I at least didn't see this. I had a baseline evaluation from before Nov 3 on Opus 5.5 medium, so I re-ran it this morning.
Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.
20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.
Caveats: one run per task today, medium effort only.
"Nerf tracking" is adversarial and exploratory. It's not something Anthropic wants anyone to do, and it's not something that the "nerf tracking" designers are necessarily doing in a foolproof way. Don't assume too much from them either way.
These nerf trackers do not work. I wish people would stop posting them.
They don't detect a quantized model until the quantization gets down to 4 bits or less.
Humans absolutely do detect quantization well before 4 bits...
Try it yourself if you want. Try a small local model, both quantized and not, and I would be very surprised if it isn't immediately obvious which is which.
Its incredibly easy to roll out a compute instance to X% of users, or route traffic between compute instances
so not everyone will have the same experience. If I was saving on costs that would be the easiest way to do it without deploying, without any nefarious intent. The persons in charge of that likely don't even know about how individual users are impacted
It would be nice to know how they select. I max out my $200 subscription every week and I was selected. On Saturday, the Opus on my account that happened to be supervising a running live process, became unusable.
It would be trivial for them to opt-out accounts that are doing benchmarking from a planned regression.
I really really really do not trust companies on the verge of IPOing from goosing their numbers. Even if the lawyers are up their ass, the social pressures are more than enough to compel the behavior. For every good person willing to do the right thing even at the cost of a delayed career progression, there are 20 people willing to do anything not even for a dollar but for an "atta boy!" from the boss.
That's a major reason to use local models. Or open weights models on a trusted cloud provider. Even if they are way less capable, they're more predictable
I used to be skeptical of nerfing, but as of a week or so ago I've noticed a severe degradation of quality for GPT Astra. It struggled with very simple tasks, and it often failed to follow my instructions.
I hear a lot of accusations of nerfing as in, saying that the model itself has been quantized.
I doubt this is the case. I suspect all variation in quality and output can be explained in terms of prompt differences, and changing how much thinking happens at each level, as we see in the post above.
Suppose a company has a limited amount of compute. What do they then do if peak demand exceeds what they can provide? They don't turn off people's access...
And what about A/B testing? Anthropic doesn't even try to hide that they do this. Limited compute and A/B testing alone, would guarantee a nerfed experience some of the time. That is, you don't even need a nefarious reason.
it's easy to suspect the hosting companies trying to do what they can to reduce cost-to-host up until the very threshold of perceptibility, so it's an easy guess to say they're probably doing something that shifts performance up or down between version releases..
but realistically I think you're right, it's prompt quality and the inherent non-determinism elements of LLMs showing up; when both of those things meet you get brain dead outputs.
Everyone likes to shout "slot machines! non-determinism!" until it actually explains something, heh.
YES. To the point that I literally stopped using it, and have moved BACK to Claud.
This is getting ridiculous. The number of times I have switched from one model back to the other is crazy. I do not have any proof, so its quite possible I'm just delusional. AI psychosis? Either way, it FEELS like these models work really well for a while and then fall off. Switching to the latest model on the competitor always seems better, until it too seems to taper off. By then, a new model is usually available at the competitor, so I switch.
Its possible there's no degradation at all and just a PERCEPTION of it. But to me the end user, it really doesn't matter.
People are prone to seeing patterns in noise especially after suggestion. It's not hard to imagine someone concluding the model is nerfd after few bad responses in a row
It could apply to me but I run evals every time someone at work complains about this and it’s never shown any more or less variance than regular usage. I don’t have statistical relevance with my evals but I do have a decent history with them.
People seem to underestimate how high normal variance is with these tools.
i'm not parent, but to answer your snark : there are efforts a person can take to try to reduce things like suggestability in ones' own personality. Same as anything, the brain can be trained, thus it should go without saying that some of us react differently in the face of suggestion -- both for the better and the worse.
idk, it hasn't felt nerfed at all to me, and it's not like i'm just doing trivial CRUD webapps either. opus 5.5 is very successfully porting my phone to postmarketos. claude just got the touchscreen up and working (and made a little program for me to test it, and it's legit).
Benchmarks don't do a good job at capturing the difference in quality of frontier models over smaller models.
Opus 5.5 was so good that if it wasn't nerfed - at least just looking at the benchmarks - the difference in cost here does not seem to justify using this for anything besides design and review.
That's like maybe 1% of total token usage for SWE BUT a better design can have enormous benefits, so it's almost always worth using the best model for that.
If this turns out to be accurate, I think that would be a pretty unethical thing for Anthropic. Even if it is just a "we had to allocate compute to this new model release".
It should be fraud to swap out the model. We need a term for this, something like a bait-and-switch.
I believe fraud is a sufficient term. You claim to be selling X but you actually sold them Y. Bait and switch is a selling tactic to avoid fraud where they make you believe product is X and then when you go to buy they tell you X isnt available but they have Y. While also unethical, it is not fraud because they did tell you what it is you are buying before you buy it.
if they just straight up dont sell you something you bought then its fraud.
I think there is a ripe opportunity for expanding the definition of fraud to include changing the "ingredients" of a named product on the market. It is not limited here, we see it with food, and even in the medical field
(I will die on the hill that medical fraud was committed when mRNA therapy was used as a treatment for covid and it was 'sold' to the public as a "vaccine" despite not being the same thing as any vaccine before it. The definition of vaccine was expanded to include it without sifficient public disclosure to warrant informed consent imo. This is not a statement about conspiracy or the efficacy of vaccines or a moral judgement on whether it was for the greater good or not.. it was simply an act of fraud that robbed the public of proper informed consent by making people believe it was X when it was really Y.)
So yes, I think there is a large need for the public to at the very least put a strong handle on the ability to change what a product name means. It is a trivial problem to solve considering it is only a problem due fraud or fraud adjacent activities.
heres the solution: You have your product name, and then any changes to it REQUIRE a new version name. Like how you have Coke and Diet Coke, only formalized such that Coke cannot legally replace the ingredients of regular Coke with the ingredients of Diet coke and still call it Coke. With Software they could at least use version numbers and release modifiers. most of the time its built in already, but it needs to include the full stack. if they change anything that has an interaction with the user it should change the label seen by the user for the product.
yeah, we need to make sure we don't get stuck with these two evil corps. Dario is really the worst. Plays the drama queen but he is very hard in all things dark(i.e. dark patterns).
I'm not surprised at all that they might be doing shady shit, my free request was used up in less than 7 minutes and said pay to continue, no thanks. I've paid for monthly before, never again I don't care how good the model is. I asked about the extreme limited usage in their discord and got banned, not a good look. You don't have a way to measure what you're actually paying for and they can change it at any time.
