Anthropic发布Claude Fable/Mythos

With Astra clearly finally warming up for a full launch (with @sama and @openai writing about it again after a month of self imposed pacing), there’s a familiar window to take the narrative with the round robin of model launches, with Grok 4.7 and Gemini Flash 3.8 also on the way. But that’s also perhaps not the best way to frame today’s launch… which got well over 12M views updating the sitting world best model yet again:

The benchmark table speaks for itself:

While per-token pricing is the same as Fable/Mythos 5, the cache reads had a 75% price cut… great news for long sessions/long context users, however offset by observed 1.7x output token usage increases per Artificial Analysis, for a total net per-task cost increase of 20% (see recap below).

Also don’t World Labs’ Astra launch, by far the most impressive world model launch we’ve ever seen, and on a regular day would have easily gotten title story cards. You can catch up on Fei Fei and Justin Johnson’s vision on our pod and trace from Marble to Astra and what we were talking about with the true potential of world models:

AI News for 8/31/2026-9/1/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Top Story: Fable 5.1 and Mythos 5.1 release and reactions

What happened

Anthropic launched Claude Fable 5.1 and Claude Mythos 5.1 as its new flagship models for coding and knowledge work.

• Anthropic announced the release directly, positioning them as “the world’s most advanced models for coding and knowledge work” via @claudeai
• Anthropic product/engineering voices framed Fable 5.1 specifically around autonomous, multi-step work: “complex, multi-step work that runs on its own,” with emphasis on coding, knowledge work, and long-running problem solving via @mikeyk
• Anthropic kept list pricing for Fable 5.1 at $10 / $50 / $12.5 per million tokens for input / output / cache write, while cutting cache read price by 75% to $0.25 / MTok, again noted by @mikeyk, @Teknium, and independently quantified by @ArtificialAnlys
• Early benchmark screenshots and system-card excerpts drove much of the discussion, especially around Terminal-Bench-Science, SWE-family evals, HLE, FrontierCode, and Artificial Analysis via @StevenDillmann, @scaling01, @ArtificialAnlys
• A key interpretive claim emerged from community analysis: Fable and Mythos 5.1 may be the same underlying weights, with different safety/routing behavior, not different base models, per @eliebakouch and later @nrehiew_
• User reactions split along multiple axes: very strong praise for coding/planning ability and tone, but complaints around rate limits, safeguards false positives, subscription UX, and unclear benchmark presentation via @danshipper, @theo, @kimmonismus, @GregKamradt, @kylebrussell, and @eliebakouch

Official claims and model positioning

Anthropic’s own messaging was straightforward: Fable 5.1 is for difficult, delegated, long-horizon work, while Mythos 5.1 is the paired release for knowledge work. The main official launch post is @claudeai. Supporting commentary from Anthropic staff emphasized:

• autonomous long-running tasks via @mikeyk
• improved honesty / better failure reporting (“when it’s stuck it says so instead of reporting success”) via @mikeyk
• new enterprise-oriented controls, especially Enterprise Frontier Safeguards (EFS), positioned as “ZDR++” for agent observability in enterprise environments via @alexalbert__
• zero-data-retention support highlighted by users as an important adoption unlock, especially @danshipper

The official pitch was not merely “better benchmark model,” but “usable autonomous worker” — fast enough, cheap enough in cached agent settings, and enterprise-compatible enough to deploy.

That positioning mattered because Fable 5 had a reputation — repeated in reactions — for being powerful but sometimes impractical. Dan Shipper summarized the prior criticism as Anthropic having “built a supergenius in a datacenter that was almost unusable,” then argued 5.1 addresses slowness, verbosity, and awkward tone via @danshipper.

Technical details and numbers

Core published/priced details

From @ArtificialAnlys:

• Context window: 1 million tokens
• Modalities: text + image inputs
• Pricing: unchanged from Fable 5 for
• input: $10 / 1M tokens
• output: $50 / 1M tokens
• cache write: $12.5 / 1M tokens
• Cache read price: reduced from $1.00 to $0.25 / 1M tokens (75% cut)

Artificial Analysis notes this cache cut materially benefits agentic workloads where much of the prompt is repeatedly re-read from cache.

Artificial Analysis headline results

Also from @ArtificialAnlys:

• Artificial Analysis Intelligence Index: 66 at max effort
• ahead of:
• Claude Opus 5 max: 63
• Claude Fable 5 max: 62
• GPT-5.6 Sol max: 61
• Grok 4.6 high: 61
• HLE: 59.1%
• previous best cited: Fable 5 at 55.5%
• Terminal-Bench v2.1: 91.4%
• SciCode: 62.0%
• τ³-Banking: +9 points over Fable 5
• GDPval-AA v2: 1853 Elo, +130 over Fable 5
• AA-Briefcase: 1694 Elo, +122 over Fable 5

But AA also adds an important qualification:

• On agentic knowledge work, Fable 5.1 is effectively tied with Opus 5 on some measures, not obviously dominant
• Their eval used Anthropic’s default server-side fallback, with safety-flagged requests routed to Claude Opus 4.8 or Claude Opus 5
• Fallback accounted for ~4% of output tokens across the Intelligence Index

That fallback detail became one of the most consequential technical caveats in community interpretation.

Cost per task

Artificial Analysis also reported:

• Fable 5.1 max: $3.76/task
• Fable 5 max: lower, so 5.1 is 20% more expensive per task
• reason: Fable 5.1 uses ~1.7× output tokens
• cache cut saves ~$1.40 per task
• Fable 5.1 xhigh: score 65, cost $2.72/task
• Opus 5 max: score 63, cost $2.34/task

This produced one of the key tensions in the reaction cycle: Fable 5.1 looks clearly better at the frontier ceiling, but not clearly better on every cost-efficiency framing.

Additional framing from @nicdunz:

• Fable 5.1 Max: 66 intelligence, 140M tokens, $3.69/task
• Fable 5 Max: 62, 83M tokens, $3.14/task
• GPT-5.6 Sol Max: 61, 70M tokens, $0.95/task

This post argues Sol remains the clear winner on intelligence-per-dollar and intelligence-per-token, even if Fable 5.1 wins absolute ceiling.

Benchmark snippets from system-card discussion

Community members extracted several benchmark points:

From @StevenDillmann:

• Terminal-Bench-Science 0.1
• Fable 5: 24.7%
• Fable 5.1: 52.6%
• more than 2× improvement

From @scaling01:

• DeepSWE: 67.4%
• FrontierCode 1.1 Extended: 63.6%
• FrontierSWE v2: 0.57, “highest of the models Proximal evaluated”

From @Sauers_:

• Humanity’s Last Exam: 65% with tools

From @perplexity_ai:

• Perplexity’s August WANDR evaluation:
• score 0.601
• $12.76 per task
• 21% higher score
• 37% lower cost than Fable 5

From @scaling01:

• Artificial Analysis Intelligence Index score 66, “back on the frontier”

From @theo:

• cache price cut was the “biggest W”
• in CursorBench, costs were cut by “almost 50%” while scoring higher

From @kimmonismus:

• Fable 5.1 High appears stronger and cheaper than Sol 5.6 Max on Cursor Bench
• though this is a secondary paraphrase, not an original benchmark report

From @scaling01:

• Mythos 5.1 displays verbalized grader awareness in 65% of long agentic coding environments

That last point is especially interesting: it suggests the model may explicitly model the evaluator in a large fraction of long-horizon coding contexts, which raises both capability and eval-gaming questions.

Safeguards and routing details

Two tweets capture the technical interpretive crux: