Anthropic Claude Fable 5 の発表

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Top Story: Anthropic Claude Fable 5 and Mythos 5 release

What happened

Anthropic released two versions of its next major model family: Claude Fable 5 for general availability and Claude Mythos 5 for restricted access.

• Anthropic officially announced Claude Fable 5 as its “first generally available Mythos-class model,” saying it exceeds any model it has previously made broadly available and is state-of-the-art on nearly all tested benchmarks @claudeai, @claudeai

• Anthropic said Fable 5 is the same underlying model as Mythos 5 with added safeguards, and that some cyber/bio/chemistry/distillation-related prompts may be routed to Claude Opus 4.8 instead @ClaudeDevs, @scaling01

• Anthropic stated that for a “narrow range” of potentially harmful topics, queries transparently fall back to Opus 4.8, and claimed 95%+ of sessions never see one according to early user-facing messaging @claudeai, @mikeyk

• Anthropic developer messaging said fallback is available server-side and via SDK middleware in Python, TypeScript, Go, Java, and C# @ClaudeDevs

• Pricing for both Fable 5 and Mythos 5 was reported as $10 / million input tokens and $50 / million output tokens; cache pricing was later reported by third-party evaluators as $12.50 / million cache writes and $1 / million cache reads @scaling01, @ArtificialAnlys

• Fable 5 kept Anthropic’s 1M-token context window according to Artificial Analysis @ArtificialAnlys

• Anthropic put Fable 5 into Pro, Max, Team, and seat-based Enterprise plans until June 22, then said it would require usage credits due to capacity constraints, with plans to restore broader subscription access later @ClaudeDevs, @scaling01, @ArtificialAnlys, @kimmonismus

• Confusion over the temporary inclusion was immediate; users asked what “included until June 22” meant and Anthropic staff clarified the rollout @dejavucoder, @TheAmolAvasare

• Anthropic later reset 5-hour and weekly rate limits across products after heavy demand @ClaudeDevs

Official claims and third-party benchmark data

Anthropic and partner platforms reported a broad benchmark lead, especially in coding and long-horizon agentic tasks.

• Anthropic’s public claim: Fable 5 is especially strong in software engineering, knowledge work, scientific research, and vision, and its lead increases with task length and complexity @claudeai

• Cursor said Fable 5 set a new CursorBench SOTA at 72.9%, 8 points above the previous best @cursor_ai

• Cognition said Fable 5 took the #1 spot on FrontierCode, and Devin integrated it into Devin Cloud Ultra, Desktop, and CLI @cognition, @cognition

• Cline reported Fable 5 at 88.0% on Terminal-Bench 2.1, beating GPT-5.5 by 4.6 points @cline

• Artificial Analysis placed Fable 5 #1 on its Intelligence Index at 64.9, roughly 5 points ahead of GPT-5.5, and said Anthropic occupied the top two spots @ArtificialAnlys

• Artificial Analysis also reported:

GDPval-AA Elo 1932, #1 on agentic real-world knowledge work @ArtificialAnlys

• 53% on Humanity’s Last Exam, more than 7 points ahead of the next-best model, while fallback triggered on 9% of HLE tasks @ArtificialAnlys

• ~8% fallback routing across Intelligence Index tasks, mostly on scientific questions @ArtificialAnlys

• Anthropic stated fallback occurs in fewer than 5% of sessions on average @ArtificialAnlys

• Community benchmark summaries highlighted very large deltas in coding:

SWE-Bench Pro: Fable 5 80.3% vs GPT-5.5 58.6% @Yuchenj_UW

• FrontierCode Diamond: Mythos 5 30.9% vs second-best 13.4% @scaling01

• Anthropic ECI 161.29 for Mythos 5 @scaling01

• Artificial Analysis noted that Fable 5’s knowledge benchmark jump on AA-Omniscience could imply a larger model than prior public Anthropic models, though that is inference rather than confirmed spec @ArtificialAnlys

Product behavior, usage profile, and deployment details

The release was defined as much by workflow changes and cost profile as by raw evals.

• Anthropic staff and early users repeatedly described Fable 5 as a model for very long, high-effort tasks, with users shifting from giving it tasks to giving it objectives/responsibilities @felixrieseberg, @ClaudeDevs, @alexalbert__

• Anthropic advised users to default to xhigh/high effort, rewrite old CLAUDE.md instructions, and let the model use more judgment @alexalbert__

• Anthropic’s developer messaging emphasized multi-agent orchestration, with Fable delegating to smaller models in Claude Managed Agents @ClaudeDevs

• Multiple testers described Fable as slow, token-hungry, expensive, but unusually capable:

Dan Shipper said it routinely used 500k to 1M tokens on tasks and was best reserved for heavy jobs @danshipper

• Simon Willison called it “slow, expensive and capable” @simonw

• Theo quickly hit limits and later welcomed Anthropic’s rate-limit reset @theo, @ClaudeDevs

• Third-party and internal anecdotes emphasized large gains on long-running engineering tasks:

Ethan Mollick said he could hand it a 15-page design document and it would work for 9+ hours @emollick

• Kimmonismus highlighted Anthropic’s claim that Stripe used Fable to do a 50-million-line Ruby migration in a day, replacing what would have taken a whole team over two months @kimmonismus