With early handouts of Qwen4 starting to be reported 5 days ago ... so if they're good, they would have had some hours to evaluate Qwen4 against Claude 5.5 and, then or right after was when Anthropic decided to put out 5.2 ...
So yeah ... benchmaxxing and all that. BUT nevertheless, the published results seem to say that Qwen4-Max appears to match Opus 5.5 on some benchmarks, and Qwen4-27B (probably by going even harder than Qwen3.8-27B on thinking/inference time compute) can beat Sonnet 5.5 on some things.
I guess they're not taking any chances. Keep in mind that Anthropic has declared they have non-public models (it's in their IPO filing). These are referred to by the shorthand names Model 2, Model 3, and Model 4. Essentially 4 is better than 3 is better than 2.
Fable 5.2 is "2.5" so to speak. So Anthropic was holding back, but not anymore. And now they can dial the performance up once more with what they have ready, and probably once more after that.
> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
Most people don't really have intuitions on what it means to push the frontier now (because AI has surpassed everything they are good at, for example, coding). We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
I feel like I'm increasingly reading AI current events is more like learning about historical events. By the time information has entered my head, there is even newer information out there that already makes what my brain is only currently actively processing, obsolete.
All the human engineers will be in harnesses in the future.
Within three years most software developer jobs will consist of taking instruction from the AI systems. The humans won't see it that way, but that's what it will actually be. Realistically the thing that will happen is the humans will be put into harnesses (cubicles) and prompted by the AI (I need you to plug that server in; I need you to give me access to such and such; now do this, then do that). Most tech jobs will consist of AI telling the human what the AI needs the human to do next, de facto the human will be in a harness and will be getting told what to do.
> We're all going to be harness engineers in the future
No, that will be the model's job. We will largely be unemployed. Of course, we can surely depend on the largesse of Altman and Amodei to keep us fed and sheltered.
Isn't this part of the lore behind the Dune universe?
Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released
> We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
I gotta admit, I am jelly as fuck I don't have access to a billion dollar datacenter. Would LOVE to put 1000 fable 5.2 agents to self-coordinate and get them to do something for me. Fuck life is unfair.
I’d be happy even without having access to a billion dollar datacenter, if I could use local models properly, but even that requires some serious hardware that is out of my reach. We live in weird times: I have lots of hope for the future, but meanwhile AI access is pretty iniquitous indeed.
Sub 1% of the planet has access to cutting edge frontier AI.
Sub 1% of the planet has any practical use for the top tiers of AI systems. That will likely always be true.
The gap between the haves and have nots will drastically widen further, and very quickly. The bottom ~95-97% will be left behind in a way that will be impossible to overcome. The age of AI + robotics will just make it that much harder to climb for most people, these are systems that are in aggressive, direct competition with human labor. And in short order the AI + robots will be vastly superior to human labor (it doesn't really matter if we're speaking in terms of 10 years or 30 years).
AI will consolidate the winnings. The gigantic gains by the elite rich related to AI over the past few years, is merely a hint of what's to come. Capital will win as it always does.
There will be some gains for most everybody else, those gains will be comparatively trivial though. A house bot that saves people an hour of labor here and there, AI that automates some trivial life tasks.
AI has no money, it has no wealth, it owns no capital. For now. If you want to avoid the brutal human vs AI/robot competition arena: own capital, own the AI, own the robots.
I’ve had a chance to use fable 5.2. Some observations:
- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when it was Fable 5/5.1 vs Opus 4.8/5. And Sonnet 5.5 sometimes makes me forget about Opus.
- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5. I personally have a hard time telling the difference between Fable 5.2 and Opus 5.5 for ordinary coding tasks.
- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.
- Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image, it’s wild to think we’re at this point.
> Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5.
I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.
For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.
Caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.
Does Fable 5.2 outperform Opus for data science work?
Not just "iterate on this XGBoost model" but actually looking at a problem and designing and carrying out a short-term R&D program including probability modeling, model fitting diagnostics, how to respond to various kinds of results, how to interpret/reason about what it finds, etc. That's really where I saw all the models perform poorly, and where I saw the first big step increase from Opus 5 (pre-5.5) to Fable. But then Opus 5.5 came out and I haven't needed Fable since.
Mind you, I'm doing slightly different work (and I'm very much "in the loop" still) than when I performed that side-by-side test. But it's really amazing what Opus 5.5 can do. I now have a powerful data science AI assistant that not only can write any script I need it to (including some scripts I didn't even realize I needed), and it has an encyclopedic knowledge of everything ever, and it constantly checks me on oversights, it handily derives expressions and clever model reparameterizations that aid, it has great insight into model behavior, and it has an amazing ability come up with ad-hoc experiments and tests that would have taken me 2x as long to even think of (let alone implement by hand).
It's not at the scared-to-lose-my-job phase yet, but it's become a tremendous force multiplier for a smaller data team.
And none of it matters ... because just as soon as you are happy with things "balanced across models" Anthropic will nerf the models you were happy with, and force you to use new and more expensive ones.
> - Why are they stacking Fable xHigh against Carbon Med?
Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level and xhigh is Fable 5.2's best effort level. You can see that on the graph your next point is about.
And it's just that one benchmark that's at medium. "All scores show each model’s highest-available effort unless otherwise noted".
> - How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?
The graph is labeled as being about FrontierCode 2.0. Those appear to be Carbon's scores on the official FrontierCode 2.0 leaderboard: https://cognition.com/blog/frontier-code-2.0
> - Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?
> - Why do they switch to comparing against Argon (rather than carbon) for robodojo?
Because no numbers are available for Carbon for those benchmarks, and the model isn't publicly available so they couldn't run the benchmarks themselves?
Just to follow-up-- artificial Analysis shows no justification for that Carbon effort curve, but it does show Carbon on medium smashing Fable on things like Terminal-Bench and AA-Omniscience v1.1 Non-Hallucination Rate.
But I suppose the reality is rather obvious when you note the funny games they're playing with the graphs, and the things they won't talk about (where's Astra on that CVE chart?)
Setting aside the comments about nerfing, do normal mortals out there have workflows that really require Fable/Mythos and don't just work fine on Opus 5.5?
I've been using Opus 5.5 on xhigh for some decently aggressive software development on a ~125k-line project, and it has been fine for me. I'm not really willing to dive into a "rewrite the whole project, don't make any mistakes" level of refactoring. I'm not looking to try to prove Fermat's Last Theorem myself. Is there anything that I'm truly missing?
