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Mistral · Balanced

Mistral Medium 3.1

Added Sep 5, 2025

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Use in the app

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.

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Mistral Medium 3.1MistralThis modelMistral Small 3 24B (2501)MistralRemove
ProviderMistralMistral
Discovery tierBalancedOpen weights
AddedSep 5, 2025Sep 10, 2026
Limits
Context window131.07KBest32.77K
Max output32.77KBest8.19K
Average output60 tokens403 tokens
Pricing, USD per 1M tokens
Input$0.40$0.12Best
Output$2.00$0.35Best
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index9.2Best6.7
Coding Index20.5—
Math Index38.3Best4.3
Capabilities
ToolsNoYes
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Mistral Medium 3.1
  • Mistral Small 3 24B (2501)

Intelligence Index

  • Mistral Medium 3.19.2
  • Mistral Small 3 24B (2501)6.7

Mistral Medium 3.1: Better than 17% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Medium 3.1: 20.5
    • Mistral Small 3 24B (2501): —

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3.1: 38.3Top
    • Mistral Small 3 24B (2501): 4.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Medium 3.1: 4.1%
    • Mistral Small 3 24B (2501): —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Medium 3.1: 0.2%
    • Mistral Small 3 24B (2501): —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Medium 3.1: 530 Elo
    • Mistral Small 3 24B (2501): —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Medium 3.1: 385 Elo
    • Mistral Small 3 24B (2501): —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Medium 3.1: 1.2%
    • Mistral Small 3 24B (2501): —

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3.1: 42.7%Top
    • Mistral Small 3 24B (2501): 0.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Medium 3.1: 2.8%
    • Mistral Small 3 24B (2501): —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3.1: 4.7%Top
    • Mistral Small 3 24B (2501): 3.8%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3.1: 58.8%Top
    • Mistral Small 3 24B (2501): 46.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3.1: 68.3%Top
    • Mistral Small 3 24B (2501): 65.2%

  • IFBench

    Following precise output instructions

    • Mistral Medium 3.1: 39.8%Top
    • Mistral Small 3 24B (2501): 26.4%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Medium 3.1: 0.0%
    • Mistral Small 3 24B (2501): —

  • SciCode

    Scientific computing in Python

    • Mistral Medium 3.1: 32.4%
    • Mistral Small 3 24B (2501): —

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3.1: 40.6%Top
    • Mistral Small 3 24B (2501): 25.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3.1: 38.3%Top
    • Mistral Small 3 24B (2501): 4.3%

  • AIME

    Competition mathematics

    • Mistral Medium 3.1: —
    • Mistral Small 3 24B (2501): 8.0%

  • MATH-500

    Competition math problems

    • Mistral Medium 3.1: —
    • Mistral Small 3 24B (2501): 71.5%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Medium 3.1: 10.6%
    • Mistral Small 3 24B (2501): —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3.1: 40.6%Top
    • Mistral Small 3 24B (2501): 19.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3.1: 42.7%Top
    • Mistral Small 3 24B (2501): 0.0%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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.

Hourly request success · last 72 hoursLowest hour 100.0%
  • 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.

Read the quickstart
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

Other Mistral models, starting with the same family.