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MiniMax-M2

MiniMaxMiniMaxOpen WeightMIT · Commercial OK

Description

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.

Release Date
2025-10-26
Parameters
230.0B
Context Length
197K
Modalities
text

Capability Radar

43
general
72
coding
78
reasoning
50
science
80
agents
0
multimodal

Rankings

Domain#RankScoreSource
Agentic Capability77
45.0
LS
Code Ranking123
69.0
AA
General Ranking104
71.0
AA
Science213
54.0
AA

Benchmark Scores (LLM Stats)

(LLM Stats (zeroeval))

Agents

Tau-bench77.2%SR
Terminal-Bench46.3%SR
BrowseCompOpenAI (2025)44.0%SR

Biology

GPQANYU + Cohere + Anthropic (2023)78.0%SR
SciCode36.0%SR

Code

LiveCodeBench83.0%SR
SWE-Bench Verified69.4%SR
SWE-bench Multilingual56.5%SR
Multi-SWE-Bench36.2%SR

Communication

Tau2 Telecom87.0%SR

Finance

MMLU-Pro82.0%SR

General

IF72.0%SR
AA-Index61.0%SR

Math

AIME 202578.0%SR
Humanity's Last Exam12.5%SR

Reasoning

BrowseComp-zh48.5%SR

AA Evaluation Indices

(Artificial Analysis)
Math Index(Artificial Analysis)
78.3
Intelligence Index(Artificial Analysis)
28.9
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.9
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.8
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.8
Aime 25(MAA (Mathematical Association of America))
0.8
Gpqa(NYU + Cohere + Anthropic (2023))
0.8
Ifbench(Google Research (2023))
0.7
Lcr(Artificial Analysis)
0.7
Scicode(UIUC + Argonne National Lab (2024))
0.4
Terminalbench Hard(Stanford × Laude Institute (2026))
0.3
Hle(Center for AI Safety + Scale AI (2025))
0.1

LLM Stats Category Scores

(LLM Stats (zeroeval))
Communication
90
Legal
80
Language
80
Finance
80
Healthcare
80
Tool Calling
80
Frontend Development
70
Physics
60
Reasoning
60
General
60
Agents
60
Biology
60
Chemistry
60
Math
50
Search
50
Code
50
Vision
10

Pricing

Input Price$0.3 / 1M tokens
Output Price$1.2 / 1M tokens
Blended Price (3:1)$0.525 / 1M tokens

Speed

Tokens/sec0.0
Time to First Token0.00s
Time to Answer0.00s

Provider Price Ranking

Provider Price Ranking

14 providers

Cheapest: MiniMaxMost Expensive: Cortecs
ProviderInputOutput
1MiniMaxCheapest
$0
$0
2Novita
$0
$0
3NanoGPT
$0.17
$1.53
4LLM Gateway
$0.2
$1
5OpenRouter
$0.3
$1.2
6ZenMux
$0.3
$1.2
7NovitaAI
$0.3
$1.2
8Kilo Gateway
$0.3
$1.2
9Vercel AI Gateway
$0.3
$1.2
10MiniMax (minimax.io)
$0.3
$1.2
11MiniMax (minimaxi.com)
$0.3
$1.2
12Merge Gateway
$0.3
$1.2
13302.AI
$0.33
$1.32
14Cortecs
$0.349
$1.405

Compare pricing across different API providers for this model.

External Sources