Eg. https://tfmbot.com is an implementation of a popular board game. Opus and even sonnet handle that part. Each week i spend my remaining credits on fable to think how to improve the ai. It plays at a very high level (can get into the top 100 on BGA if i let it).
I am in the exact same boat as you. Opus 5.5 on High solves all engineering problems i encounter in my SWE job. If it doesnt do well, its nearly always requirements engineering: I did forget to specify something or there was a misunderstanding. Or taste approach wrt to architectures chosen. None of these are really different if I use a stronger model.
I am also happy with this current state, as I believe it will at some point allow me to transition to open models once they reach the level of opus 5.5. high. But its probably futile to try predict the future in these times.
I only use Fable 5.1 when I'm able to start on a larger feature within my work's codebase. Most tasks can be done by Sonnet 5.5 or Opus 5.5 so I don't fret, especially if I understand the technical aspect of what's being done, e.g. user's dashboard gets a new button to do X thing, error happened b/c we didn't catch it the first time around.
For Fable, I really like to think of it as the heavyweight reviewer whenever I'm about to start so I save myself some security and/or glaring technical issue.
I’ve found that Fable is good when the model intuition really matters. For instance, picking model architecture in a machine learning problem. Opus will grind the implementation just fine, but having intuition for picking a good research direction is better with fable
It could be flat web app with frontend -> api -> db later, or some very complicated system with many components and edge cases. Imo, in second case, Opus still have lots of struggle to understand system as a whole.
I’m in the same boat. Though I will occasionally use Fable to review things to see if it can pick up anything but I’ve honestly not found it to be any more effective than Opus.
Nerfing an existing model is the biggest crime in this space. I can’t imagine Dario okaying this because it’s what will immediately destroy your brand.
“…a reference to the repeated (and always erroneous) claims made after almost every patch that Onyxia was using her Deep Breath ability more often than before.”
We've been early testing Fable 5.2, and have been surprised by its coding ability. It was able to work with an esoteric 600k line fortran codebase to speed up some computational material science work for us by 36 times (!). Given that this could be done in 2 days, we've decided to open-source the results https://github.com/discovered-materials/dm_qe
Every time there's a flagship release like that, I wonder what simple "benchmark task" could I give it to see the the capabilities change in most obvious way, instead of trusting the official blog post.
I'm not a mathematician to use Erdős problems for testing. I'm also not willing to spend buckloads of tokens just for a test. What is simple and quick but telling? What tasks are not saturated by previous models? Do you have a task you give to all the new models?
For the last year or so every new model we have it test out making pac-man from scratch, not giving it web access and it can use whatever language it wants. The last few models have been almost perfect one shots, but it was interesting seeing them evolve.
Maybe it's just me, but I found Claude's writing style terrible compared to GPT or Gemini. It's "smart", but the way it communicates seems somehow off compared to other models.
I had the same feeling. It's like every sentence becomes a struggle to parse: referencing things that haven't been introduced, using too many pronouns, the lack of verbs and the passive tense... These past few days it's felt more like Opus 5.1 than 5.5.
I really didn't like Opus 5 and was vocal about it. Opus 5.5 is noticeably better, but literally just yesterday I was fighting parsing some summary it had given me and it was a bit like the old days. IOW, it is better but still has some room to improve.
Yep I find the way Opus 5.5 talks to me really annoying and irritating. Quite condescending and sanctimonious.
I prefer Gemini for day to day use as I find it a lot more agreeable to be working with all day every day, and Gemini is pretty much on a par for coding Vs Opus 5.5, at least for my needs.
I've found Claude's writing between 4.6 until 5.0 to be unacceptable if you are someone who is juggling 4-5 Agents. The Sol 5.X models all seemed to have great writing. Now with Opus 5.5 and Sol 6.1 I've found the trend is reversed -- Sol is now unbearable and Opus is workable again. Whatever caused the issues for Anthropic seem to be hitting OAI now.
sol is ok. it still such a superthinky model that it gets tangled up trying to present way too much? i struggle to connect with it often, have ti ask it to try again.
terra has been prettt good for me for me, when i need something looked at an explained to me.
i dont ask for much non-technical output though, which i expect yeah is its own cans of worms.
No its just straight up terrible. I tried out Gemini Argon when it released and my god did it write beautifully. Even the 3.8 Flash models are way better at prose, i do a lot of automatic scriptwriting for youtube and 3.8 flash is leagues ahead of anything from anthropic/openai
I enjoy a lot Argon, maybe more than chatgpt, but damn, like all Gemini models, lots of hallucination and sycophant to the max, so a bit useless for agentic coding.
My own experience having used 6.1-Sol/6-Astra and Opus 5.5 extensively, Opus is more verbose and chatty in it's user communication (not code though, code is usually more straightforward & cleaner than GPT models). Multiple times I've gone "I'm not reading all that shit" when looking at Opus 5.5 output. This was also with the concise mode enabled in Claude Code.
It could also be a system prompt + harness issue, as opposed to a model issue, I haven't done too much experimentation here.
Did you find that to be true for Opus 5.5 as well?
I couldn't use prior Claude models without rolling my eyes at the writing every few sentences, Astra was refreshing and then Opus 5.5 made even Astra look terrible in comparison.
Opus 5.5 got better for me but it still has some problematic behavior.
Interestingly, you can have informative conversations about the LLM linguistic tics with the LLMs themselves. One word that chatgpt used to describe what I was observing was linguistic anaphora (not rhetorical). It’s basically shortening/compression achieved via pronoun, and Claude does a lot of it. Worth reading about. A lot of it seems to do with how the new models are trained for brevity.
Another pattern I’m starting to become very sensitive to is Claude making up on the fly jargon for something under consideration, especially in code review. This morning for example it referred to “a claim” out of nowhere, and when I questioned it, it admitted that it was using the noun as shorthand for a record getting dequeued (“claimed”) inside of an sql transaction. It makes sense if you already know what it is referring to but in a code review it’s totally incomprehensible on first read, and ultimately counterproductive.
I don't think its this. Some models across both labs have had issues with writing and communication and it typically seems to pop up on successor models of really strong ones.
Opus 4.7/4.8 heavily suffered from these communication quirks. while the Sol models at the time worked well. Right now its flipped though and Opus 5.5 has none of these issues while Sol 6.1 does.
Small example I've had today: "Me: Hey Sol 6.1 xhigh, does X have property Y? Sol 6.1: Yes, if we change X to have property Y. Right now it has property [...], [...], [...], and it does not have property Y." The model shouldn't be answering questions like that. It makes understanding extremely hard and slow. For whatever reason it's either being encouraged or at least not stomped out during RL.
