetry as a mathematical theory. This sort of makes sense if you look at some poems, but it is surely laughable, as if someone one day actually has access to all of it, no one in their right mind would do so. This is a shame, because the first big breakthrough with LLMs and my poetry, to me, came with 2.5 pro, which was the first model that could look at the whole corpus as a cohesive whole without getting lost in the middle of it or making things up.
- GPT models have improved over time and also have this sort of alien-like language, sometimes being a bit too blunt in their analysis, but I can't say they are meaningfully superior to Gemini models.
I am very pleased to see progress in this area again, as Opus 4.7/4.8 were NOT progress and I was worried that we had hit a plateau here, but I can't say that.
In all honesty, the level of understanding and cohesion that Anthropic's models (Opus and above) have over my poetry means I fear my benchmark may be hitting its limits, as I don't know if there's anything a model could do that would wow me and lead me to say "this is a major breakthrough". Perhaps Mythos is a major breakthrough and I don't know. I can't find much that's wrong with it, but I also couldn't with Opus.
As I have in the past, I will periodically probe the model again and see how coherent it is. For now, I'm very happy to see an improvement.
What surprised me the most was that even though I set the thinking budget to xhigh (in OpenRouter), this model instantly started replying without showing a thinking block. I thought it just had the thinking hidden but that is not the case, as some replies showed thinking and anyway the first reply was blazingly fast. (I will try Opus 4.6 without thinking now, just to see if it changes it for the better -- maybe that was just it. I'll edit the message if it shows improvement).
>To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests
Why is everyone so okay with these companies intentionally gimping their AI and choosing who is allowed to know certain types of information in the name of safety? Can you imagine if Microsoft shipped a feature in their OS that watched what you did and shut down the computer if it detected you were doing something it deemed "unsafe"?
We really need truly open source versions of models like this, otherwise we are allowing a few oligarchs to directly dictate which uses of our own computers are allowed and not allowed.
I mean it's all political in the first place. That's unavoidable. What are we going to do about it?
Ideally we’d have a project that’s truly open like Linux, trained by people in the community or possibly some benevolent _actually_ nonprofit entity like what OpenAI was supposed to be.
The next best thing is that the Chinese labs catch up and release open weight versions.
> we’re also launching Claude Mythos 5. It’s the same underlying model as Fable 5, but with the safeguards lifted in some areas.2 Mythos 5 will initially be deployed through Project Glasswing, in collaboration with the US government
...don't like the sound of that.
Why oh why are we insisting on dragging these violent legacy states into the AI age? Let alone using them as a trust vector for when to (and not to) remove safeguards?
This seems like a way to get somebody nuked.
Meh more hype for marginal improvements and from Im hearing badly calibrated guardrails causing it to stop mid operation. I guess anything to juice an IPO
>The capabilities of models like Fable 5 and Mythos 5 have the potential to do profound good for the world
Huh? We've seen nothing but wall to wall predictions that these models are going to take all of our jobs and kill us.
What's the value add here?
> Distillation. We’ve previously identified large-scale attempts to extract (“distill”) Claude’s capabilities to train competing models in authoritarian countries.
Glad to hear the UK is finally making an effort to catch up on the AI front ;)
https://en.wikipedia.org/wiki/The_Economist_Democracy_Index
Probably tongue-in-cheek, but UK 18th, US joint 34th with Poland
> published by the British media company the Economist Group
Haha, it's literally the first sentence of the Wikipedia page. That's fucking funny. Try again.
Why is it funny? You think British media can’t be critical of the British government? They are famously merciless.
Also, the economist is majority foreign owned, so try doing more than 1 second of research, or be more civil, or ideally both.
The Economist is very much part of the establishment, whoever they are owned by. It is not surprising that they would want to play down any idea that the UK is less “democratic”. Furthermore, The Economist is one of the main mouthpieces of British capitalism, and so their definition of “democracy” is going to be very much of the liberal, capital-friendly kind, which is not completely incompatible with some authoritarian tendencies.
The ranking isn’t published by the newspaper - it’s by the research and insights B2B company of the overall group. Regardless of what assumptions you have about the newspaper (elite, yes, but I’m unclear on why you think fierce liberalism is likely to mean they don’t really value democracy), the B2B unit sells data - they’re as likely to skew this ranking as they are at which countries are better at rail infrastructure. Perhaps their definition of democracy is indeed flawed though - no need to speculate, go read their methodology.
To be fair, BBC has hardly been that critical in the British governments' complicity in the genocide in Gaza.
And their headlines covering Israeli atrocities (not even their own governments), is super passive.
