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Anthropic's frontier reasoning model with a 1M-token context window, always-on adaptive reasoning, and native text-and-image input.
Overview. Claude Fable 5.1 is Anthropic's proprietary, closed-weight frontier reasoning model, released as an incremental successor to Claude Fable 5. It pairs a 1,000,000-token context window with mandatory adaptive reasoning that scales from low to max effort on every request, and accepts text and image input while producing text-only output. Developers reach for it when a task needs deep multi-step reasoning over very large inputs rather than a fast, low-effort response.
Key strengths. Artificial Analysis places Claude Fable 5.1 in the frontier quality tier, with an Intelligence Index of 65.7 at max effort and a Coding Index of 81.6, indicating particular strength on code-generation and code-reasoning tasks. Its Agentic Index of 61.3 supports multi-step tool-use and planning workloads. The model's always-on reasoning process (mandatory, not optional) is the defining trait behind these scores.
When to use it. Use Claude Fable 5.1 for complex coding tasks, multi-step agentic workflows, and analysis over very large documents or codebases that benefit from the 1,000,000-token context window. Its native image input also makes it suitable for tasks that combine visual and text reasoning, such as interpreting diagrams or screenshots alongside written instructions. Because reasoning effort is adjustable from low to max, it can be tuned for either deeper analysis or faster turnaround within the same model.
Limitations. Reasoning is mandatory and cannot be disabled, which adds latency compared to non-reasoning chat models, even though the model remains usable in interactive settings. It does not support a `temperature` parameter, and output is text-only — it cannot generate images or audio. Its training data cutoff is June 2026.
Sovereignty note. Claude Fable 5.1 is built by Anthropic and served under Anthropic's Acceptable Use Policy; no distinct licence exists for this model. The available research does not confirm endpoint data residency or CLOUD Act exposure, so no Sovereign designation applies.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €9.07 / 45.37+2 rates | Online |
20 use cases classified by the curator. The note says why.
1M-token context, tool-calling, and always-on adaptive reasoning support long-horizon autonomous coding workflows; family leads its class on the Artificial Analysis Intelligence Index at 65.
Adaptive reasoning plus tool-calling enables iterative hypothesis testing against large codebases within the 1M-token window.
1M context window allows full-repository review in a single pass, and the family's top-tier reasoning score supports deep structural analysis over premium-priced tokens.
1M-token context window allows ingesting entire large documents for Q&A without chunking, a key differentiator for this family.
Tool-calling capability plus high intelligence-index score make this a reliable backbone for structured function-calling applications.
Native text-and-image input plus frontier reasoning supports detailed visual question answering.
Native vision input supports chart-image parsing, though no chart-specific benchmark is documented for this variant.
Strong reasoning quality applies to analytical tasks, but premium pricing and below-average throughput make it a costlier choice than volume-oriented alternatives.
Vision input enables OCR-style document parsing, though this is not a specialized OCR model and carries premium per-token cost for high-volume scanning.
Strong reasoning and huge context support large-scale refactor planning, but below-average throughput (60.3 tok/s) makes iterative refactor loops slower than lighter-weight coding models.
Vision capability supports UI/screenshot analysis, consistent with the family's stated strength in structured-output and dataviz tasks.
High per-token pricing ($12.50/$50.00 per 1M) and below-average throughput (60.3 tok/s) make this a poor fit for high-volume, latency-sensitive chatbot deployments.
No audio-generation capability documented for this model.
Model has no documented audio capability, only text-and-image input.
No audio input/output capability is documented; unsuitable for voice-agent deployment.
No audio capability documented; model is text-and-image only.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "anthropic/claude-fable-5-1",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at