Anthropic on Tuesday, June 9, 2026, brought its most powerful AI model to the general public for the first time. Named Claude Fable 5, the model is the first publicly available version of the company's restricted Mythos tier, which sits above the Opus class in capability. Anthropic said Fable 5 leads on nearly every tested benchmark, with particular strength in software engineering, knowledge work, vision, and scientific research, and that its advantage grows as tasks become longer and more complex.
The model's defining feature is the set of hard safety limits that accompany its capabilities. In high-risk areas such as cybersecurity, biology, chemistry, and model distillation, Fable 5 blocks responses and falls back to the more constrained Claude Opus 4.8. Anthropic said it stress-tested the safety classifiers through a bug bounty exceeding 1,000 hours and external red-teaming, neither of which produced a universal jailbreak. Pricing for both Fable 5 and Mythos 5 is set at $10 per million input tokens and $50 per million output tokens — double the cost of Opus 4.8.
Mythos first launched as a preview in April for a handful of partners, and last week access was expanded to hundreds of organizations managing critical infrastructure across 15 countries. The release follows Anthropic's preparations for a public listing and its call for major AI labs to establish a coordinated "brake pedal" on frontier development, after warning that systems may soon reach recursive self-improvement without human intervention. Anthropic also introduced a mandatory 30-day data-retention requirement for all
Fable 5 and Mythos 5 traffic, even for enterprises that previously held zero-retention agreements.
Access will roll out in stages: through June 22, Fable 5 is included in Pro, Max, Team, and seat-based Enterprise plans at no extra cost, before being pulled on June 23 in favor of usage credits. In third-party testing, analytics firm Hex said Fable was the first model to score 90% on its core benchmark of complex, long-running analytical tasks. Still, the high price point may deter widespread adoption, as many enterprises grow increasingly critical of mounting AI costs.