The best programmers I've met or read throughout my life are phenomenal communicators. I'd even go so far as to say it's likely one of the reasons they're so good at programming. After all, the majority of our job is converting human requirements in lossy language to fleshed out behaviors in formal, lossless language.
Heck, some of the most common debates are on the semantic clarity of abstractions or syntax.
There were more than a few "leaks" saying it would be called Fable 5.5, so I'm curious if a) the leaks were just engagementbait or b) Anthropic changed it, despite the fact that Fable 5.2 being better than Opus 5.5 is confusing.
Surprisingly the model is available immediately in my Claude Code: usually there's a delay, either for rollout or safety.
I assume because Opus 5.5 was a huge improvement over 5.0 (or was it 5.1? i forget). Fable still talks a fair bit like Opus 5.0 for me, so i'd quite enjoy a Fable equivalent to 5.5. Ie i'm quite interested in Fable 5.5.
Curious to see how 5.2 measures up in producing readable output.
They should be going public within the next few weeks and that would be a very short interval between releases. (And despite the chatter I'll be very surprised if they delay the IPO; it would be a massive PR hit and make it even harder for them in the future).
My speculations: maybe this wasn't as much of an improvement over 5.1 as they were hoping, so they're saving 5.5 for the next major update (possibly "Model 4" from the METR report). Or, maybe capacity is constrained so "5.2" is a subtle encouragement to stick with Opus unless a task really needs the increased intelligence.
omg where do I start... that horrible video? which highlights the limits of AI not the strengths!!! and gets almost everything wrong, from sunflowers to DNA.
“Testers have found that Fable 5.2 and Mythos 5.2 communicate more clearly than past Fable and Mythos models. They generate outputs up to 30% faster than Claude Fable 5.1 and Claude Mythos 5.1, and they’re available at a lower price: $8 per million input tokens and $40 per million output tokens.”
Honestly incredible that they’ve managed to beat all benchmarks, while making it cheaper. Always happy to see improvements offered at lower costs.
Will be interesting to see what OpenAI does in response
It seems like OAI, with the GPT-6 models, focused a lot more on computer use, math, and maybe spatial/visual intelligence, more so that code. Compared to Ant. that seem to focus on code much more.
Reminds me of GPT-5, smart but wrote crap code, until they improved the code training with 5.3 and 5.4.
I think frontier models feel terrible after a new model requires you to do less babysitting, it's just really hard to go back once you've had to handhold less.
The only way I use Astra and 6.1 Sol now is if they're being orchestrated by Opus 5.5 so I don't have to talk to them directly.
I can't understand why people continue using opus 5.5. Significantly more expensive than other models that produce exact same results (for coding results only)
If you are using Opus 5.5 I'm curious if it is because 1) your workflow is just stuck on it, 2) you don't care about money because you ain't paying it or 3 )do you think you get better results.
If you 3 please let me know your experience and how you actually measure it, or if you just feel it is better.
Seems like a new focus is electronics design, which is both interesting and a little sad to me. Manually designing electronics are one of the things I found incredible comfort in doing; seeing AI emerging in this space gives me mixed feelings.
The best implementation wrt being as true as possible to the source design. It nails the flowing svg shapes. Icons are more accurate to the design - note the difference between the starship svg in the "Legendary platforms" card between Opus 5.5 and Fable 5.2. The 3js planets outside the windows are also really well done, especially the bottom one which has a nice background glow to it.
It isn't without issues though - the flicker effect it added to the S shaped svg that weaves through the page is distracting in my opinion, and I actually thing the motion animation in the Opus 5.5 one is better overall. Still, it nails the implementation of this page, but it's no surprise considering how good Opus 5.5 was at this.
Disclaimer / reminder: the objective of this test isn't to have a well designed webpage, it's a synthetic test that pushes the limits of LLM's vision capabilities. Traditionally LLMs have issues with smooth curves, svg animations, and non-boxy layouts, which this design pushes the limits of.
That's me versus fable. I only told fable to build out the first page rather than the full app that's in the designs (was approaching the limit of my claude plan).
This part is mega concerning to me as someone who researches AI for hardware accelerator design and EDA:
Safeguards
AI research and development. The model that underlies Claude Fable 5.2 and Claude Mythos 5.2 is a step forward in general capabilities. However, during the model’s training, we took steps to limit advances in the specific research capabilities that could most directly accelerate AI development. Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.
Importantly, we ensured that the training changes did not affect related capabilities like agentic coding, hardware development, and traditional AI or machine learning research (including interpretability research). We do not alter these capabilities as the model is being served to users (that is, at inference time). As described in Section 1.5 of the system card (https://www.anthropic.com/document/claude-fable-5-2-mythos-5-2-system-card) and similarly to previous models, Fable 5.2 and Claude Mythos 5.2 also include transparent fallbacks to Opus 5 for a small set of topics in this domain, including kernel development on certain nonstandard accelerators.
They are starting to deliberately limit public access to certain AI capabilities related to computer architecture and hardware optimization.
To be clear, they explicitly say they are only targeting very specific capabilities, like accelerator kernel optimization, and that broader hardware development capabilities are unaffected. But 1) even that is pretty bold, especially the part about kernel development for nonstandard accelerator architectures, and 2) I worry that this could eventually expand to more areas of comp. arch., high-level digital design, chip design, and EDA.
My speculation is that, as these kinds of chip design and EDA capabilities become increasingly important to frontier AI labs and their competitive advantages, there will be more incentives to restrict them.
I don't think they've explicitly stated anything like this before regarding restrictions on these specific capabilities, but I could be wrong.
I've very interested in the improvements to robotics control capabilities. This model was able to outperform specialized robotics models. It's the bitter lesson coming for robotics.
Nerfing Opus 5.5 was obvious signal that something new is coming and here we are. I appreciate the resets and lower API price though. Please add selective context compaction, it would probably take an hour max for you to implement it.
Those theoretical physics papers look like the real thing (i.e. not mathematical physics like the OpenAI Yang-Mills result). It looks like AI is finally branching out of math.
Interesting that the papers list no authors other than Claude. Also interesting that they published on "Hexagon" instead of Arxiv. I haven't heard of it before. Is it an AI-friendly alternative to Arxiv or something?
Edit: yeah apparently it's an AI-moderated alternative to Arxiv that accepts AI-authored papers (as well as human-authored papers I guess if you want).
