MiniMax M2.1

MiniMax M2.1 builds on M2 with enhanced context understanding and improved complex tool use. 230B parameter MoE model (10B active) optimized for agentic workflows and long-horizon tasks.

  • Reasoning
  • Tool Calling
  • Structured Output

Added Jan 20, 2025

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.33
Output
$1.32
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Specifications

Context window
200K
Max output
131.1K
Parameters
230B / 10B
Total / active
Avg output (7d)
638 tokens
Longer than 55% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

20.9

Better than 70% of models compared

Agentic work

T²-Bench Telecom (legacy)

Legacy fallback · Conversational AI agents in dual-control scenarios

85.4%

Better than 78% of models compared

Document reasoning

AA-LCR v1.1

Long context reasoning with updated grading

67.7%

Better than 59% of models compared

Reasoning

HLE

Humanity's Last Exam

23.2%

Better than 71% of models compared

IFBench

Instruction-following benchmark

69.9%

Better than 84% of models compared

CritPt

Research-level physics reasoning

0.3%

Coding

Terminal-Bench Hard (legacy)

Legacy fallback · Agentic coding and terminal use

28.8%

Better than 71% of models compared

LiveCodeBench

Contamination-free coding benchmark

81.0%

Better than 93% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

82.7%

Better than 79% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

87.5%

Better than 98% of models compared

AA-Omniscience Accuracy

Proportion of correctly answered questions

21.2%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

68.5%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

83.0%

Better than 73% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

67.7%

Better than 59% of models compared

Last updated Oct 12, 2026

Artificial Analysis

Providers

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