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Claude Fable 5 review: what the new Mythos model gets right (and very wrong)

How I AI

13 HOURS AGO
How I AI

How I AI

13 HOURS AGO
Claire Vo shares her early access impressions of Anthropic's new Claude Fable 5 model, the first Mythos-class intelligence model to be generally available. She walks through what the model promises, how it performed on real-world tasks, and where it fits in your AI stack.
Claude Fable 5 is a state-of-the-art, token-intensive model that excels at complex, long-running autonomous tasks and vision, but it is expensive and consumes tokens at twice the rate. While it crushes benchmarks like SWBench Pro, its output can be overly detailed and hard to parse, making it less suitable for specs or PRDs. The model performed poorly on one-shot design tasks and was too conservative in execution, producing minimal MVPs. It showed mixed results in multi-agent orchestration, with stalls and errors. Claire recommends using lower-tier models like Sonnet or Opus for design and spec writing, reserving Fable 5 for hard technical problems, long-horizon work, and vision tasks like PDF parsing. The model has a place in your stack, but users should consult the prompting guide for best results.
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Claude Fable 5 (Mythos) early access impressions
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Claude Fable 5 is state-of-the-art, expensive, and excels at benchmarks.
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Token intensity does not guarantee correct results
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95% of sessions did not hit a fallback
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Fable includes safeguards and is generally available.
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Fable 5 as a senior advisor with cheaper execution models
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Claude Fable 5 significantly outperforms other models
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Claude Fable 5 excels at vision and formatting.
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Output detailed but nearly impossible to parse
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Claude Fable 5 produced terrible design results
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Execution too conservative, producing minimal MVPs.
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Multi-agent orchestration had both successes and failures
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Excels at hard technical problems and vision tasks