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Mistral · Open weights

Mistral Medium 3.5 Thinking

Added Apr 30, 2026Open weights

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Use in the app

Mistral Medium 3.5 with reasoning enabled by default (reasoning_effort=high), for complex coding, agentic, and multi-step reasoning prompts.

  • 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 Thinking compared

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

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Mistral Medium 3.5 ThinkingMistralThis modelMistral Medium 3MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsBalanced
AddedApr 30, 2026Sep 25, 2025
Limits
Context window256KBest131.07K
Max output32.77K32.77K
Average output—683 tokens
Pricing, USD per 1M tokens
Input$1.50$0.40Best
Output$7.50$2.00Best
Performance
Throughput—107.8 tok/s
Time to first token—0.7 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—9.0
Math Index—30.3
Capabilities
ReasoningYesNo
ToolsYesNo
VisionYesYes
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 Thinking
  • Mistral Medium 3

Intelligence Index

  • Mistral Medium 3.5 Thinking—
  • Mistral Medium 39.0
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 30.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 31.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 4.1%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 57.8%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 76.0%

  • IFBench

    Following precise output instructions

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 39.3%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 40.0%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 30.3%

  • AIME

    Competition mathematics

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 44.0%

  • MATH-500

    Competition math problems

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 90.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 3.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 24.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3.5 Thinking: —
    • Mistral Medium 3: 31.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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:thinking",
    "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:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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