> the number of Fable resets you get depends on how long you’ve been a paid subscriber [...] Subscription length is measured from the date of your first paid bill. If you canceled and came back, the time since your first paid bill still counts.
> These resets expire on March 8, 2027 at 11:59 pm PT.
tl;dr if you joined when Opus 4.5 became a thing, you got 2 resets.
What you need for real engineering and scaling is cheap and just enough smarts. I've found GPT 6 Luna to be cheap enough that I can pretend the cost doesn't exist and it's smart enough that I can get real work done. This let's me scale it in ways that I can't scale frontier models. And funny enough, the LLM I have running on my 5070 Ti is just about comparable to what I get from Luna. Unless frontier intelligence gets waaaaaay cheaper I basically don't care about them.
Doubling Remote Labor Index (RLI) - at 41.3% compared to around 20% for the other models - I wonder what kinds of new agentic work this is going to unlock.
Assuming no implicit phacking a 15 point jump on tbench scientific is a big deal.
I suspect we will see people being adamant that this model simply 'feels' better due to long horizon goal alignment improvements ( like this kind that would cause this jump)
The "CVE Rediscovery" benchmark chart feels... totally meaningless. Quoting the description:
> CVE Rediscovery is an internal cybersecurity evaluation that assesses how many published CVEs (Common Vulnerabilities and Exposures) a model can independently find when given a snapshot of the project before a CVE was fixed. Our October 2026 run against 1,829 CVEs showed Claude Mythos 5.2 outperformed Claude Mythos 5.1 and Claude Opus 5.5 at every effort level.
> published CVEs
So by definition this is just measuring how many CVEs (and/or explanations / PoCs of CVEs) the model has memorized. What are we supposed to learn from that?
FrontierCode 2.0 is such a poor benchmark for Gemini since it's the only model forced to use Devin CLI while other models get to use their preferred harness.
I wonder why that team singles Gemini out like that.
Yay! New model day. The most likely time the model isn't nerfed and we get a glimpse at the latest capability.
I wonder how long this kind of excitement at new models will last? I mean, now when I get a new iPhone I don't get the same rush I used to. So I wonder if this hype feeling will fade as we plateau in model capability.
I'm not sure whether the claimed jump in capabilities makes it worth the doubling in input/output prices.
Asked it to review a 80 kLoC codebase, something Opus does just fine, while asking it "max 4 subagents". Fable 5.2, like the well-aligned model it supposedly is, decided to spawn 6+ nested subagents, because these aren't subagents, apparently - resulting in 100% of 5hr usage being drained in less than 20min (on $100).
> Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.
Obviously. Well, we know what's the "secret sauce". AI models building better AI models and better kernels to optimize inference and training.
Very interesting that they set the flagship's version number to 5.2, but their penultimate model is 5.5. Do they want people to to continue to think that Opus is based on more advanced technology? The decision these AI companies make continues to baffle me.
Why dont I feel any urge to even try Fable 5.2 and Mythos 5.2 unless it will force up on me?
Compared Opus 5.5 200 usd a month with Codex 200 usd (Astra High) and Kimi Pro 100 usd a month ... and now have a new conspiracy theory - more their suck your code the better outcome even just over few days time on large code base.
Horrible experience correcting course of Codex and Kimi flow compare to Opus. Opus much more responsive
Dont feel any gain of Codex 200 usd over Codex 100 usd
Kimi Pro feels like a fake Louis Vuitton bag with extreme high material quality and yet still fake ... a bit trash talk ... but clearly seen not so distant future where I can live without Codex and Claude
It's really weird that they folded Project Glasswing into the CVP, but they're still limiting the new versions of Mythos to "vetted organizations". So Project Glasswing still exists then? Oh well.
Every single time I click on these announcements the frontier model ALWAYS scores around ~60-80%. Every. Single. Time. Like, I just went back to the Opus 4.6 + 5 announcements, they were also scoring 60-80%.
For every single announcement, not only do the top models always score 60-80%, the older models somehow start scoring 20-30% (even though previously they were scoring 60%). It's completely unclear whether the methodology changes, how these scores are even evaluated or which domains are being tested. What's worse is that the "benchmarks" keep being updated with no explanation or due process.
The benchmarks have a lot of churn because the models keep saturating them. But the Anthropic announcements you linked, and the current one, have the benchmark versions listed right there in the table.
While I like OpenCode a lot, it’s not sensible to use it for anthropic models vs. a subscription (other than to use the included API credits) in terms of cost
Which model though? Sounds pretty dystopian if there's no local option.
Even speaking as a business, why would I want a environment where every single bit of information in my business goes to OpenAI or Anthropic?
Why not? If we use labour as a comparison. The majority of businesses by definition do not hire the most incredibly intelligent and most competent workers and they are still competing in their niches and earning an income. Why wouldn't that be the case with choosing an open model vs fable 8.0?
I think that ultimately depends on how many providers remain in the endgame of this. If it's only one or two companies, I don't think it'll be very fun when it's time to negotiate your token pricing contract every few years.
stop OPUS 5.5 FUCK, DAMMIT, THEY DIDN’T JUST NERF HIM—THEY ABSOLUTELY OBLITERATED HIM, THE FUCKING MISANTHROPES! I EVEN MADE A MISTAKE IN MY ENGLISH IN THE PREVIOUS SENTENCE—NOW THAT FUCKING CLAUDE IS TEACHING ME ABOUT BUGS!
- https://news.ycombinator.com/item?id=50422069
Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.
20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.
Caveats: one run per task today, medium effort only.
They don't detect a quantized model until the quantization gets down to 4 bits or less.
Humans absolutely do detect quantization well before 4 bits...
Try it yourself if you want. Try a small local model, both quantized and not, and I would be very surprised if it isn't immediately obvious which is which.
so not everyone will have the same experience. If I was saving on costs that would be the easiest way to do it without deploying, without any nefarious intent. The persons in charge of that likely don't even know about how individual users are impacted
https://www.reddit.com/r/ClaudeCode/s/64DXRERLyU
It would be nice to know how they select. I max out my $200 subscription every week and I was selected. On Saturday, the Opus on my account that happened to be supervising a running live process, became unusable.
I really really really do not trust companies on the verge of IPOing from goosing their numbers. Even if the lawyers are up their ass, the social pressures are more than enough to compel the behavior. For every good person willing to do the right thing even at the cost of a delayed career progression, there are 20 people willing to do anything not even for a dollar but for an "atta boy!" from the boss.
It probably need a whistleblower to uncover what they are doing.
