Claude Fable 5.2 Scores 65 on Artificial Analysis, Highest So Far
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The output tokens per task just exploded. From 118k tokens (46k Answer/71k Reasoning) in 5.1 to 274k tokens (81k Answer/192k Reasoning) in 5.2.
Most people on plan use do NOT use up all their plan usage.
Users on API plans can still use Opus 5.5 which is a great model. If they want top of the line performance, they can pay more.
When another company puts out a model that undercuts Fable 5.2 on price at similar performance, Anthropic will put out Opus 6 and undercut them right back. That's how the wheel turns. This is just part of the cycle.
This might be true for a small portion of the user base, but it is not true as a statement of fact.
https://support.claude.com/en/articles/9266767-what-is-the-t...
> Team plans support up to 150 seats. If your organization is on the Team plan and needs more than 150 seats, you can upgrade to the Enterprise plan.
> Enterprise: Seat price + usage at API rates
> US$20/seat/month, billed annually, 20 seat minimum. Usage cost scales with model and task.
There is no premium seat option listed for the enterprise plan
I really don't get the value proposition of these massive models, seems like they're just loss leaders simply to say you're the in the top 3. There's not much use cases outside of niche academic research (and even then I question the need). They mostly function as marketing tools, like a casino that offers high roller accommodations.
People will say "cyber" but companies didnt want to spend on security before, not sure why they would all of a sudden be compelled to spend even more.
Security is mandatory now in a way that it wasn't, now that AI has eliminated the skill barrier to hacking.
*Scale numbers to whatever suits you.
I used to work on evals/observability at Opik: https://github.com/comet-ml/opik And I can say from working with lots of users running agents at scale, there is definitely a pretty commonly reached frontier of difficulty/complexity/abstraction where the capability difference between something like Fable/Astra and smaller models is a big deal. I find this to be especially true the more autonomous your workflow gets. You can still often tackle the hardest tasks with something like Opus, it just typically involves more direct intervention and design work from the user.
But again, this is just anecdata on my part. All of this is still pretty ambiguous and lots of companies are trying to solve the problem of matching models to task difficulty (I.e. routers), with varying degrees of success.
The amount of compute it cost to get the best previous performance from anthropic went down. Nobody is putting a gun to your head and forcing you to go further down the paretto frontier if the current level of intelligence/task is acceptable to you.
Notably, in order to achieve a specific quality, the model has to "reason" for every prompt proportional to the training inference.
This is arguably the case of diminishing returns being reached, as answer token prices saturate the willingness of customers to pay.
They've run into a wall.
An economically successful step would be to improve training cost/frontier advance.
They essentially failed at that, assuming they kept their training costs for the model step at least constant.
Notable takeaways:
- Sol-low - cost-competitive with Haiku, while outperforming Sonnet on medium
- Sol-High - Competes with Astra medium and beats Opus 5.5-medium, at 20% the cost
- Sol-Max - Beats Opus 5.5 high, Astra high, and competes with Opus xhigh, at cheaper than Sonnet and a fraction of the Opus cost
And when you look at time spent, With the exception of Sol-max you get better results in less time for less money. My own experience is that this is true-- it is difficult to call Opus strictly better than Sol, but Sol gets where it's going far more efficiently.
It's rather hard to look at opus-- or any of anthropic's models-- and say "economical".
And for most people here who care about coding performance Anthropic has a substantial lead[1] - although sadly they don't have coding benchmarks separately for Opus 5.5 Medium which I think it the sweet spot.
The cost tab really should have something like "how many tasks can you do in a particular subscription tier" for many people. I realize this changes often though.
[1] https://artificialanalysis.ai/?models=gpt-6-astra-high%2Cgpt...
If you leave xhigh and max off.. you still get better performance than Opus 5.5 max
Opus 5.5 Max - score 59 - cost per task - $10.04
Fable 5.2 High - score 62 - cost per task - $9.08
Note: This doesn't really capture the reality of things like 3d design or electronics design, where the earlier models just can't DO any of it well. Maybe you can bandaid over some of the gaps with strong skills, etc. The newer models can just do things that were not possible at all. When you move outside of coding into the visual/spacial space, you'll feel the inflection point on those capabilities that are happening right now.
