# claude-fable-5-1 decision evidence

Claude Fable 5.1 is Anthropic's most capable widely released model: closed-weight, released on 2026-09-01, with a 1M-token context window, 128K output tokens and text and image input. Anthropic's model guide starts most workloads on Opus 5.5 and reserves Fable for the highest capability; in the reviewed tests it took 40 minutes and $17 on one front-end prompt, placed last of three models on two real robot tasks, and ranked sixth on AI Coding Daily's coding leaderboard, below Claude Opus 5.5 and GPT-6 Astra.

Verified: 2026-09-24; snapshot: 2026-09-25.

## Published model card

As the vendor publishes it for the model and its weights; these are not the limits of a hosted endpoint.

- Context window: 1M tokens
- Maximum output: 128K tokens
- Modalities: Text and image input; text output
- Parameter count: Not disclosed

## Open weights and deployment

Status: Unavailable. Anthropic publishes no Claude Fable 5.1 weights. The model is available on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS; Claude Mythos 5.1, the same model with different safeguards, is limited to Project Glasswing participants.

Open-weight alternatives: [deepseek-flash](https://dash.chinaapi.ai/models/deepseek-flash/deploy), [glm-5.3-flash](https://dash.chinaapi.ai/models/glm-5.3-flash/deploy)

## Real-task reports

### [Step 5 Preview and Claude Fable 5.1 on one front-end prompt](https://x.com/notjazii/status/2101653125623152714)

By [J A Z I I](https://x.com/notjazii) (@notjazii). X account that tests AI models and covers AI news

Published: 2026-09-20; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Build a front-end page from a single prompt
- Method: Same prompt at the highest available reasoning setting for both models; the author reported time and cost and repeated the Step 5 run without skills.
- Finding: Claude Fable 5.1 took 40 minutes and $17 on the front-end prompt that Step 5 Preview finished in 10 minutes for about $0.50; the author left the quality comparison to readers.
- Limitation: One prompt; the post leaves the quality comparison to readers rather than scoring it, and the cost gap follows each vendor's list prices.
- Claude Fable 5.1: 40 min · $17
- Step 5 Preview: 10 min · $0.50

### [Cortex AI: GPT-6 Astra, Gemini 3.8 Flash and Fable 5.1 on two real robot tasks](https://cortexrobot.ai/research/llm-real-world-robotics-evals/)

By [Cortex AI Research](https://cortexrobot.ai/) (@cortexairobot). Robotics team that builds real-world robot datasets and runs the MolmoAct2 real-world evaluations

Published: 2026-09-17; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Load a sandwich into a microwave and close the door; attach a 5 mm tip to a pipette
- Method: Each model controlled a bimanual YAM robot through an overhead and two wrist cameras with depth images, at medium reasoning effort with up to 10 minutes per task; an evaluator scored each attempt with partial credit for completed steps.
- Finding: Fable 5.1 scored 6.7% on the microwave task and 20% on the pipette task, behind GPT-6 Astra (80% and 70%) and Gemini 3.8 Flash (13.3% and 30%).
- Limitation: The post does not say how many attempts each model had; scores are partial-credit judgements by the lab's evaluator, and the authors attribute the gap mainly to weaker depth perception in the lower-scoring models.
- Microwave: 6.7%
- Pipette: 20%

### [AI Coding Daily LLM Coding Leaderboard](https://aicodingdaily.com/leaderboard)

By [AI Coding Daily](https://www.youtube.com/@AICodingDaily) (@AICodingDaily). Coding-model testing channel and website that runs its own leaderboard and sells premium tutorials and channel advertising

Published: 2026-09-24; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Implement and harden production-shaped projects in several stacks, including a Laravel app, a Go service and a React/TypeScript app, from the same prompts
- Method: Each model and reasoning setting is a separate row run in a coding harness, with the average cost and time per run. Code quality is graded by a judge model against a published 100-point rubric and scaled to 20 points per project; behavioural reliability runs each of four projects five times and scores the checks every attempt fails.
- Finding: Claude Fable 5.1 at medium reasoning scored 53.63 ($1.53 and 3 min 6 s per run in Claude Code), sixth of 47 rows, behind Claude Opus 5.5 and GPT-6 Astra.
- Limitation: One author's benchmark whose code-quality half is graded by a judge model; rows run in different harnesses, the page is updated as models are added (read on 2026-09-24, 47 rows), and the author sells premium tutorials and advertising.
- Points (medium): 53.63
- Rank: 6 of 47

## Public leaderboard coverage

A board missing from this list has no figure for this model in this snapshot; missing coverage is not a zero.

- LMArena Text Arena — Arena score (Elo): 1501 ±6 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text)
- LiveBench — Overall: 83.4 (retrieved 2026-10-04) [source](https://livebench.ai/)
- Artificial Analysis Intelligence Index — Intelligence Index: 53 (retrieved 2026-09-21) [source](https://artificialanalysis.ai/leaderboards/models)
- Epoch Capabilities Index (ECI) — General ECI: 165 (162 - 169) (retrieved 2026-10-04) [source](https://epoch.ai/eci)
- Vals Index — Accuracy (GDP-weighted finance, coding, legal and tax): 65.83 ±1.11 (retrieved 2026-10-04) [source](https://www.vals.ai/benchmarks/vals_index)
- LMArena Text Arena · Chinese — Arena score (Elo): 1570 ±21 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text/chinese)
- LMArena Text Arena · Coding — Arena score (Elo): 1531 ±13 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text/coding)
- LiveBench · Coding — Coding: 86.4 (retrieved 2026-10-04) [source](https://livebench.ai/)
- LiveBench · Agentic Coding — Agentic Coding: 66.1 (retrieved 2026-10-04) [source](https://livebench.ai/)
- Artificial Analysis · Output Speed — Median output tokens/s: 65 (retrieved 2026-09-22) [source](https://artificialanalysis.ai/leaderboards/models)
- Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%): 62 (retrieved 2026-09-21) [source](https://artificialanalysis.ai/leaderboards/models)
- Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%): 52 (retrieved 2026-09-21) [source](https://artificialanalysis.ai/leaderboards/models)
- Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%): 71.1 (retrieved 2026-09-07) [source](https://artificialanalysis.ai/evaluations/mlcr-aa)

## Curated comparisons

- [claude-fable-5-1 vs gemini-3.8-flash](https://dash.chinaapi.ai/models/compare/claude-fable-5-1-vs-gemini-3.8-flash)
- [claude-fable-5-1 vs gpt-6-astra](https://dash.chinaapi.ai/models/compare/claude-fable-5-1-vs-gpt-6-astra)
- [claude-fable-5-1 vs step-5-preview](https://dash.chinaapi.ai/models/compare/claude-fable-5-1-vs-step-5-preview)

## Official sources

- [Introducing Claude Fable 5.1 and Claude Mythos 5.1](https://www.anthropic.com/claude-fable-and-mythos-5-1) — Anthropic, retrieved 2026-09-24
- [Claude Fable 5.1 model documentation](https://platform.claude.com/docs/en/models/fable-5-1/overview) — Anthropic, retrieved 2026-09-24
- [Choosing a model](https://platform.claude.com/docs/en/about-claude/models/choosing-a-model) — Anthropic, retrieved 2026-09-24
- [System Card: Claude Fable 5.1 & Claude Mythos 5.1](https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card) — Anthropic, retrieved 2026-09-24