Mistral Medium 3.1 (v25.08)
Mistral Medium 3.1 (v25.08) is an API model from Mistral AI. It’s positioned for vision + text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Medium
Speed
Medium
106 tok/s output
Cost
Moderate
$0.40 in / $2.00 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Multimodal
Combines text, vision, and audio in one model.
Vision
Understands images, scenes, and visual context.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Medium tier — cheaper/faster than Large 3.
- 675B sparse MoE / ~41B active — Mistral's biggest model and first with 256K context
- Beats Kimi-K2 and DeepSeek-3.1 on Mistral's headline benchmarks; 1418 LMSYS Elo at release
- MMLU 88.7% and HumanEval 92.3% — frontier-tier on coding, top of the open-weight pack
Best use cases
- Vision + text workflows that benefit from iteration (image input is first-class)
- Long-document analysis up to 256K tokens at affordable open-weight pricing
- European customers needing GDPR-friendly hosting and EU-anchored vendor
Tools to try
Not ideal for
- Latency-sensitive interactive chat — throughput is below average
- Hardest reasoning leaderboards — Claude Opus 4.5, GPT-5, DeepSeek V4 score higher