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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 modelLlama 4 MaverickMetaRemove
ProviderMistralMeta
Discovery tierBalancedOpen weights
AddedSep 5, 2025Sep 5, 2025
Limits
Context window131.07K1.05MBest
Max output32.77K65.54KBest
Average output60 tokens114 tokens
Pricing, USD per 1M tokens
Input$0.40$0.19Best
Output$2.00$0.65Best
Cache read—$0.05
Performance
Throughput—15.7 tok/s
Time to first token—1.1 s
Request success (24h)100.0%Best99.7%
Benchmark indexes
Intelligence Index9.29.3Best
Coding Index20.5Best16.3
Math Index38.3Best19.3
Capabilities
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Medium 3.1
  • Llama 4 Maverick

Intelligence Index

  • Mistral Medium 3.19.2
  • Llama 4 Maverick9.3

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: 20.5Top
    • Llama 4 Maverick: 16.3

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3.1: 38.3Top
    • Llama 4 Maverick: 19.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Medium 3.1: 4.1%Top
    • Llama 4 Maverick: 0.2%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Medium 3.1: 0.2%Top
    • Llama 4 Maverick: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Medium 3.1: 530 Elo
    • Llama 4 Maverick: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Medium 3.1: 385 Elo
    • Llama 4 Maverick: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Medium 3.1: 1.2%Top
    • Llama 4 Maverick: 0.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3.1: 42.7%
    • Llama 4 Maverick: 50.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Medium 3.1: 2.8%
    • Llama 4 Maverick: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3.1: 4.7%
    • Llama 4 Maverick: 4.9%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3.1: 58.8%
    • Llama 4 Maverick: 67.1%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3.1: 68.3%
    • Llama 4 Maverick: 80.9%Top

  • IFBench

    Following precise output instructions

    • Mistral Medium 3.1: 39.8%
    • Llama 4 Maverick: 43.0%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Medium 3.1: 0.0%Top
    • Llama 4 Maverick: 0.0%Top

  • SciCode

    Scientific computing in Python

    • Mistral Medium 3.1: 32.4%Top
    • Llama 4 Maverick: 31.7%

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3.1: 40.6%Top
    • Llama 4 Maverick: 39.7%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3.1: 38.3%Top
    • Llama 4 Maverick: 19.3%

  • AIME

    Competition mathematics

    • Mistral Medium 3.1: —
    • Llama 4 Maverick: 39.0%

  • MATH-500

    Competition math problems

    • Mistral Medium 3.1: —
    • Llama 4 Maverick: 88.9%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Medium 3.1: 10.6%Top
    • Llama 4 Maverick: 6.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3.1: 40.6%Top
    • Llama 4 Maverick: 17.8%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3.1: 42.7%
    • Llama 4 Maverick: 50.0%Top

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.