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Mistral Medium 3.5 Thinking

mistralai/mistral-medium-3.5:thinking
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Mistral Medium 3.5 Thinking

mistralai/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.

Added Apr 30, 2026

Context Window

256.0K

Max Output

32.8K

Avg output tokens (7d)

1.5K tokens

80%

Input Price (Auto)

$1.50/1M

Output Price (Auto)

$7.50/1M

Cache Read (Auto)

$0.75/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

14.9

Better than 58% of models compared

Coding Index

46.9

Better than 56% of models compared

Agentic Index

9.4

Better than 41% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

6.3%

Better than 35% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

0.3%

Better than 7% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

524 Elo

Better than 28% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

875 Elo

Better than 41% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.8%

Better than 23% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

69.3%

Better than 65% of models compared

Reasoning

HLE

Humanity's Last Exam

13.8%

Better than 63% 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 18% of models compared

SciCode

Python programming for scientific computing

40.2%

Better than 29% 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 65% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

18.8%

Last updated Sep 11, 2026

Artificial Analysis

Providers

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