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Field Guide

Claude Fable 5: The 5-Minute Field Guide

What Anthropic's new top-tier model is, when it's worth the money, and how to switch without breaking your code.

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01

What Fable 5 actually is

Fable 5 (claude-fable-5, codenamed Mythos) is Anthropic's most capable model — a new tier that sits above Opus. Same 1M-token context as Opus 4.8, same 128K max output, but it's tuned for the hardest work: deep research, complex judgment, and large multi-step projects.

  • Model idclaude-fable-5
  • Context / output — 1M tokens in · up to 128K out (stream for big outputs)
  • Price — $10 / 1M input · $50 / 1M output — 2× Opus 4.8
02

Is it actually better? (and the catch)

Early hands-on reviews put it in a different class for senior-level engineering. In Every's week-long test it scored 91/100 on their senior-engineer benchmark — vs 63 for Opus 4.8 and 62 for GPT-5.5.

  • Reach for it: deep research, architecture, large refactors, tricky judgment calls.
  • Skip it: chat, classification, simple edits, high-volume jobs — cheaper models win on speed + cost.
The catch: it's slow and expensive. Reviewers agree it shines on big, ambiguous problems — and is overkill (and overpriced) for quick, well-defined tasks. Treat it as your specialist, not your default.
03

The model picker — which Claude for the job

You rarely want one model for everything. Match the model to the task and you cut cost without losing quality.

ModelBest forContext$/1M in·out
Fable 5Hardest research, judgment, big projects1M$10 · $50
Opus 4.8Strong default for agentic + coding1M$5 · $25
Sonnet 4.6Best speed/intelligence balance1M$3 · $15
Haiku 4.5Fast, cheap, simple tasks200K$1 · $5
04

Switch to Fable 5 without getting a 400

Fable 5 shares Opus 4.8's request surface, with a few parameters removed. Update these and you're done:

  1. Set the model string to claude-fable-5.
  2. Use adaptive thinking: thinking: {type: "adaptive"}. The old budget_tokens is gone (returns 400).
  3. Delete temperature, top_p, top_k — all removed. Steer with the prompt instead.
  4. Fable-5 quirk: do not send thinking: {type: "disabled"} (400). To run without thinking, just omit the thinking field entirely.
  5. Tune output_config: {effort: ...}lowmax (incl. xhigh). Start at high; reserve max for the hardest jobs.
  6. Streaming for large outputs (>~16K tokens) to dodge HTTP timeouts.
No last-assistant-turn prefills (use output_config.format for structured output). Cacheable-prefix minimum is 2048 tokens.
05

5 ways to put it to work today

  • Research analyst — Dump 10+ sources in the 1M window; ask for a cited, decision-ready brief.
  • Architecture partner — Hand it the whole task spec up front; let it plan the build at high effort.
  • Large refactor — Point it at a big codebase and a clear goal — it holds long-horizon context well.
  • Judgment calls — Use it where being right matters more than being cheap (contracts, tradeoffs, reviews).
  • Route, don't default — Send only the hard 10% to Fable 5; everything else to Sonnet/Haiku.

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Sources · Anthropic — Claude model docs (ids, pricing, params) · Every — "We Tested Anthropic's Fable 5 for a Week" (Dan Shipper) · Alex Finn — "Claude Fable 5 just dropped" · Nate Herk — "Claude Mythos is Finally Here"

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