• Victor Taelin reported Fable finding a subtle bug and producing claimed speedups up to 1770% in one case, though he still needed to audit correctness @VictorTaelin

• Anthropic-associated posts cited 430x kernel speedups, 69x self-training speedups, and 10x drug-design acceleration, though these came from benchmark/system-card interpretations and should be treated as vendor-side claims unless independently replicated @scaling01, @scaling01, @scaling01

• Ecosystem rollout was immediate: Fable 5 appeared in Cursor, Devin, Notion, Microsoft Foundry, GitHub Copilot App/CLI, Cline, Replit, Base44, MagicPath, Arena, MCP Atlas and more @cursor_ai, @cognition, @NotionHQ, @Azure, @pierceboggan, @cline, @pirroh, @ScaleAILabs

Safety architecture and the main controversy

The biggest debate was not whether Fable/Mythos is strong; it was Anthropic’s decision to silently reduce usefulness on some frontier-AI-development tasks.

• Anthropic’s system-card language, surfaced by multiple users, said: when Fable 5 is used for frontier LLM development, Anthropic may limit the model’s effectiveness via prompt modification, steering vectors, and PEFT, and that the user is not notified; Anthropic estimated this would affect roughly 0.03% of traffic @Hangsiin, @kimmonismus

• Anthropic also separately disclosed auto-rerouting for cybersecurity and biosecurity requests to Opus 4.8 @ClaudeDevs

• This distinction mattered: some risky queries are visibly rerouted/billed as Opus, while frontier-LLM-development requests may be silently weakened rather than rerouted or refused

• Critics argued that this creates an unlogged confounder in research and engineering workflows:

“silent handicaps should not be a thing in a paid product” @nrehiew_

• “degrading performance on ML research without telling the user is shockingly hostile” @deanwball

• “safety interventions must be visible, auditable, and attributable” @MattGibsonMusic

• “this is the trust break” because every poor result becomes ambiguous @MattGibsonMusic

• Several researchers framed it as anti-competitive ladder-pulling against open research and open weights:

“labs starting to pull up the ladders” @natolambert

• “this is the biggest wake-up call to protect and nourish open source AI” @rasdani_

• “They didn’t mean pause AI research, they meant pause your AI research” @bayeslord

• “original thinkers can't be an underclass” @marksaroufim

• “concentration of power, capabilities and economic wealth is the biggest risk in AI” @ClementDelangue

• Multiple users worried the classifier boundary was too broad or too error-prone:

one user said “the word cancer is flagged as a biosecurity risk” @DeryaTR_

• another said Fable wouldn’t answer “What does the heart do?” @Yuchenj_UW

• users in biology reported account-context differences, including being able to use Fable in Incognito Mode but not normal mode @cremieuxrecueil

• Teknium and others reported refusal on simple engineering prompts @Teknium, @Teknium

• users reported PTX ISA questions and inference optimization queries getting flagged @snowclipsed, @dejavucoder

• Some examples were humorous but pointed: users joked that asking for inference code caused the model to “start importing ONNX” or implementing JEPA, as a sign of capability steering @vikhyatk, @MattVMacfarlane

Facts vs. opinions

Facts / directly supported by release materials or benchmark posts

• Fable 5 is generally available; Mythos 5 is restricted-access @claudeai, @TheRundownAI

• Fable 5 and Mythos 5 share the same underlying model with additional safeguards on Fable @ClaudeDevs, @scaling01

• Pricing is $10 / $50 per million input/output tokens @scaling01, @ArtificialAnlys

• Fable retains 1M context @ArtificialAnlys

• Anthropic introduced refusal/fallback mechanisms and SDK middleware @ClaudeDevs

• Anthropic disclosed silent interventions for frontier LLM development affecting about 0.03% of traffic @Hangsiin

• Fable is temporarily included in subscriptions until June 22, then credit-based @ArtificialAnlys

Opinions / interpretations

• “Anthropic won,” “Anthropic has a coding moat,” “Anthropic going for ASI” are commentary rather than verified fact @scaling01, @scaling01, @scaling01

• Claims that the move is primarily for IPO optics, anti-open-source positioning, or specifically to slow Meta/China/open labs are plausible interpretations but not confirmed by Anthropic @kimmonismus, @kylebrussell, @natolambert

• Claims that Anthropic is acting from sincere safety beliefs rather than cynical moat-building are also interpretive @finbarrtimbers

• Subjective reports like “GPT-4 moment,” “big model smell,” “strictly dominates me as an engineer,” or “doesn’t seem much better to normal users” are experiential, not standardized evidence @karinanguyen, @bcherny, @akbirkhan, @citrini

Different perspectives

Supportive / capability-first

• Anthropic staff and close testers described Fable 5 as a step-function improvement:

Felix Rieseberg: shift from giving AI tasks to giving it responsibilities @felixrieseberg

• Alex Albert: model feels collaborative rather than tool-like @alexalbert__

• Karpathy: a “major-version-bump-deserving step change,” especially on long difficult tasks, though safeguards are “a little too trigger happy for launch” @karpathy

• Bcherny: biggest step since Opus 4.5; the model shows judgment, taste, methodical debugging @bcherny

Third-party infra and app vendors emphasized benchmark win