Claude Fable 5 is Anthropic's most capable widely released model, built for the most demanding reasoning and long-horizon agentic work. Claude Mythos 5 shares the same capabilities without the safety classifiers and is available only in limited release through Project Glasswing.
| Model | API model ID | Description |
|---|---|---|
| Claude Fable 5 | claude-fable-5 | Anthropic's most capable widely released model, for the most demanding reasoning and long-horizon agentic work |
| Claude Mythos 5 | claude-mythos-5 | Shares Claude Fable 5's capabilities without the safety classifiers. Available through Project Glasswing. Successor to Claude Mythos Preview. |
Claude Fable 5 and Claude Mythos 5 support a 1M token context window by default and up to 128k output tokens per request.
Claude Fable 5 and Claude Mythos 5 are priced at $10 per million input tokens and $50 per million output tokens. For specs across all current models, see the models overview.
Claude Fable 5 includes safety classifiers that can decline certain requests. The sections below summarize what that means for your integration; each links to the full guide.
When Claude Fable 5 declines a request, the Messages API returns stop_reason: "refusal" as a successful HTTP 200 response, not an error. The response also reports which classifier declined the request. See Refusals and fallback for response shapes and handling guidance.
A request that Claude Fable 5 refuses can usually be served by another Claude model. Pass the fallbacks parameter to have the API retry for you (in beta on the Claude API and Claude Platform on AWS), or use the SDK middleware (TypeScript, Python, Go, Java, and C#) to retry from the client on any platform. See Server-side fallback, client-side fallback, and SDK middleware.
You are not billed for a request that is refused before any output is generated. When you retry on another model, fallback credit refunds the prompt-cache cost of switching. See Fallback credit.
Claude Fable 5 and Claude Mythos 5 both become available on June 9, 2026:
Claude Fable 5 and Claude Mythos 5 are designated Covered Models, which carry 30-day data retention and are not available under zero data retention. See Model-specific data retention requirements.
Claude Fable 5 responds to the same prompting techniques as other Claude models, with a few differences in how to structure long-context prompts and reasoning instructions. See Prompting Claude Fable 5.
The behaviors in this section are specific to Claude Fable 5 and Claude Mythos 5. The Messages API is unchanged for Opus, Sonnet, and Haiku models.
Adaptive thinking is the only thinking mode on Claude Fable 5 and Claude Mythos 5. It applies whenever the thinking parameter is unset, and thinking: {"type": "disabled"} is not supported. Use the effort parameter to control thinking depth.
The raw chain of thought is never returned on Claude Fable 5 and Claude Mythos 5. thinking.display defaults to "omitted", which returns thinking blocks with an empty thinking field; set display: "summarized" to receive readable summarized thinking. Pass thinking blocks back unchanged in multi-turn conversations on the same model. See thinking output on Claude Fable 5 and Claude Mythos 5 for cross-model handling.
At launch, Claude Fable 5 and Claude Mythos 5 support:
task-budgets-2026-03-13 header)context-management-2025-06-27 header)If you're migrating from Claude Mythos Preview, see Migrating from Claude Mythos Preview to Claude Mythos 5 for step-by-step instructions.
If you're migrating from Claude Opus 4.8, see Migrating from Claude Opus 4.8 to Claude Fable 5.
Step-by-step upgrade instructions from Claude Opus 4.8 and Claude Mythos Preview.
Specs and comparison for all current Claude models.
The only thinking mode on Claude Fable 5 and Claude Mythos 5.
How Claude Fable 5 declines requests, and how to retry on another model.
Avoid paying the prompt-cache cost twice on a retry.
A worked end-to-end example of refusal handling, fallback, and billing.
Control thinking depth and cost on Claude Fable 5 and Claude Mythos 5.
Fable-specific prompting techniques.
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