anthropic
Anthropic: Claude Fable 5 (batch)
anthropic/claude-fable-5:batch
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 in its :batch variant, listed in the OpenRouter catalog as a Mythos-class model aimed at autonomous knowledge work and coding. It accepts text, image, and file inputs and returns text, with a cataloged context window of one million tokens. Reasoning is mandatory with adjustable effort levels, and tool calling plus structured outputs are supported. The only evidence is the OpenRouter catalog; no independent model card was available in this source pack, so capabilities and measured results should be verified on real workloads.[1]
Strengths
- Cataloged one-million-token context window with multimodal text, image, and file inputs, suitable on paper for large documents and codebases.[1]
- Reasoning effort adjustable across five levels, alongside cataloged support for tools, structured outputs, response formatting, and a verbosity parameter.[1]
- Catalog-reported arena standings are competitive across several agentic coding and design categories, and Artificial Analysis data in the catalog shows a high coding index relative to its agentic index.[1]
Limitations
- All evidence comes from the OpenRouter catalog alone; no independent model card was available in this source pack, so behavior should be validated on your own workloads before adoption.[1]
- The catalog marks reasoning as mandatory, so it cannot be fully disabled; resulting cost and latency under different effort settings are not documented here.[1]
- Knowledge cutoff, licensing terms, and what distinguishes the :batch variant from the base model are not specified in the supplied evidence.[1]
Where to use it
- Suggested for autonomous coding and knowledge-work pipelines where a very large context window and tool use matter; treat as a hypothesis to test, not a proven fit.[1]
- Worth evaluating for document, image, and file analysis that must produce text output, given the cataloged input modalities.[1]
- A candidate for agentic design and prototyping tasks, consistent with its catalog-reported arena standings, pending workload validation.[1]
Sources
- [1] OpenRouter catalog · Anthropic: Claude Fable 5 (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and...
Specifications
- Model ID
- anthropic/claude-fable-5:batch
- Canonical ID
- anthropic/claude-5-fable-20260609
- Creator
- anthropic
- Added to OpenRouter
- 09.06.2026
- 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 $5 / out $25
- 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 | No measurement | 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 | Coding index | 76.5 | SS — top 10% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 50.7 | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 62% · rank 2 | 1,233Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · agentichtmlslideswin rate 59.4% · rank 1 | 1,254Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(html)win rate 59.5% · rank 1 | 1,252Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 65.3% · rank 4 | 1,276Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 58.2% · rank 8 | 1,258Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 64.3% · rank 2 | 1,274Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 63.1% · rank 4 | 1,173Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 60.5% · rank 6 | 1,257Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 61.4% · rank 5 | 1,254Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | agents · webappswin rate 56.3% · rank 13 | 1,243Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 61.6% · rank 18 | 1,300Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 67.9% · rank 4 | 1,321Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 57.9% · rank 16 | 1,302Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 57.8% · rank 12 | 1,304Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 60.8% · rank 12 | 1,326Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 62.8% · rank 8 | 1,294Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 58.2% · rank 16 | 1,310Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 56.3% · rank 21 | 1,289Elo | AA — next 20% 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) |
|---|---|---|---|---|---|---|
| Anthropic | 1,000,000 | $5 | $25 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.