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Mistral · Open weights

Mistral Medium 3.5

Added Apr 29, 2026Open weights

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Use in the app

Mistral Medium 3.5 is a 128B dense open-weights flagship model for instruction-following, reasoning, coding, long-horizon agentic work, tool use, structured output, and multimodal prompts. It supports a 256k context window and configurable reasoning effort.

  • Reasoning
  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Reasoning effort
none · high
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$1.50
Output
$7.50

Reference prices as published by the model provider. API example

Specifications

Context window
256K
Max output
32.8K

Side by side

Mistral Medium 3.5 compared

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

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Mistral Medium 3.5MistralThis modelMistral Small 3 24B (2501)MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedApr 29, 2026Sep 10, 2026
Limits
Context window256KBest32.77K
Max output32.77KBest8.19K
Average output—403 tokens
Pricing, USD per 1M tokens
Input$1.50$0.12Best
Output$7.50$0.35Best
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index—6.7
Math Index—4.3
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesNo
Structured outputYesNo
Reasoning effortnone, high—

Public evaluations

Benchmarks

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

  • Mistral Medium 3.5
  • Mistral Small 3 24B (2501)

Intelligence Index

  • Mistral Medium 3.5—
  • Mistral Small 3 24B (2501)6.7
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 4.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 3.8%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 46.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 65.2%

  • IFBench

    Following precise output instructions

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 26.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 25.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 4.3%

  • AIME

    Competition mathematics

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 8.0%

  • MATH-500

    Competition math problems

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 71.5%

Legacy evaluations

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3.5: —
    • Mistral Small 3 24B (2501): 19.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3.5: —
    • 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.

Request success (24h)
100.0%
Reliable
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-3.5",
    "reasoning_effort": "none",
    "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-3.5",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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