Anthropic's Claude Fable 5 Launched and Got Pulled by the US Government

Anthropic launched Claude Fable 5 as the first publicly available model from its top-tier Mythos class , a capability level that, until now, had only been accessible to a small group of vetted government cyber defenders. The launch was historic. The aftermath was even more dramatic. Three days after release, the US government ordered Anthropic to pull both Fable 5 and its restricted sibling, Mythos 5, from every customer worldwide, citing national security concerns. The story of this model is as much about what it can do as it is about who gets to use it.

A new tier above Opus

To understand the significance of Fable 5, you need to understand Anthropic's model hierarchy. Fable 5 is the first publicly available Mythos-tier model, positioned above the Opus class , until now, the strongest Claude you could buy was Opus 4.8; the ceiling just moved up a level. The Mythos class was first introduced quietly in April through Project Glasswing, a collaboration with the US government where only vetted cyber defenders could access it.

Fable 5's capabilities exceed those of any model Anthropic has ever made generally available, and it is state-of-the-art on nearly all tested benchmarks, showing exceptional performance in software engineering, knowledge work, vision, scientific research, and many other areas. The key insight: the longer and more complex the task, the larger Fable 5's lead over other models. This isn't a model optimized for quick Q&A , it's built for sustained, autonomous work.

Benchmark numbers that matter

Fable 5 leads the major models on agentic coding benchmarks, scoring 80.3% on SWE-bench Pro against 58.6% for GPT-5.5, and 29.3% on Cognition's FrontierCode Diamond against 13.4% for Claude Opus. On the Artificial Analysis Intelligence Index, Fable 5 scored 65, ahead of OpenAI's GPT-5.5 at 60 and Google's Gemini 3.1 Pro Preview at 57.

Real-world early access results back up the numbers:

• Stripe reported Fable 5 compressed months of engineering into days, completing a codebase-wide migration across a 50-million-line Ruby codebase in a single day that would have taken a full team over two months.