Claude Fable 5.1vsMistral Large 3
Claude Fable 5.1 | Mistral Large 3 | |
|---|---|---|
| Specifications | ||
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1M | — |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $10.00 | $0.50 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $50.00 | $1.50 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.25 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $10.00Amazon Bedrock | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $50.00Amazon Bedrock | — |
| BenchmarksPublished by one model only | ||
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 81.2% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 89.1% | — |
SWE-Bench MultimodalMultimodal coding — Real bug reports that arrive with pictures attached — a screenshot, a mockup, a page rendering wrongly — so the AI has to read the image as well as the code to work out what to fix. Higher is better. | 54.7% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 55.8% | — |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 52.6% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 60.9% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 65% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 90% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 31.4% | — |
HealthBench ProfessionalHealth — Realistic health conversations graded against detailed rubrics written by physicians — can the AI respond the way a careful medical professional would? Higher is better. | 62.1% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1853 | — |
AA-BriefcaseKnowledge work — Artificial Analysis agentic office-work eval (Elo) | 1694 | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Sep 1 2026 | Dec 2 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
Claude Fable 5.1vsGPT-6 AstraMistral Large 3vsGPT-6 AstraClaude Fable 5.1vsGemini 3.8 FlashMistral Large 3vsGemini 3.8 FlashClaude Fable 5.1vsMuse Spark 1.3Mistral Large 3vsMuse Spark 1.3Claude Fable 5.1vsGrok 4.6Mistral Large 3vsGrok 4.6Claude Fable 5.1vsDeepSeek-V4-Pro-0813Mistral Large 3vsDeepSeek-V4-Pro-0813Claude Fable 5.1vsKimi K3Mistral Large 3vsKimi K3
Frequently asked questions
Claude Fable 5.1 and Mistral Large 3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Large 3 is cheaper on both input and output: $0.50 vs $10.00 per million input tokens, and $1.50 vs $50.00 per million output tokens. Mistral Large 3 shipped 273 days before Claude Fable 5.1, so benchmark comparisons should account for the intervening progress.
Claude Fable 5.1 is proprietary, while Mistral Large 3 is open weight.
Direct benchmark comparisons are unavailable — Claude Fable 5.1 and Mistral Large 3 don't publish scores on any of the same benchmarks.
Claude Fable 5.1 was released by Anthropic on Sep 1 2026.
Mistral Large 3 was released by Mistral on Dec 2 2025.
Mistral Large 3 is cheaper on both input and output: $0.50 vs $10.00 per million input tokens, and $1.50 vs $50.00 per million output tokens. Rates are pay-as-you-go API prices verified on September 1, 2026.
Claude Fable 5.1 is a proprietary model released by Anthropic. Mistral Large 3 is an open weight model released by Mistral.