MiniMax M2.7

MiniMax M2.7 is the first model deeply involved in iterating on its own training. It excels in real-world software engineering (SWE-Pro 56.22%), end-to-end project delivery (VIBE-Pro 55.6%), and complex office workflows with strong tool-use compliance and agentic capabilities.

  • Reasoning
  • Tool Calling
  • Structured Output

Added Mar 18, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.21
Output
$0.84
Cache read
$0.042
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Specifications

Context window
204.8K
Max output
131.1K
Parameters
230B / 10B
Total / active
Avg output (7d)
717 tokens
Longer than 60% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

22.8

Better than 74% of models compared

Coding Index

52.6

Better than 62% of models compared

Agentic Index

15.3

Better than 47% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

4.4%

Better than 26% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

717 Elo

Better than 30% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1013 Elo

Better than 46% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

6.0%

Better than 30% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

78.3%

Better than 81% of models compared

MLCR-AA

Medical long-context reasoning

3.3%

Better than 12% of models compared

Reasoning

HLE

Humanity's Last Exam

29.6%

Better than 77% of models compared

IFBench

Instruction-following benchmark

75.7%

Better than 94% of models compared

CritPt

Research-level physics reasoning

0.6%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

50.1%

Better than 53% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

26.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

35.6%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

87.4%

Better than 84% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

39.4%

Better than 86% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

84.8%

Better than 76% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

78.3%

Better than 80% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

25.5%

Last updated Oct 14, 2026

Artificial Analysis

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