Mistral · Balanced
Mistral Medium 3.1
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 state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases. The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration in
- Input
- Text
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $0.40
- Output
- $2.00
Reference prices as published by the model provider. API example
Specifications
- Context window
- 131.1K
- Max output
- 32.8K
- Average output (7 days)
- 60 tokens
- Longer than 5% of 468 models
Side by side
Mistral Medium 3.1 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Mistral Medium 3.1MistralThis model | Mistral Large 4MistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Balanced | Open weights |
| Added | Sep 5, 2025 | Oct 6, 2026 |
| Limits | ||
| Context window | 131.07K | 524.29KBest |
| Max output | 32.77K | — |
| Average output | 60 tokens | — |
| Pricing, USD per 1M tokens | ||
| Input | $0.40Best | $0.68 |
| Output | $2.00Best | $2.09 |
| Cache read | — | $0.07 |
| Performance | ||
| Request success (24h) | 100.0%Best | 68.9% |
| Benchmark indexes | ||
| Intelligence Index | 9.2 | — |
| Coding Index | 20.5 | — |
| Math Index | 38.3 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | none, high |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Mistral Medium 3.1
- Mistral Large 4
Intelligence Index
- Mistral Medium 3.19.2
- Mistral Large 4—
Mistral Medium 3.1: Better than 17% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Mistral Medium 3.1: 20.5
- Mistral Large 4: —
Math Index
Composite of the math evaluations
- Mistral Medium 3.1: 38.3
- Mistral Large 4: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Mistral Medium 3.1: 4.1%
- Mistral Large 4: —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Mistral Medium 3.1: 0.2%
- Mistral Large 4: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Mistral Medium 3.1: 530 Elo
- Mistral Large 4: —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Mistral Medium 3.1: 385 Elo
- Mistral Large 4: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Mistral Medium 3.1: 1.2%
- Mistral Large 4: —
AA-LCRv1.1
Reasoning across long documents
- Mistral Medium 3.1: 42.7%
- Mistral Large 4: —
MLCR-AA
Long-context reasoning over medical records
- Mistral Medium 3.1: 2.8%
- Mistral Large 4: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mistral Medium 3.1: 4.7%
- Mistral Large 4: —
GPQA Diamond
Graduate-level science questions
- Mistral Medium 3.1: 58.8%
- Mistral Large 4: —
MMLU-Pro
Multi-subject knowledge and reasoning
- Mistral Medium 3.1: 68.3%
- Mistral Large 4: —
IFBench
Following precise output instructions
- Mistral Medium 3.1: 39.8%
- Mistral Large 4: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Mistral Medium 3.1: 0.0%
- Mistral Large 4: —
SciCode
Scientific computing in Python
- Mistral Medium 3.1: 32.4%
- Mistral Large 4: —
LiveCodeBench
Recent competitive programming problems
- Mistral Medium 3.1: 40.6%
- Mistral Large 4: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Mistral Medium 3.1: 38.3%
- Mistral Large 4: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Mistral Medium 3.1: 10.6%
- Mistral Large 4: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mistral Medium 3.1: 40.6%
- Mistral Large 4: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mistral Medium 3.1: 42.7%
- Mistral Large 4: —
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Mistral Medium 3.1
- Mistral Large 4
| Leaderboard | Mistral Medium 3.1 | Mistral Large 4 |
|---|---|---|
| Vision Style Control · Creative Writing | #8 · 1204.1 | Not in top 20 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 55.6%
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/mistral-medium-3.1",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="mistralai/mistral-medium-3.1",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Mistral
Related models
Other Mistral models, starting with the same family.