Claude Fable 5.1 and Mythos 5.1: Same Model, Different Safeguards

✍️ OpenClawRadar📅 Published: September 2, 2026🔗 Source
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Anthropic just released Claude Fable 5.1 and Claude Mythos 5.1 — the same underlying model, but with different safety levels. Fable 5.1 is generally available; Mythos 5.1 is limited to trusted access programs for cybersecurity and life sciences.

Pricing and data retention

Fable 5.1 costs about 25% less than Fable 5 for typical token-billed workloads, thanks to reduced pricing on cache reads (inputs already processed and stored). For highly agentic work, savings can reach ~45%.

Enterprise Frontier Safeguards (EFS) give customers complete privacy — data stored in customer-controlled cloud infrastructure, not Anthropic's. EFS rolls out in phases starting fall 2025. Until then, eligible customers get zero data retention on Fable 5.1.

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Benchmarks

Fable 5.1 shows gains across the board. Key numbers from the source:

  • Terminal-Bench-Science 0.1: Fable 5.1 reaches ~50% accuracy vs ~25% for Fable 5, at similar cost.
  • Terminal-Bench 4.0: At each effort level (low/med/high/xhigh/max), Fable 5.1 outperforms Fable 5. The gap between Fable and Mythos is expected to shrink with improved cyber safeguards.
  • Humanity's Last Exam: Fable 5.1 with tools shows ~65% pass rate at max effort, before hitting the cost ceiling.
  • CursorBench 3.2.0: Fable 5.1 scores ~72% at high effort, vs ~65% for Fable 5.

Fable 5.1 defaults to High effort in Claude Code, Medium in Claude Cowork and on Claude.ai. Lower effort settings yield comparable results to Fable 5 at lower cost.

Safeguard improvements

False positives (benign content flagged) drop 60% in cybersecurity. The model can now discover software vulnerabilities but not develop exploits. The biology side has an access program built with the US government — scientist enrollment opens soon.

Real-world impact

Investment firm Millennium reported Fable 5.1 found the root cause of a rare crash in their internal systems that engineers and other models couldn't explain after years of tries.

📖 Read the full source: HN AI Agents

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👀 See Also