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MiniMax-M2

MiniMaxMiniMaxOpen WeightMIT · Usage Commercial

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.

Date de sortie
2025-10-26
Paramètres
230.0B
Longueur du contexte
205K
Modalités
text

Radar de capacités

37
general
83
coding
78
reasoning
58
science
80
agents
0
multimodal

Classements

Domaine#RangScoreSource
Classement codage196
67.0
AA
Classement général118
64.0
AA
Science256
50.0
AA

Scores de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

Communication

Tau2 Telecom87.0%Aut.

General

Tau-bench77.2%Aut.
IF72.0%Aut.
AA-Index61.0%Aut.

Language

MMLU-Pro82.0%Aut.

Math

AIME 202578.0%Aut.

Reasoning

LiveCodeBench83.0%Aut.
GPQANYU + Cohere + Anthropic (2023)78.0%Aut.
SWE-Bench Verified69.4%Aut.
SWE-bench Multilingual56.5%Aut.
BrowseComp-zh48.5%Aut.
Terminal-Bench46.3%Aut.
BrowseCompOpenAI (2025)44.0%Aut.
Multi-SWE-Bench36.2%Aut.
SciCode36.0%Aut.
Humanity's Last Exam12.5%Aut.

Indices d'évaluation AA

(Artificial Analysis)
Tau2(Sierra + U Toronto + Vector Institute (2025))
86.8
Livecodebench(UC Berkeley + MIT + Cornell (2024))
82.6
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
82.0
Aime 25(MAA (Mathematical Association of America))
78.3
Math Index(Artificial Analysis)
78.3
Gpqa(NYU + Cohere + Anthropic (2023))
77.7
Ifbench(Google Research (2023))
72.3
Lcr(Artificial Analysis)
64.3
Terminalbench Hard(Stanford × Laude Institute (2026))
25.8
Intelligence Index(Artificial Analysis)
18.6
Hle(Center for AI Safety + Scale AI (2025))
13.7

Scores par catégorie LLM Stats

(LLM Stats (zeroeval))
Communication
90
Language
80
Legal
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

Tarification

Prix d'entrée$0.3 / 1M tokens
Prix de sortie$1.2 / 1M tokens
Prix mixte (3:1)$0.525 / 1M tokens

Vitesse

Tokens/sec0.0
Délai du premier token0.00s
Temps de réponse0.00s

Classement des Prix par Fournisseur

Classement des Prix par Fournisseur

16 fournisseurs

Moins cher: MiniMaxPlus cher: Cortecs
FournisseurEntréeSortie
1MiniMaxMoins cher
$0
$0
2Novita
$0
$0
3DevPass (LLM Gateway)
$0.2
$1
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 (minimax.cn)
$0.3
$1.2
12Merge Gateway
$0.3
$1.2
13Ofox
$0.3
$1.2
14NanoGPT
$0.302
$1.207
15302.AI
$0.33
$1.32
16Cortecs
$0.349
$1.405

Comparer les prix entre différents fournisseurs API pour ce modèle.

Sources externes