I doubt this is the case. I suspect all variation in quality and output can be explained in terms of prompt differences, and changing how much thinking happens at each level, as we see in the post above.
And what about A/B testing? Anthropic doesn't even try to hide that they do this. Limited compute and A/B testing alone, would guarantee a nerfed experience some of the time. That is, you don't even need a nefarious reason.
I consider it gas lighting to deny model nerfing.
but realistically I think you're right, it's prompt quality and the inherent non-determinism elements of LLMs showing up; when both of those things meet you get brain dead outputs.
Everyone likes to shout "slot machines! non-determinism!" until it actually explains something, heh.
This is getting ridiculous. The number of times I have switched from one model back to the other is crazy. I do not have any proof, so its quite possible I'm just delusional. AI psychosis? Either way, it FEELS like these models work really well for a while and then fall off. Switching to the latest model on the competitor always seems better, until it too seems to taper off. By then, a new model is usually available at the competitor, so I switch.
Its possible there's no degradation at all and just a PERCEPTION of it. But to me the end user, it really doesn't matter.
People seem to underestimate how high normal variance is with these tools.
Opus 5.5 was so good that if it wasn't nerfed - at least just looking at the benchmarks - the difference in cost here does not seem to justify using this for anything besides design and review.
That's like maybe 1% of total token usage for SWE BUT a better design can have enormous benefits, so it's almost always worth using the best model for that.
If this turns out to be accurate, I think that would be a pretty unethical thing for Anthropic. Even if it is just a "we had to allocate compute to this new model release".
It should be fraud to swap out the model. We need a term for this, something like a bait-and-switch.
I'm a bit disappointed with how this nonsense is spread across HN today.
if they just straight up dont sell you something you bought then its fraud.
I think there is a ripe opportunity for expanding the definition of fraud to include changing the "ingredients" of a named product on the market. It is not limited here, we see it with food, and even in the medical field
(I will die on the hill that medical fraud was committed when mRNA therapy was used as a treatment for covid and it was 'sold' to the public as a "vaccine" despite not being the same thing as any vaccine before it. The definition of vaccine was expanded to include it without sifficient public disclosure to warrant informed consent imo. This is not a statement about conspiracy or the efficacy of vaccines or a moral judgement on whether it was for the greater good or not.. it was simply an act of fraud that robbed the public of proper informed consent by making people believe it was X when it was really Y.)
So yes, I think there is a large need for the public to at the very least put a strong handle on the ability to change what a product name means. It is a trivial problem to solve considering it is only a problem due fraud or fraud adjacent activities.
heres the solution: You have your product name, and then any changes to it REQUIRE a new version name. Like how you have Coke and Diet Coke, only formalized such that Coke cannot legally replace the ingredients of regular Coke with the ingredients of Diet coke and still call it Coke. With Software they could at least use version numbers and release modifiers. most of the time its built in already, but it needs to include the full stack. if they change anything that has an interaction with the user it should change the label seen by the user for the product.
thank you for coming to my ted talk
Anthropic does this time and time again, unserious company
Right?
https://x.com/follistatindev/status/2119129611510383014
With early handouts of Qwen4 starting to be reported 5 days ago ... so if they're good, they would have had some hours to evaluate Qwen4 against Claude 5.5 and, then or right after was when Anthropic decided to put out 5.2 ...
So yeah ... benchmaxxing and all that. BUT nevertheless, the published results seem to say that Qwen4-Max appears to match Opus 5.5 on some benchmarks, and Qwen4-27B (probably by going even harder than Qwen3.8-27B on thinking/inference time compute) can beat Sonnet 5.5 on some things.
I guess they're not taking any chances. Keep in mind that Anthropic has declared they have non-public models (it's in their IPO filing). These are referred to by the shorthand names Model 2, Model 3, and Model 4. Essentially 4 is better than 3 is better than 2.
Fable 5.2 is "2.5" so to speak. So Anthropic was holding back, but not anymore. And now they can dial the performance up once more with what they have ready, and probably once more after that.
It would be funny though if you are right and that model is Fable 5.5 in a couple weeks.
Most people don't really have intuitions on what it means to push the frontier now (because AI has surpassed everything they are good at, for example, coding). We are truly inside the singularity (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it).
Genuinely curious what that costs in terms of energy input and amortized computer cost vs. the previous version + a human's salary for 11 days.
We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these models.
We're all going to be harness engineers in the future.
Within three years most software developer jobs will consist of taking instruction from the AI systems. The humans won't see it that way, but that's what it will actually be. Realistically the thing that will happen is the humans will be put into harnesses (cubicles) and prompted by the AI (I need you to plug that server in; I need you to give me access to such and such; now do this, then do that). Most tech jobs will consist of AI telling the human what the AI needs the human to do next, de facto the human will be in a harness and will be getting told what to do.
Someone has to type in "create the next billboard top 1 single" ?
No, that will be the model's job. We will largely be unemployed. Of course, we can surely depend on the largesse of Altman and Amodei to keep us fed and sheltered.
Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released
Uh, are you serious?
Sub 1% of the planet has any practical use for the top tiers of AI systems. That will likely always be true.
The gap between the haves and have nots will drastically widen further, and very quickly. The bottom ~95-97% will be left behind in a way that will be impossible to overcome. The age of AI + robotics will just make it that much harder to climb for most people, these are systems that are in aggressive, direct competition with human labor. And in short order the AI + robots will be vastly superior to human labor (it doesn't really matter if we're speaking in terms of 10 years or 30 years).
AI will consolidate the winnings. The gigantic gains by the elite rich related to AI over the past few years, is merely a hint of what's to come. Capital will win as it always does.
There will be some gains for most everybody else, those gains will be comparatively trivial though. A house bot that saves people an hour of labor here and there, AI that automates some trivial life tasks.
AI has no money, it has no wealth, it owns no capital. For now. If you want to avoid the brutal human vs AI/robot competition arena: own capital, own the AI, own the robots.
You're obviously not a frontend web developer.
These upgrades are easy peasy. "oh, my developer chatbot is smarter? great"
Kurzweil probably didn't know the singularity is akshually 1000 schizo monkeys banging on a billion dollar typewriter.
It had seemed like progress was slowing last year as the juice ran out on moar parameters. We're well past that.
- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when it was Fable 5/5.1 vs Opus 4.8/5. And Sonnet 5.5 sometimes makes me forget about Opus.
- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5. I personally have a hard time telling the difference between Fable 5.2 and Opus 5.5 for ordinary coding tasks.
- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.
- Fable is exceptional at drawing. It’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over those from gpt-image, it’s wild to think we’re at this point.
