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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 465 models

Side by side

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TryQwen3 Coder NextGemini 2.5 Flash Lite Preview (09/2025)Llama 4 MaverickAll 3

Public evaluations

Benchmarks

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

Intelligence Index

9.2

Better than 17% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    20.5

    Composite of the coding evaluations

    Better than 14% of 168 scored models

  • Math Index

    38.3

    Composite of the math evaluations

    Better than 38% of 118 scored models

Agentic work

  • AutomationBench-AA

    4.1%

    Workflow automation, penalized for guardrail violations

    Better than 23% of 173 scored models

  • AutomationBench-AA tasks completed

    0.2%

    Workflows finished end to end without a guardrail violation

    Better than 13% of 173 scored models

  • AA-Briefcasev1.1

    530 Elo

    Agentic knowledge work, rated head to head

    Better than 22% of 166 scored models

  • GDPval-AAv2.1

    385 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 6% of 187 scored models

Document reasoning

  • GDP.pdf

    1.2%

    Professional reasoning over PDF documents, all-pass rate

    Better than 4% of 171 scored models

  • AA-LCRv1.1

    42.7%

    Reasoning across long documents

    Better than 20% of 309 scored models

  • MLCR-AA

    2.8%

    Long-context reasoning over medical records

    Better than 9% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    4.7%

    Expert-level questions across academic fields

    Better than 14% of 323 scored models

  • GPQA Diamond

    58.8%

    Graduate-level science questions

    Better than 17% of 291 scored models

  • MMLU-Pro

    68.3%

    Multi-subject knowledge and reasoning

    Better than 17% of 132 scored models

  • IFBench

    39.8%

    Following precise output instructions

    Better than 27% of 215 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    32.4%

    Scientific computing in Python

    Better than 8% of 174 scored models

  • LiveCodeBench

    40.6%

    Recent competitive programming problems

    Better than 34% of 129 scored models

Math

  • AIME 2025

    38.3%

    Competition mathematics, 2025 problems

    Better than 38% of 118 scored models

Legacy evaluations

  • Terminal-Bench Hard

    10.6%

    Earlier terminal task suite

    Better than 22% of 212 scored models

  • τ²-Bench Telecom

    40.6%

    Tool use in a customer-support simulation

    Better than 32% of 214 scored models

  • AA-LCR (unversioned)

    42.7%

    Long-context reasoning, earlier grading

    Better than 20% of 309 scored models

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

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