Mistral Medium 3.1
Mistral AI · released Aug 13, 2025
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. It balances...
Specification
- Context window
- 131K
- Max output
- —
- Knowledge cutoff
- Jun 30, 2025
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- No
- Uptime
- 99.8%
Intelligence
14.7
49th percentile
Coding
20.5
Coding Index
Agentic
6.1
Agentic Index
Output speed
51 t/s
Median across providers
Latency
844ms
Time to first token
Cost per task
$0.07
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro68.3%
- GPQA Diamond58.8%
- τ²-bench (Telecom)40.6%
- LiveCodeBench40.6%
- IFBench39.8%
- AIME 202538.3%
- SciCode33.8%
- AA-LCR (long context)21.3%
- Terminal-Bench Hard10.6%
- Humanity's Last Exam4.7%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 68.3% |
| GPQA Diamond | 58.8% |
| τ²-bench (Telecom) | 40.6% |
| LiveCodeBench | 40.6% |
| IFBench | 39.8% |
| AIME 2025 | 38.3% |
| SciCode | 33.8% |
| AA-LCR (long context) | 21.3% |
| Terminal-Bench Hard | 10.6% |
| Humanity's Last Exam | 4.7% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- gpt-oss-20bOpenAI15.2
- 15.0
- GPT-4.1 MiniOpenAI14.8
- Mistral Medium 3.1Mistral AI14.7
- Solar Pro 3Upstage14.5
- Llama 4 MaverickMeta14.5
- 14.4
- Ling-2.6-flashInclusionAI14.2
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| gpt-oss-20b | 15.2 |
| Nemotron 3 Nano Omni (free) | 15.0 |
| GPT-4.1 Mini | 14.8 |
| Mistral Medium 3.1 | 14.7 |
| Solar Pro 3 | 14.5 |
| Llama 4 Maverick | 14.5 |
| Qwen3 VL 235B A22B Instruct | 14.4 |
| Ling-2.6-flash | 14.2 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.4
- Output / 1M tokens
- $2
- Cached input / 1M
- $0.04
- Blended 3:1
- $0.8
One task, estimated
$0.07
- Input tokens
- 50,000
- Output tokens
- 25,000
- Profile
- Standard
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.