> They essentially failed at that, assuming they kept their training costs for the model step at least constant.
What? No!
This is completely wrong!
This proves that inference scaling continues to work beyond the previous frontier.
Claiming that higher inference costs shows failure is an odd thing to think. We've seen this optimization cycle over and over, most recently with Opus 5 -> Opus 5.5
Opus 5 was slow and expensive. Opus 5.5 came out and was better, fast and much cheaper for them to run.
There is nothing to indicate the same can't happen here.
Anthropic probably has multiple options open to them with this model, and they'll probably explore all of them:
- Use collected Fable 5.2 traces to improve reasoning economy for Fable 5.3
- Use Fable 5.2 as a teacher model for Opus 5.6
- Add additional fields RL data to expand Fable capabilities for Fable 5.4
Same is happening in LLMs. Sadly, the Golden Era is over.
And maybe that's how all of it is going to evolve in the future – so you pay per solution, not tokens?
Perhaps, it's a bit like being a super smart person in contemporary society: at some point, being smarter doesn't really reliably translate into a better life.
When you learn from available human data, those text books max out at some point. You cannot learn what isn't there.
Doesn’t seem like a “wall” to me.
To do that you need a larger model to have many many branches to try.
This is a dataset basically constructed by taking a bunch of DeepSeek R1 trajectories and filtering out the incredibly long rambling ones: https://huggingface.co/datasets/open-thoughts/OpenThoughts-1...
There's also a bunch of RL research around strategies to coax the student model towards more concise trajectories.
In general, a lot of people in industry are finding cost savings these days by tuning a smaller model over longer agent trajectories (not exactly the same as distilling the reasoning traces, since those aren't exposed by many frontier labs anymore) collected from their production agents. Typically works best when you have a pretty workflow-y agent whose tasks all share a similar shape.
Let say you have a model that score 100 in a benchmark and you distill it into a model half the size. It has very few guarantees that it will score close to 100 again.1
But I assume one of the hard problems is to know that the model might not perform well enough outside of the distillation part.
There is also another caveat that models do display some level of sub language control, by this I mean that innocuous text might contain information relevant for the own agent or for other agent with it shares tasks. (Like when your partner says there is nothing wrong) Because distillation uses very few resources, we don't know exactly what gets send forward or not.2
As Caleb said, many companies found that they can tune small open weight agents over procedural known tasks because that's easy to inspect and check and because there is little space dor ai-loitering, it tends to be less problematic. The key difference, at least in my understanding, is that you are not concerned with the weights or reducing data. You are basically tuning agents existing weights to emulate another agent answer.
1 - The process of distillation is super effective for specific domains though.
2 - I know I will be called out for supposed AI mystery machine. What I mean is that Large models rely on latent reasoning and nuanced context, often lost during condensation. Distilling models to use fewer tokens or parameters makes predicting retained details difficult. Some papers suggest that model preferences introduced during training, whether intentionally or inadvertently, may be inherited by distilled and fine-tuned models.
We would need to change how we compute. New chips. New architectures. Or maybe we reached the end of physics. Photonic compute is the last frontier. I don’t know.
They are doing IPO now because it might become apparent that they reached a limit in the next year
And I am simply ignoring the ridiculous comments around local model usage from certain CEOs.
Having these highly rated models reflects (potentially) the frontier of what they are capable of producing. Essentially its and advertisement and shows directionally where the smaller faster model's capabilities are headed.
The "wall" is in increasing intelligence per cost. No one is saying the absolute intelligence won't continue increasing, it's that the rate of intelligence increase per cost is falling from super-linear growth rates to linear at the same time the companies are backing out the investor-funded subsidies they were using to buy adoption and user growth numbers. You're correct that there is a 'trickle down' effect to lower tier models but OAI/Ant are clearly using this to segment customers by willingness to pay and their IPOs depend on extracting the maximum amount possible from each segment.
