MiniMax
Released March 18, 2026

MiniMax M2.7

MiniMax M2.7 from MiniMax is built for complex agent teams, self-evolution, software engineering, and office editing with strong tool use and workflows.

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Model Overview

Capabilities, design details, and architectural traits

MiniMax M2.7 - self-evolving agent model

MiniMax M2.7 is built around self-improvement and complex agent workflows. It is presented as a model for software engineering, office tasks, and multi-agent coordination.

TraitDocumented detail
Self-improvement loopIt is described as a model that participated in its own evolution and uses repeated optimization cycles.
Agent TeamsIt is built to work with multiple agents and to handle complex agent harnesses.
Tool useIt supports tool interaction and dynamic tool search during task execution.
Office editingIt is documented for multi-turn, high-fidelity editing in Excel, PowerPoint, and Word.
Coding and engineeringIt is highlighted for end-to-end software delivery, bug hunting, security analysis, and ML tasks.

Agent workflow

The model is framed as a system for handling hard, multi-step productivity work. Its identity is tied to repeated refinement, tool use, and coordinated execution across agent roles.

Workspace focus

It is also positioned for professional office work, with stronger handling of complex edits and document workflows. The model page emphasizes productivity tasks rather than general chat alone.

Benchmark Performance

Independent evaluations · Artificial Analysis

22.8%
Intelligence
52.6%
Coding Index
15.3%
Agentic Index

Accuracy & Capability Details

GPQA - Graduate Science87.4%
Humanity's Last Exam29.6%
SciCode - Scientific Coding50.1%
Instruction Following75.7%
Long Context Reasoning78.3%
τ²-Bench - Agentic Tasks84.8%
TerminalBench - System Control39.4%

Compare Models Side-by-Side

Evaluate specifications, pricing, and independent benchmark indices

Model Details
General Info
ProviderMiniMaxAnthropicAnthropic
Release DateMarch 18, 2026September 22, 2026September 28, 2026
Knowledge Cutoff--Jun 2026
Context & Limits
Context Window205K
1M
Best Context Window
1M
Best Context Window
Pricing (per 1M tokens)
Input Pricing
$0.30
Best Input Pricing
$4$2
Output Pricing
$1.20
Best Output Pricing
$20$10
Modalities
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Outputs
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Benchmarks (0-100)
Intelligence Index22.8
57.6
Best Intelligence Index
56.0
Coding Index52.6--
Agentic Index15.3--
MiniMax M2.7
Claude Opus 5.5
Claude Sonnet 5.5

Humanity's Last Exam

Extremely difficult logical reasoning and knowledge.

30%
MiniMax M2.7
Humanity's Last Exam
Score: 30%
MiniMax M2.7
61%
Claude Opus 5.5
Humanity's Last Exam
Score: 61%
Claude Opus 5.5
55%
Claude Sonnet 5.5
Humanity's Last Exam
Score: 55%
Claude Sonnet 5.5

Long Context Reasoning

Logical reasoning over long context windows.

78%
MiniMax M2.7
Long Context Reasoning
Score: 78%
MiniMax M2.7
85%
Claude Opus 5.5
Long Context Reasoning
Score: 85%
Claude Opus 5.5
83%
Claude Sonnet 5.5
Long Context Reasoning
Score: 83%
Claude Sonnet 5.5

SciCode Benchmark

Scientific coding and mathematical modeling.

50%
MiniMax M2.7
SciCode Benchmark
Score: 50%
MiniMax M2.7
67%
Claude Opus 5.5
SciCode Benchmark
Score: 67%
Claude Opus 5.5
61%
Claude Sonnet 5.5
SciCode Benchmark
Score: 61%
Claude Sonnet 5.5

Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.

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