Mistral Medium 3.5 Thinking

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

  • Reasoning
  • Vision
  • Tool Calling
  • Structured Output

Added Apr 30, 2026

Pricing

Auto routing · per 1M tokens
Input
$1.50
Output
$7.50

Specifications

Context window
256K
Max output
32.8K
Parameters
128B
Avg output (7d)
82 tokens
Longer than 7% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

14.2

Better than 57% of models compared

Coding Index

46.9

Better than 55% of models compared

Agentic Index

7.0

Better than 37% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

6.3%

Better than 29% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

0.3%

Better than 24% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

523 Elo

Better than 23% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

758 Elo

Better than 33% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.8%

Better than 18% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

69.3%

Better than 59% of models compared

MLCR-AA

Medical long-context reasoning

10.0%

Better than 32% of models compared

Reasoning

HLE

Humanity's Last Exam

13.8%

Better than 58% of models compared

IFBench

Instruction-following benchmark

68.8%

Better than 82% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

40.2%

Better than 26% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

24.7%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

81.6%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

74.8%

Better than 56% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

33.3%

Better than 77% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

94.2%

Better than 92% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

69.3%

Better than 59% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

12.9%

Last updated Oct 22, 2026

Artificial Analysis

Providers

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Reliability

Last 3 days