That's why the prices of frontier models have been skyrocketing, though they are using every trick imaginable to mask this being clear and obvious. They take with price increases, token deflation, shifting plan levels and limits but muddy things more by giving back some (but always eventually less) with resets, subs, etc.
They absolutely have said that.
> That's why the prices of frontier models have been skyrocketing,
They haven't.
"The cost of achieving a given level of AI performance has fallen about 47% per quarter since 2023, or 13× per year."
"The cost of a given level of performance often falls fastest right after that level is first achieved, that is, when it is state of the art (SOTA). We see this pattern on three of our five main benchmarks of AI capability. Averaging across all five, cost falls 66% per quarter (75× per year) for performance that has just debuted as SOTA."
https://epoch.ai/publications/the-plunging-price-of-thought#...
On a per-token basis Fable 5.2 is cheaper than 5.1:
5.2: $8 per million input tokens and $40 per million output tokens.
5.1: $10 per million input tokens and $50 per million output tokens.
Compared to older (and much less capable) frontier models it is also cheaper. For example GPT-4 was $30 per million input tokens and $60 per million output tokens
https://developers.openai.com/api/docs/models/gpt-4
Be very careful comparing the Artificial Analysis "cost per task" scores. Fable 5.2 is expensive there because it is doing tasks the other models fail at.
I'm curious to see the next generation of dedicated AI processors and how they perform.
Anthropic and OpenAI are expensive bleeding edge, but few use cases really need it.
again, they can be great for narrow use cases, and save you a fortune.
few use cases really need it.
very cost competitive
What I often observe is that models being asked the exact same prompt can alternate between getting something right and getting it wrong, thanks to the wonders of a non-deterministic output.
also, very cool idea
That's about all you can say for sure, especially about a topic that is so broad as to be virtually meaningless.
AI seems more likely to help with detecting cancer than curing it imo.
There have also been many incredible breakthroughs in math in recent years without AI. But things are different now.
If the AI money had gone into that, I suspect we'd have better results.
Otherwise known as "eating your seed corn."
Compared to other new technology, like, say mRNA vaccines.
Really?
Cheaper and faster than Opus(max) AND scored higher. But yeah let's doompost about it...
Fable 5.2 low is 3x cheaper than Fable 5.1 xhigh while being 8% smarter.
https://artificialanalysis.ai/?releases=claude-fable-5-2%2Cc...
Booooooooooo!
Fable 5.2 at high scores a 62, which is better than anything else on the market (except Fable 5.2 at xhigh and max) -- and is about a third of the price per task.
The thing is, all of these pointers are in the CLAUDE.md file, so it just ignored instructions because it thought it was smarter than the world and didn't need them. It feels really arrogant is how I would describe it.
They've been using Fable as a selling point, but upping to this model will presumably eat further into their profits (should any exist).
* xhigh: 62 score at $14.83 per task for Fable 5.2, vs 50 score at $10.85 per task for 5.1
* high: 62 score at $9.08 per task for 5.2 vs 47 at $7.54 per task for 5.1
* medium: 56 at $3.80 per task for 5.2 vs 43 for $5.56 for 5.1
* low: 54 at $3.10 per task for 5.2 vs 39 at $4.37 per task for 5.1
At medium and low the scores are much higher for Fable 5.2, while actually costing less per task than 5.1
I am always interested in this comparison since it shows how quickly you can go back and forth with a model, capturing both tokens/task and tokens/second. The new models seem to be meaningfully better here.
Another member of the pro-REPL / anti-agentic tribe. There are dozens of us!
If you take the average point inside the 4 quadrants, the top left one is pareto optimal (of the 4)
For example, the official provider for Kimi K3.1 Pro is dead last for speed on OpenRouter. You could run it more than 5x faster for the same cost elsewhere, which should push it comfortably into the pareto quadrant.