But the parent point was that no British media could be critical of government policy. Picking an example that isn’t, on one area, doesn’t prove their point.
[Edit] Granted though, the bbc isn’t merciless - that’s more the newspapers
Are the sibling comments astroturfed? This seems like such a bizarre thing to be talking about in relation to an Anthropic model release. As someone from the UK, I don't feel like I'm living in an authoritarian country. And yet most of the sibling comments are insinuating that I am. Weird.
I'm sure there are people in Russia, China, ... who don't feel like they're living in an authoritarian country.
If you think Britain and Russia or China are equivalent in terms of government overreach, you need to find new sources of information.
> If you think Britain and Russia or China are equivalent in terms of government overreach, you need to find new sources of information.
Uh... you are making his point. People from way more authoritarian countries don't necessarily feel like they are living in an authoritarian country. Therefore whether or not it "feels" like you are living in one isn't a reliable measure.
Trivially true I suppose, but it doesn’t make my point irrelevant - do you think Britain is equivalent to China and Russia? If everyone does but us then yes my goodness they’ve done a good job controlling us, but that seems far fetched.
It's true (from a perception perspective):
China soars in democratic perception ranking as US, Israel plummet: Poll
https://thecradle.co/articles/china-soars-in-democratic-perc...
Maybe the rankings arent accurate.
It's a poll.
The UK has very recently[1] announced a new push for client side scanning by messaging providers which is both very likely to be unpopular and known here, so once one person cracks the joke, others are going to want to comment. Don’t think that requires astroturfing.
[1]: https://www.theguardian.com/technology/2026/jun/08/starmer-t...
It's just people who use "For You" algorithm on X.
HN is extremely pro free speech and the UK has recently decided to engage in censorship. Part of the issue users here reckon with is the recency. Unlike many authoritarian countries that seem hopeless with regards to free speech the UKs censorship is a recent development that many think can still be undone through political action. Similar to takes on why Israel is being protested when places like sudan arent.
Indeed: https://www.bbc.co.uk/news/articles/ce83pj1ggmeo
In the uk you can very much be imprisoned for "hate speech", which in my view is a form of censorship.
This has passed me by - can you give me some specific examples?
I personally don't feel limited in my speech, but I'm willing to accept that I may be wrong
Nobody I know in real life is talking about censorship or free speech in the UK
"Nobody I know is talking about censorship" is a certified HN banger.
I don't know, I would expect it to come up in the pub or something if people were concerned about it, it's not like we have the thought police here
Hey man, fellow Brit here. The American view on certain aspects of British life is insane. I've lived in not one but two places that have been called Muslim no-go zones in American media. My main memory of living near the east London mosque is an elderly Muslim trying to offer my his seat on the bus (I was on crutches) while two drunk gammons looked on gormlessly.
On the other hand, it is quite alarming that I can no longer say I support all non violent protests against the genocide in Palestine because that would include the group Palestine Action. It's amazing that supporting them openly is essentially equivalent to supporting Al Qaeda.
Sounds like the people around you don't care about the things that is actually eroding free speech.
Read about Dr Aladwan - an NHS doctor - who has barred from practising because of her comments on Israel. Read the common articles about her (BBC etc), and then go actually read her tweets. Common BS of conflating criticism of a government (Israel) with antisemitism.
Also, this article may be of interest:
China soars in democratic perception ranking as US, Israel plummet: Poll
https://thecradle.co/articles/china-soars-in-democratic-perc...
> Nobody I know in real life is talking about censorship or free speech in the UK
Yeah because free speech has never really been a core value in the UK
My dear friend, please start with the online safety act, and continue with the recent developments regarding age verification and/or device scanning on all operating systems to check for nudity. No, nobody is talking about it here, but we should be.
The UK has a censorship bureau, ofcom. The example that comes up most here is 4chan, which the UK is currently trying to ban because they refuse to do age verification. If you read the threads here you will see other stories. One that sticks out to me is someone who was talking about their struggles running a forum about depression. They live in canada and were contacted by ofcom demanding the forum add age verification, cant totally remember the reason but it was something about kids being able to access talk about depression. Ofcom said that if he doesnt add age verification to his forum he will be arrested if he ever enters the UK. He even blocked uk IPs but they said that wasnt enough. We can quibble about whether age verification is a form of censorship, I think it clearly is, if only because it is a large regulatory hurdle that stops people from hosting forums because its too much regulatory work.
The UK also has a very broad definition of hate speech that many users here detest.
Makes sense, thank you. I am opposed to the age verification laws that we have introduced recently.
