anthropic
Anthropic: Claude Fable 5.1
anthropic/claude-fable-5.1
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
Anthropic's Claude Fable 5.1, listed in the OpenRouter catalog, is a text-output model that accepts text, image, and file inputs across a very large context window. Reasoning is mandatory, with several selectable effort levels, and the listing reports support for tool use, structured outputs, and response formatting. The catalog describes it as an update focused on agentic coding, long-running workflows, and knowledge work, with benchmark entries covering agent-oriented tasks. No independent model card was available in this source pack, so claims rest on catalog data.[1]
Strengths
- According to the OpenRouter catalog, it accepts text, image, and file inputs while producing text output, suiting multimodal analysis workflows.[1]
- The catalog reports a million-token context window and mandatory reasoning with several effort levels adjustable per request.[1]
- Listed parameters include tool calling, tool choice, structured outputs, and configurable reasoning effort.[1]
Limitations
- Evidence comes solely from the OpenRouter catalog; no independent model card was in this source pack, so capabilities and results need validation on real workloads.[1]
- Reasoning is mandatory per the catalog, which may add latency or cost where lighter behavior is preferred.[1]
- The catalog leaves knowledge cutoff and licensing unspecified, and the vendor description is truncated, so some details remain unknown.[1]
Where to use it
- Agentic coding and long-running development workflows, which the catalog description names as its biggest improvement areas.[1]
- Document- and image-grounded tasks that need text answers, given the reported multimodal inputs.[1]
- Long-context knowledge work such as large refactors or multi-file analysis; an editorial suggestion, not a verified result.[1]
Sources
- [1] OpenRouter catalog · Anthropic: Claude Fable 5.1Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual...
Specifications
- Model ID
- anthropic/claude-fable-5.1
- Canonical ID
- anthropic/claude-fable-5.1-20260831
- Creator
- anthropic
- Added to OpenRouter
- 01.09.2026
- Context length
- 1,000,000
- Modality
- text+image+file->text
- Input modalities
- text, image, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $10 / out $50
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | AA — next 20% of the cohort | reasoning: mandatory · default high · efforts: max, xhigh, high, medium, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | SS — top 10% of the cohort | — |
| Agents | SS — top 10% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default high · efforts: max, xhigh, high, medium, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 50 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Coding index | 80.7 | SS — top 10% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 57.2 | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 57.1% · rank 3 | 1,304Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 60.7% · rank 1 | 1,307Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 56.5% · rank 1 | 1,394Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 56.3% · rank 7 | 1,257Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 60.8% · rank 1 | 1,308Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 70.4% · rank 1 | 1,426Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · webappswin rate 58.2% · rank 2 | 1,316Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 63.1% · rank 8 | 1,391Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 65% · rank 5 | 1,318Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 57.5% · rank 9 | 1,331Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 62.4% · rank 5 | 1,346Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 59.5% · rank 7 | 1,366Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 56.6% · rank 9 | 1,294Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 59.9% · rank 13 | 1,333Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 55.2% · rank 13 | 1,306Elo | SS — top 10% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Azure | 1,000,000 | $10 | $50 | — | — | 100% |
| Anthropic | 1,000,000 | $10 | $50 | — | — | 100% |
| Amazon Bedrock | 1,000,000 | $10 | $50 | — | — | — |
| 1,000,000 | $10 | $50 | — | — | 100% | |
| 1,000,000 | $11 | $55 | — | — | — |
Provider data retrieved: 13.11.2026