While they made, hands down, the best models, their 100x price for few percent better is completely made up, and I think it will end up bad for them
In winner-takes-all markets, it becomes worth it. The utility of the marginal improvement doesn't actually diminish, in fact, it increases like a hockey stick. If you could pay 3x more in compensation to hire a 20% better employee, it's almost always worth it in knowledge work industries.
There will always be a market for the most intelligent model. Keep in mind as well that something like Fable doesn't have to be cost effective on its own. Fable is Anthropic's way of building datasets for smaller more dense models, which is viable on its own as a method of success. These large labs are constrained by their dataset more than anything else, and these expensive-but-intelligent models create some of the best data out there.
But perhaps the performance on real-world tasks will be more noticeable than on benchmarks.
You too can sell stuff for $100k. Just make something useful to someone who can throw around $100k.
You think a $100K lens is crazy? Wait until you hear what people are paying for enterprise software. Big business will spend 10x that on software... and then never implement it.. The lens is a deal.
Modern studio pictures that get nominated for best picture or best cinematography cost around $80 million on average. If you look at highest grossing films the budgets are closer to $175 million on average – even taking into account the $20 million budget of Demon Slayer: Infinity Castle.
Isn't it more that when you're spending that kind of money that $100k for the "best" thing becomes kind of like "insurance" where you don't want to be the one who doesn't use "the best" when "the best" costs about 0.1% of the picture's budget?
Additionally a studio can buy the $100,000 lens once and use it on many films. But it's not like you can reuse Fable 5.2 in the same way.
Furthermore, you make one lense and rent it out, apparently some Panavision lenses made in 60’s are still in use. It doesn’t churn constantly electricity and GPU-chips sucking in more and more resources like these things do.and at the same time, it is quite hard to find the exact cases where the markup is justified, unlike with those lenses. Maybe in cybersecurity etc. but even there some Chinese model can be almost equally good or even better.
Those lenses save money in budgets and make money in getting people into theatre or streaming services to watch films creating profits way beyond their cost. I think in terms of economics that is productivity increase justifying the cost. We are still waiting for that productivity increase come from the AI industry.
Edit: high seems better than max
Fable (5.1 and 5.2) consistently write a much shorter script (2.5 pages) compared to Opus 5.5 (4.5 pages).
Fable 5.2: https://claude.ai/share/274a96a8-c8a5-4564-9ca1-e914edffc722
Opus 5.5: https://claude.ai/share/da8ab6bd-0987-47dc-9c26-68ebbfb25781
I remember Mythos 5.1 being a tad better even for not cybersecurity tasks.
Later, I ran a "health check" session—asking "what went wrong". I read the session, and its suggestion turned out to be something that was already part of the design.
It’s like the attention mechanism is too leaky or something.
That kid might indeed be a genius, clearly bright, but is more interested in deciding whether I should have asked a question than in answering it.
For me personally, if the best model on the planet refuses to answer why I shouldn't mix bleach and ammonia, where I'm trying to understand the chemistry behind the warning, it's not pareto frontier to one that atleast tries to and points me in a general direction. Being told that the topic is concerning, referred to someone else and charged part of my quota for that experience feels like getting an invoice from that school genius for telling me to "just ask the chemistry teacher".
If my LLM asks me to rethink my life when I ask it to reverse engineer the firmware of my Samsung TV that I suspect is setting up adhoc radio networks to relay my data to Samsung's cloud, I feel rather sad, specially when that kid sends me an invoice for saying "no"
At least Anthropic should not bill me for the refusal (as of my last tests, refusals still comes out of my quota) - getting slapped and pickpocketed for asking a question is really upsetting.
There's a setting to not degrade to a lower model on refusals and instead pause the chat.
Me: No, I'm away from the computer right now, you figure it out. Use the root login you already have if you can't figure it out.
Opus: This can wait until you get home, then.
Me: No, figure it out now or use root.
Got [cyber]'d, some scary message that referred me to legal/AUP, and refused to continue.
