Mistral Small 4 119B Thinking

Mistral Small 4 with reasoning enabled (reasoning_effort=high). A hybrid MoE model with deep step-by-step reasoning for complex prompts, coding, and multi-step problem solving.

  • Reasoning
  • Vision
  • Tool Calling
  • Structured Output

Added Mar 17, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.40
Output
$1.40
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Specifications

Context window
262.1K
Max output
16.4K
Parameters
119B / 6B
Total / active
Avg output (7d)
887 tokens
Longer than 65% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

11.3

Better than 45% of models compared

Coding Index

26.6

Better than 34% of models compared

Agentic Index

0.8

Better than 7% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

1.0%

Better than 13% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

242 Elo

Better than 15% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

365 Elo

Better than 20% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

0.0%

Better than 0% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

49.7%

Better than 43% of models compared

Reasoning

HLE

Humanity's Last Exam

9.9%

Better than 50% of models compared

IFBench

Instruction-following benchmark

48.2%

Better than 57% of models compared

CritPt

Research-level physics reasoning

0.3%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

38.8%

Better than 22% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

21.7%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

66.5%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

76.9%

Better than 61% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

17.4%

Better than 56% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

41.2%

Better than 47% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

49.7%

Better than 43% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

0.0%

Last updated Oct 4, 2026

Artificial Analysis

Providers

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