Mistral Code Agent Latest

Mistral Code Agent Latest is Mistral's direct API alias for devstral-2512, an agentic coding model built for autonomous software engineering, tool use, and long-running code tasks.

  • Tool Calling
  • Structured Output

Added Jun 2, 2026

Pricing

Auto routing · per 1M tokens
Input
$0.40
Output
$2.00
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Specifications

Context window
262.1K
Max output
32.8K
Parameters
123B
Avg output (7d)
292 tokens
Longer than 24% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

8.6

Better than 32% of models compared

Coding Index

31.3

Better than 39% of models compared

Agentic Index

2.3

Better than 33% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

3.1%

Better than 24% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

479 Elo

Better than 23% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

531 Elo

Better than 28% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.4%

Better than 14% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

32.3%

Better than 32% of models compared

Reasoning

HLE

Humanity's Last Exam

3.6%

Better than 6% of models compared

IFBench

Instruction-following benchmark

38.1%

Better than 34% 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

32.8%

Better than 12% of models compared

LiveCodeBench

Contamination-free coding benchmark

44.8%

Better than 51% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

36.7%

Better than 37% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

76.2%

Better than 53% of models compared

AA-Omniscience Accuracy

Proportion of correctly answered questions

20.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

85.3%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

59.4%

Better than 34% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

18.9%

Better than 59% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

24.9%

Better than 28% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

32.3%

Better than 31% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

1.6%

Last updated Oct 1, 2026

Artificial Analysis

Providers

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