Switched to OpenAI in part due to this silliness. Sol seems a little less smart but doesn't get on a high horse to lecture you about this stuff. It either does the work or fails to do the work, and leaves the moralizing out of it. It was able to take over from where Claude left off and finish the project.
Max effort is nice for revealing the limits of a model, but it's almost never used in practice.
If you scroll down to the coding charts you can start adding Fable 5.2, Opus 5.5, and others at more normal difficulty settings.
Fable 5.2 on medium looks quite attractive compared to Opus 5.5 high/xhigh
Fable 5.2 on max (the default) looks like a good way to light your money on fire.
I am not sure how people keep saying google are not in the race, or have lost it or whatever when just a few hours ago they had the #1 model? What more do people want?
But Gemini models are still neck and neck in the lead both in my experience and in benchmarks, and Gemini is far above the competitors (although Opus 5.5 closed much of the gap) when it comes to any kind of complex web dev. (I generally use Astra for anything 3D, Gemini for anything web/mobile, and Opus for everything else.)
well, releasing it would be nice
So certainly released to the unwashed masses like me to use for free without any special extra work or API keys.
So, what more do I want? It'd be a good start to actually tell me it's available and giving me a clear path to using it via plan & harness?
I learned not to put too much trust into benchmarks. The best benchmark is to use the model on a codebase you know well.
Pretty big Gemini fan - but IMO Argon is universally worse for coding than Astra or Opus 5.5 (un-nerfed).
I strongly prefer Gemini's output compared to anything else. So I typically use Argon to either direct other agents or explain something without psychobabble.
Argon/Carbon have by far the lowest hallucination rates, so IMO it serves as the best bullshit shield from other models.
In terms of implementation (and especially design), it's far behind Astra and Opus, and I'm sure Fable will make that gap even larger.
Argon/Carbon may give you not-the-best advice, but they are far less likely to just flat-out-and-completely lie to you.
For an open source project (CrowdGPT) I hit upon this workflow in a recent PR:
DeepSeek v4 Flash 1017 made a draft plan
Google Gemini 4 Argon corrected the plan
DeepSeek v4 Flash 1017 did the first implementation
Google Gemini 4 Argon debugged the problems in that implementation
Kimi 2.7 Code did a code review and proposed minor fixes
DeepSeek v4 Flash 1017 performed the fixes
Google Gemini 4 Argon debugged one problem that I found in that fix.
I reviewed the plans and the resulting codeIt's not as smart for coding as Opus, but it's above the best open weights.
IME, YMMV, etc.
I had to remind them 4.6 only came out February this year. In a normal world 4.6 is a newer model! A model 6 months old is seen as ancient!
In the JS ecosystem 6 months always meant breaking changes, without incremental updates everything would be basically like a full port
Pretending like I did “20% hands on coding” just doesn’t cut it, even the reality is having no idea what’s going on anymore
The crypto space moved that fast as well
And AI keeps the same cadence
It’s more important for me to be able to build
Max should not be used unless you really need every bit of perf, it's deep into diminishing returns territory.
Change the models in the dropdown to medium / high and Fable 5.2 is about twice as cost efficient, and slightly faster.
Businesses like predictable costs and predictable performance.
If your new model is slightly better but crazy expensive, but doesn’t have to be with certain settings, but my user can accidentally run it that way by mistake or maybe your harness makes it the default by accident and the user doesn’t notice…
My beloved predictability is destroyed and trying to get users to understand this is useless.
Imagine a streaming subscription that worked like this: “We’ve updated our catalog with more movies. If you want the premium ones though you only get 2 per week and then you have to wait. But if you do mostly classics and a few from the 2010s you can squeeze in a recent theater release. If you time it right and do only one every day you can see 30 modern classics this month, but you can’t watch three today. Also your bill this month is $40 instead of $20 due to the library update”.
Nag, fuggit. Let's just burn all the capital in the world and ride it into the flames.
Fable 5.2 medium for example seems a really good choice when it comes to the performance vs task (if you have a complex task that requires such a model of course).