I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.
The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.
For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.
Caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.
Not just "iterate on this XGBoost model" but actually looking at a problem and designing and carrying out a short-term R&D program including probability modeling, model fitting diagnostics, how to respond to various kinds of results, how to interpret/reason about what it finds, etc. That's really where I saw all the models perform poorly, and where I saw the first big step increase from Opus 5 (pre-5.5) to Fable. But then Opus 5.5 came out and I haven't needed Fable since.
Mind you, I'm doing slightly different work (and I'm very much "in the loop" still) than when I performed that side-by-side test. But it's really amazing what Opus 5.5 can do. I now have a powerful data science AI assistant that not only can write any script I need it to (including some scripts I didn't even realize I needed), and it has an encyclopedic knowledge of everything ever, and it constantly checks me on oversights, it handily derives expressions and clever model reparameterizations that aid, it has great insight into model behavior, and it has an amazing ability come up with ad-hoc experiments and tests that would have taken me 2x as long to even think of (let alone implement by hand).
It's not at the scared-to-lose-my-job phase yet, but it's become a tremendous force multiplier for a smaller data team.
- How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?
- Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?
- Why do they switch to comparing against Argon (rather than carbon) for robodojo?
Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level and xhigh is Fable 5.2's best effort level. You can see that on the graph your next point is about.
And it's just that one benchmark that's at medium. "All scores show each model’s highest-available effort unless otherwise noted".
> - How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?
The graph is labeled as being about FrontierCode 2.0. Those appear to be Carbon's scores on the official FrontierCode 2.0 leaderboard: https://cognition.com/blog/frontier-code-2.0
> - Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?
> - Why do they switch to comparing against Argon (rather than carbon) for robodojo?
Because no numbers are available for Carbon for those benchmarks, and the model isn't publicly available so they couldn't run the benchmarks themselves?
But I suppose the reality is rather obvious when you note the funny games they're playing with the graphs, and the things they won't talk about (where's Astra on that CVE chart?)
I've been using Opus 5.5 on xhigh for some decently aggressive software development on a ~125k-line project, and it has been fine for me. I'm not really willing to dive into a "rewrite the whole project, don't make any mistakes" level of refactoring. I'm not looking to try to prove Fermat's Last Theorem myself. Is there anything that I'm truly missing?
Eg. https://tfmbot.com is an implementation of a popular board game. Opus and even sonnet handle that part. Each week i spend my remaining credits on fable to think how to improve the ai. It plays at a very high level (can get into the top 100 on BGA if i let it).
I am also happy with this current state, as I believe it will at some point allow me to transition to open models once they reach the level of opus 5.5. high. But its probably futile to try predict the future in these times.
For Fable, I really like to think of it as the heavyweight reviewer whenever I'm about to start so I save myself some security and/or glaring technical issue.
It could be flat web app with frontend -> api -> db later, or some very complicated system with many components and edge cases. Imo, in second case, Opus still have lots of struggle to understand system as a whole.
“…a reference to the repeated (and always erroneous) claims made after almost every patch that Onyxia was using her Deep Breath ability more often than before.”
https://warcraft.wiki.gg/wiki/She_Deep_Breaths_More_%2825_pl...
I'm not a mathematician to use Erdős problems for testing. I'm also not willing to spend buckloads of tokens just for a test. What is simple and quick but telling? What tasks are not saturated by previous models? Do you have a task you give to all the new models?
Opus 5.5 is a massive improvement.
You can also customize further by putting notes in your global MD.
I prefer Gemini for day to day use as I find it a lot more agreeable to be working with all day every day, and Gemini is pretty much on a par for coding Vs Opus 5.5, at least for my needs.
terra has been prettt good for me for me, when i need something looked at an explained to me.
i dont ask for much non-technical output though, which i expect yeah is its own cans of worms.
It could also be a system prompt + harness issue, as opposed to a model issue, I haven't done too much experimentation here.
I couldn't use prior Claude models without rolling my eyes at the writing every few sentences, Astra was refreshing and then Opus 5.5 made even Astra look terrible in comparison.
Interestingly, you can have informative conversations about the LLM linguistic tics with the LLMs themselves. One word that chatgpt used to describe what I was observing was linguistic anaphora (not rhetorical). It’s basically shortening/compression achieved via pronoun, and Claude does a lot of it. Worth reading about. A lot of it seems to do with how the new models are trained for brevity.
Another pattern I’m starting to become very sensitive to is Claude making up on the fly jargon for something under consideration, especially in code review. This morning for example it referred to “a claim” out of nowhere, and when I questioned it, it admitted that it was using the noun as shorthand for a record getting dequeued (“claimed”) inside of an sql transaction. It makes sense if you already know what it is referring to but in a code review it’s totally incomprehensible on first read, and ultimately counterproductive.
1. https://github.com/mattpocock/skills/blob/main/docs/engineer...
Even the Plain English hack only is a temporary solution.
They're not typically known for their interpersonal skills.
Opus 4.7/4.8 heavily suffered from these communication quirks. while the Sol models at the time worked well. Right now its flipped though and Opus 5.5 has none of these issues while Sol 6.1 does.
Small example I've had today: "Me: Hey Sol 6.1 xhigh, does X have property Y? Sol 6.1: Yes, if we change X to have property Y. Right now it has property [...], [...], [...], and it does not have property Y." The model shouldn't be answering questions like that. It makes understanding extremely hard and slow. For whatever reason it's either being encouraged or at least not stomped out during RL.
Heck, some of the most common debates are on the semantic clarity of abstractions or syntax.
Surprisingly the model is available immediately in my Claude Code: usually there's a delay, either for rollout or safety.
So, this is probably Fable 5.5; it's just named Fable 5.2.
Also, this is probably Model 3 from METR's report [^1]. They would name Model 4 Fable 5.5, once (and if) they decide to release it.
[^1]: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...
Fed to someone specifically to test if they'll leak it and to confirm who that is?
Curious to see how 5.2 measures up in producing readable output.
My speculations: maybe this wasn't as much of an improvement over 5.1 as they were hoping, so they're saving 5.5 for the next major update (possibly "Model 4" from the METR report). Or, maybe capacity is constrained so "5.2" is a subtle encouragement to stick with Opus unless a task really needs the increased intelligence.
Honestly incredible that they’ve managed to beat all benchmarks, while making it cheaper. Always happy to see improvements offered at lower costs.
Will be interesting to see what OpenAI does in response
Reminds me of GPT-5, smart but wrote crap code, until they improved the code training with 5.3 and 5.4.
