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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 modelQwen3 Coder NextQwenRemove
ProviderMistralQwen
Discovery tierBalancedOpen weights
AddedSep 25, 2025Dec 8, 2025
Limits
Context window131.07K262.14KBest
Max output32.77K65.54KBest
Average output683 tokens182 tokens
Pricing, USD per 1M tokens
Input$0.40$0.25Best
Output$2.00$0.80Best
Cache read—$0.0798
Performance
Throughput105.1 tok/s119.9 tok/sBest
Time to first token2.9 s1.6 sBest
Request success (24h)100.0%Best92.3%
Benchmark indexes
Intelligence Index9.09.2Best
Coding Index—36.2
Math Index30.3—
Capabilities
ToolsNoYes
VisionYesNo
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
  • Qwen3 Coder Next

Intelligence Index

  • Mistral Medium 39.0
  • Qwen3 Coder Next9.2

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: —
    • Qwen3 Coder Next: 36.2

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3: 30.3
    • Qwen3 Coder Next: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 1.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 404 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 513 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 2.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3: 31.3%
    • Qwen3 Coder Next: 47.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3: 4.1%
    • Qwen3 Coder Next: 10.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3: 57.8%
    • Qwen3 Coder Next: 73.7%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3: 76.0%
    • Qwen3 Coder Next: —

  • IFBench

    Following precise output instructions

    • Mistral Medium 3: 39.3%Top
    • Qwen3 Coder Next: 35.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 0.0%

  • SciCode

    Scientific computing in Python

    • Mistral Medium 3: —
    • Qwen3 Coder Next: 36.2%

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3: 40.0%
    • Qwen3 Coder Next: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3: 30.3%
    • Qwen3 Coder Next: —

  • AIME

    Competition mathematics

    • Mistral Medium 3: 44.0%
    • Qwen3 Coder Next: —

  • MATH-500

    Competition math problems

    • Mistral Medium 3: 90.7%
    • Qwen3 Coder Next: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Medium 3: 3.8%
    • Qwen3 Coder Next: 18.2%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3: 24.3%
    • Qwen3 Coder Next: 79.5%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3: 31.3%
    • Qwen3 Coder Next: 47.0%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.

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

Other Mistral models, starting with the same family.