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MMistral Medium 3.1 (batch)API

Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost.

131KContext window · tokens
$0.2Input price · per M tokens
$1Output price · per M tokens
Mistral AIProvider

Prices update automatically — checked daily against provider list prices.

See open-source alternatives → Compare all model prices

Benchmarks & performance

Independent benchmark scores for Mistral Medium 3.1 (batch), measured by Artificial Analysis. Higher is better.

Intelligence index9.2
Coding index20.5
Math index38.3
GPQA58.8%
MMLU-Pro68.3%
Humanity's Last Exam4.7%
Long Context Reasoning42.7%
LiveCodeBench40.6%
SciCode32.4%
AIME 202538.3%
IFBench39.8%
τ²-Bench40.6%
τ-Bench Banking8.2%
Terminal-Bench13.9%
Terminal-Bench Hard10.6%
vs. models measured heretop 79%
Scores higher than 21% of the 284 models measured by Artificial Analysis and tracked here.
Models at this level cost $0.37 per 1M tokens (median of 45) — this one costs $0.4.
Cheaper and better on this index: Claude Haiku 5.5 · Claude Haiku 5.5 (batch) · GLM 5.3 Flash and 49 more
Benchmark data by Artificial Analysis

About this model

Mistral Medium 3.1 (batch) is a commercial AI model by Mistral AI. The specifications below are tracked automatically: pricing is refreshed daily from public list prices, so the numbers on this page reflect the current cost of using the model through its API.

Frequently asked questions

What is Mistral Medium 3.1 (batch)?

Mistral Medium 3.1 (batch) is an AI language model from Mistral AI. It is a proprietary model, available through an API. It scores 9.2 on the Artificial Analysis intelligence index.

Is Mistral Medium 3.1 (batch) free?

Mistral Medium 3.1 (batch) is not free: it costs $0.2 per million input tokens and $1 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is Mistral Medium 3.1 (batch) good at?

Independent benchmarks from Artificial Analysis give it GPQA 58.8%, MMLU-Pro 68.3%, Humanity's Last Exam 4.7%, Long Context Reasoning 42.7%, LiveCodeBench 40.6%, SciCode 32.4%, AIME 2025 38.3%, IFBench 39.8%, τ²-Bench 40.6%, τ-Bench Banking 8.2%, Terminal-Bench 13.9%, Terminal-Bench Hard 10.6%. It is particularly used for code generation.

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