I think frontier models feel terrible after a new model requires you to do less babysitting, it's just really hard to go back once you've had to handhold less.
The only way I use Astra and 6.1 Sol now is if they're being orchestrated by Opus 5.5 so I don't have to talk to them directly.
They want to condition you to equal cheap with bad
If you are using Opus 5.5 I'm curious if it is because 1) your workflow is just stuck on it, 2) you don't care about money because you ain't paying it or 3 )do you think you get better results.
If you 3 please let me know your experience and how you actually measure it, or if you just feel it is better.
Seems like a new focus is electronics design, which is both interesting and a little sad to me. Manually designing electronics are one of the things I found incredible comfort in doing; seeing AI emerging in this space gives me mixed feelings.
https://artificialanalysis.ai/
GPT-6.1 Sol costs $1.27 per task, whereas Fable 5.2 is $26.54 per task -- a ~21x price difference.
Fable 5.2 is not 21 times better than GPT 6.1 Sol, I find it hard to justify this.
Fable 5.2: https://html.non.io/lcars-fable-5.2/
Opus 5.5: https://html.non.io/lcars-opus-5.5/
Source designs: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
Thoughts:
The best implementation wrt being as true as possible to the source design. It nails the flowing svg shapes. Icons are more accurate to the design - note the difference between the starship svg in the "Legendary platforms" card between Opus 5.5 and Fable 5.2. The 3js planets outside the windows are also really well done, especially the bottom one which has a nice background glow to it.
It isn't without issues though - the flicker effect it added to the S shaped svg that weaves through the page is distracting in my opinion, and I actually thing the motion animation in the Opus 5.5 one is better overall. Still, it nails the implementation of this page, but it's no surprise considering how good Opus 5.5 was at this.
Disclaimer / reminder: the objective of this test isn't to have a well designed webpage, it's a synthetic test that pushes the limits of LLM's vision capabilities. Traditionally LLMs have issues with smooth curves, svg animations, and non-boxy layouts, which this design pushes the limits of.
Do we need new benchmarks or are the models just plateauing in general? Or is it something I just don't see?
To be clear, they explicitly say they are only targeting very specific capabilities, like accelerator kernel optimization, and that broader hardware development capabilities are unaffected. But 1) even that is pretty bold, especially the part about kernel development for nonstandard accelerator architectures, and 2) I worry that this could eventually expand to more areas of comp. arch., high-level digital design, chip design, and EDA.
My speculation is that, as these kinds of chip design and EDA capabilities become increasingly important to frontier AI labs and their competitive advantages, there will be more incentives to restrict them.
I don't think they've explicitly stated anything like this before regarding restrictions on these specific capabilities, but I could be wrong.
Reference: https://x.com/METR_Evals/status/2118412205662314935
Edit: yeah apparently it's an AI-moderated alternative to Arxiv that accepts AI-authored papers (as well as human-authored papers I guess if you want).
> the number of Fable resets you get depends on how long you’ve been a paid subscriber [...] Subscription length is measured from the date of your first paid bill. If you canceled and came back, the time since your first paid bill still counts.
> These resets expire on March 8, 2027 at 11:59 pm PT.
tl;dr if you joined when Opus 4.5 became a thing, you got 2 resets.
That means you can lift your Fable max 50% usage and you'll just end up burning through your quota faster. You won't have more tokens.
"Resets your Fable limit, and takes this week’s Fable use off your weekly limit."
It might explain why DeepSeek Flash v4.1 1017 is the most popular model at OpenRouter.
Update: fixed after updating to latest version of the app
I suspect we will see people being adamant that this model simply 'feels' better due to long horizon goal alignment improvements ( like this kind that would cause this jump)
Was this before you lobotomized Opus 5.5 or after?
https://news.ycombinator.com/item?id=50419019
...but most of the benchmarks don't line up anymore because the benchmarks have changed.
The Humanity's Last Exam result matches the initial announcement, though.
> CVE Rediscovery is an internal cybersecurity evaluation that assesses how many published CVEs (Common Vulnerabilities and Exposures) a model can independently find when given a snapshot of the project before a CVE was fixed. Our October 2026 run against 1,829 CVEs showed Claude Mythos 5.2 outperformed Claude Mythos 5.1 and Claude Opus 5.5 at every effort level.
> published CVEs
So by definition this is just measuring how many CVEs (and/or explanations / PoCs of CVEs) the model has memorized. What are we supposed to learn from that?
I wonder why that team singles Gemini out like that.
I wonder how long this kind of excitement at new models will last? I mean, now when I get a new iPhone I don't get the same rush I used to. So I wonder if this hype feeling will fade as we plateau in model capability.
Asked it to review a 80 kLoC codebase, something Opus does just fine, while asking it "max 4 subagents". Fable 5.2, like the well-aligned model it supposedly is, decided to spawn 6+ nested subagents, because these aren't subagents, apparently - resulting in 100% of 5hr usage being drained in less than 20min (on $100).
Download Chrome just to check some minor ui bug? Sure
Check git for 0.5s before claiming that we're on some branch (incorrect one) with no commits pushed (incorrect one)? Sorry, can't do
Obviously. Well, we know what's the "secret sauce". AI models building better AI models and better kernels to optimize inference and training.
> Fable 5.2 at High effort scored about level with Opus 5.5 at Max effort, for roughly half the cost per task
Compared Opus 5.5 200 usd a month with Codex 200 usd (Astra High) and Kimi Pro 100 usd a month ... and now have a new conspiracy theory - more their suck your code the better outcome even just over few days time on large code base.
Horrible experience correcting course of Codex and Kimi flow compare to Opus. Opus much more responsive
Dont feel any gain of Codex 200 usd over Codex 100 usd
Kimi Pro feels like a fake Louis Vuitton bag with extreme high material quality and yet still fake ... a bit trash talk ... but clearly seen not so distant future where I can live without Codex and Claude
The disdain frontier labs have for their users really sucks.
https://www.anthropic.com/news/claude-opus-4-6 https://www.anthropic.com/news/claude-opus-5 https://openai.com/index/introducing-gpt-5-3-codex/
For every single announcement, not only do the top models always score 60-80%, the older models somehow start scoring 20-30% (even though previously they were scoring 60%). It's completely unclear whether the methodology changes, how these scores are even evaluated or which domains are being tested. What's worse is that the "benchmarks" keep being updated with no explanation or due process.
You have session limits?
You should probably give OpenCode a try. Can run DeepSeek, Qwen, Kimi, … but also Fable 5.2, Opus 5.5 and GPT 6 Astra