MiniMax-M2

Creator

MiniMax

Released

2025-10-26

Intelligence

28.3

Artificial Analysis Index

Coding

Artificial Analysis Index

In $/1M

$0.30

input tokens

Out $/1M

$1.20

output tokens

Blended $/1M

$0.53

3:1 blended

Speed

0

tokens / sec

Profile

License, openness, and modality — the governance layer.

License

MIT

commercial OK

Weights

Open

downloadable

Modalities

Text

Parameters

230B

total

MiniMax M2 is an open-source large language model by MiniMax, built for agents and coding tasks. It delivers state-of-the-art tool use, reasoning, and search performance while maintaining exceptional cost-efficiency and speed, priced at just 8% of Claude 3.5 Sonnet’s cost and running at nearly double its inference speed (≈100 TPS). Designed for end-to-end agentic workflows, it excels at long-chain tool calling across Shell, Browser, Python, and other MCP tools. While slightly behind top overseas models in programming, it ranks among the best domestic models and top five globally on the Artificial Analysis benchmark. M2 powers the MiniMax Agent platform, available in Lightning Mode for fast tasks and Pro Mode for complex multi-step reasoning, and its weights, API, and deployment guides are freely available on Hugging Face, vLLM, and SGLang.

Capability profile

Category strength across 20 domains, via LLM Stats.

communication
90legal
80finance
80language
80healthcare
80tool calling
80frontend development
70agents
60biology
60
general
60physics
60chemistry
60reasoning
60code
50math
50search
50vision
10
Via LLM Stats

Benchmark breakdown

Independent evaluation scores, normalized to 0–100.

GPQA Diamond
78
Humanity’s Last Exam
13
MMLU-Pro
82
SciCode
36
LiveCodeBench
83
MATH-500
AIME 2025
78
τ²-Bench (agentic)
87
Terminal-Bench Hard
26
IFBench
72
Via Artificial Analysis

Providers

Inference hosts serving this model — their own pricing and measured performance, cheapest input first.

ProviderIn $/1MOut $/1MThroughputLatencyStatus
MiniMax$0.30$1.20704.00active
Novita$0.30$1.20active
All providers

2 hosts · via LLM Stats

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