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Mistral · Balanced

Mistral Medium 3

Added Sep 25, 2025

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Use in the app

Mistral Medium 3 delivers frontier performance while being an order of magnitude less expensive. For instance, the model performs at or above 90% of Claude Sonnet 3.7 on benchmarks across the board at a significantly lower cost. On performance, Mistral Medium 3 also surpasses leading open models such as Llama 4 Maverick and enterprise models such as Cohere Command A. On pricing, the model beats cost leaders such as DeepSeek v3, both in API and self-deployed systems.

  • Vision
Input
Text, Images
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)
683 tokens
Longer than 61% of 468 models

Side by side

Mistral Medium 3 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Mistral Medium 3MistralThis modelMistral Medium 3.1MistralRemove
ProviderMistralMistral
Discovery tierBalancedBalanced
AddedSep 25, 2025Sep 5, 2025
Limits
Context window131.07K131.07K
Max output32.77K32.77K
Average output683 tokens60 tokens
Pricing, USD per 1M tokens
Input$0.40$0.40
Output$2.00$2.00
Performance
Throughput105.1 tok/s—
Time to first token2.9 s—
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index9.09.2Best
Coding Index—20.5
Math Index30.338.3Best
Capabilities
VisionYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Medium 3
  • Mistral Medium 3.1

Intelligence Index

  • Mistral Medium 39.0
  • Mistral Medium 3.19.2

Mistral Medium 3: Better than 16% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 20.5

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3: 30.3
    • Mistral Medium 3.1: 38.3Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 4.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 530 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 385 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 1.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3: 31.3%
    • Mistral Medium 3.1: 42.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 2.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3: 4.1%
    • Mistral Medium 3.1: 4.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3: 57.8%
    • Mistral Medium 3.1: 58.8%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3: 76.0%Top
    • Mistral Medium 3.1: 68.3%

  • IFBench

    Following precise output instructions

    • Mistral Medium 3: 39.3%
    • Mistral Medium 3.1: 39.8%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 0.0%

  • SciCode

    Scientific computing in Python

    • Mistral Medium 3: —
    • Mistral Medium 3.1: 32.4%

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3: 40.0%
    • Mistral Medium 3.1: 40.6%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3: 30.3%
    • Mistral Medium 3.1: 38.3%Top

  • AIME

    Competition mathematics

    • Mistral Medium 3: 44.0%
    • Mistral Medium 3.1: —

  • MATH-500

    Competition math problems

    • Mistral Medium 3: 90.7%
    • Mistral Medium 3.1: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Medium 3: 3.8%
    • Mistral Medium 3.1: 10.6%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3: 24.3%
    • Mistral Medium 3.1: 40.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3: 31.3%
    • Mistral Medium 3.1: 42.7%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.

Throughput
105.1 tok/s
Faster than 64% of 254 models
Time to first token
2.9 s
Quicker than 59% of 254 models
Prompt cache hits
41.1%
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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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