Claude Fable 5
- Lab
- anthropic
- Weights
- closed
- Released
- 2026-08-25
- Context window
- 1,000,000
- Modalities
- text, image
- API
- yes
- Local-runnable
- no
- Status
- current
announcementmodel cardpricing
Scores
Human preference
| Value | Source | As of | Reported by | Notes |
| 1507 elo | lmarena-text ↗ | 2026-08-31 | aggregatorA | Still #1 on the text overall board; 1508 on the 2026-08-26 fetch. |
| 1508 elo | lmarena-text ↗ | 2026-08-26 | aggregatorA | Top of the text board in the 2026-08-26 fetch; other axes pending weekly runs. |
Events
3 Simon Willison reported on September 2, 2026 that Anthropic's published system prompt for Claude Fable 5.1 added a substantial new section instructing Claude not to reproduce song lyrics, poems, or book/article passages — including choruses or paraphrased lines — and to keep declining reworded requests for the rest of a conversation; a parallel new section forbids generating images of copyrighted characters, logos, or artwork. Willison notes this closely follows news that Sony Music Publishing and Warner Chappell are suing Anthropic over training on databases of song lyrics, though he does not cite a primary source for the lawsuit itself.
4 On September 1, 2026, Anthropic released Claude Fable 5.1 and Claude Mythos 5.1, positioned for long-running agentic coding, knowledge work, and research, both with a 1M-token context window, 128k max output tokens, and always-on adaptive thinking. List pricing stays at $10/$50 per MTok input/output (same as Fable 5), but prompt cache read price drops 75% to $0.25/MTok. Anthropic also introduced Enterprise Frontier Safeguards (EFS) for agent observability. Per Stratechery, Fable's prior data retention policy was removed rather than merely altered. Anthropic's own benchmark table reports Fable 5.1 scoring 52.6% on the new Terminal-Bench-Science 0.1 benchmark versus 24.7% for Fable 5, 29.0% for Opus 5, and 22.4% for GPT-5.6 Sol. TechCrunch reported the release also reduces false-positive safeguard restrictions. Per Latent Space's AINews recap, Artificial Analysis measured roughly 1.7x more output tokens per task, offsetting the cache savings for a reported ~20% net per-task cost increase.
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