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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 modelGLM 5.3 TEEZ.aiRemove
ProviderMistralZ.ai
Discovery tierOpen weightsOpen weights
AddedApr 30, 2026Aug 31, 2026
Limits
Context window256K1.05MBest
Max output32.77K131.07KBest
Average output—1.2K tokens
Pricing, USD per 1M tokens
Input$1.50$1.40Best
Output$7.50$4.40Best
Cache read—$0.26
Performance
Throughput—38.5 tok/s
Time to first token—4.4 s
Request success (24h)—99.7%
Benchmark indexes
Intelligence Index—44.8
Coding Index—74.8
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputYesYes
Parallel toolsNoYes
Reasoning effortnone, highlow, high, max

Public evaluations

Benchmarks

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

  • Mistral Medium 3.5 Thinking
  • GLM 5.3 TEE

Intelligence Index

  • Mistral Medium 3.5 Thinking—
  • GLM 5.3 TEE44.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 74.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 62.2%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 29.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 1,505 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 1,649 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 11.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 79.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 17.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 42.3%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 91.7%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 41.9%

  • SciCode

    Scientific computing in Python

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 59.0%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Medium 3.5 Thinking: —
    • GLM 5.3 TEE: 79.7%

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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