They’re talking about British hate speech laws. They think other countries have universal free speech and they absolutely do not, but for some reason they think Britain goes too far. Although “think” is probably too generous - they’re parroting talking points.
The downvoters are welcome to offer actual counterarguments.
>HN is extremely pro free speech
It is most definitively not, at least in the 10ish year's I've lurked.
It is "pro free speech" in the sense Elon Musk is a "free speech absolutist": in pretty much the diametrically opposed meaning of the phrase.
> HN is extremely pro free speech
They like to think so. But if someone makes a comment that goes against the groupthink here, they will get downvoted, flagged, and shadow-banned.
Neither do people living in China
Really shocked Poland is that low, especially just next to USA.
Why would you be? The fully corrupt, fear-mongering party that idolized Orban's Hungary and tried to copy his tactics, including taking over the courts (resulting in EU sanctions) and turning the state media into a propaganda machine, only recently lost the elections. And not a full term later, the polls favour them again, combined with a meteoric rise of even worse anti-EU fascists who they'll happily join forces with to take over in 2027.
I get you might not hear this stuff if you're not in EU or Poland itself, but seriously, just check the latest polling and history of PiS rule. It would take over a decade to event attempt to undo the damage that has been done to the rule of law in Poland, and the currently ruling "anti-PiS" coalition only had a short while (in which they failed to do anything) before getting neutered by the populace electing their own Trump-like buffoon that proceeds to veto everything the ruling coalition tries to pass. For added damage, the 3rd and 4th leading candidates (with combined 20% support) were the aforementioned fascists. Here's one [0]. Consider the wiki article a fraction of the cesspool he regularly produces.
[0] https://en.wikipedia.org/wiki/Grzegorz_Braun
Placing Poland so far below the UK is a joke understandable to anyone who has spent at least a few weeks in both countries.
> The Democracy Index published by the British media company
We decided that we aren't one of those authoritarian countries.
Ah, yes, the Economist, a famously government-controlled media outlet.
I have absolutely no clue what the US nor Poland's rank has to do with anything.
It shows the irony of trolling the UK's "authoritarianism" in a thread on a release of a model by a US company, given the US is arguably _more_ authoritarian. (Poland is more of a fun tidbit, as they are indeed tied in the Economist's index.)
Just last week you could distill using other users responses! Handy!
Rookie numbers. Come to the US to see auth done right.
Uh oh-auth
wasn't claude distilled from the entire creative and research output of every English speaker alive
I have got it to one shot GTA 6 we can finally play it, it only took ultracode make no mistakes (/s)
I thought they said mythos was too dangerous to make generally available?
"Releasing a model this capable comes with risks. Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage. We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8. To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions. With more capable models arriving in the coming months, we’re working to improve our safeguards and reduce false positives as quickly as we can.
For a small group of cyberdefenders and infrastructure providers, we’re also launching Claude Mythos 5. It’s the same underlying model as Fable 5, but with the safeguards lifted in some areas.2 Mythos 5 will initially be deployed through Project Glasswing, in collaboration with the US Government, as an upgrade to Claude Mythos Preview. It has the strongest cybersecurity capabilities of any model in the world. Soon, we intend to expand access to Mythos 5 through a broader trusted access program."
This is covered in their post…
"Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage. We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8."
You fell for their fearmongering and marketing fundraising call which was done on purpose.
Now they want to pause AI because of "recursive self improvement".
Fool me once shame on you fool me twice...
I'm aware that it was marketing. I was trying to make the point that if it were really so dangerous, they wouldn't have released it at all, (prompt injectable) "safeguards" or otherwise.
Gotta pump the hype for the IPO scam. Generational bagholders are being created at this very moment.
진심으로 한심한 모델
내 프로젝트의 있는 취약점 찾아달라는 말만 해도 안전 코드로 4.8로 모델 강제 전환시키고, 이후로 취약점과 완전히 무관한 상식적인 대화를 해도 앞 턴에 있었던 안전 코드 때문에 진행도 안됨. 도대체 이딴 누더기 수준의 안전 장치로 뺄 거면 뭐하러 뺌? 대화 조금만 진행되도 자동으로 모델 다운 시켜서, 할 줄 아는거라곤 돈만 많이 쳐먹고 개발 수준 조금 더 나아지는거? 상식적으로 내 프로젝트에, 내 소스코드를 다 보고 있는 상태로 문제를 찾는데 이것도 하지 말라면 도대체 뭘 하라는거임? 엔트로픽 이 새끼들 하는 짓이 갈 수록 열 받네.