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MiniMax-M2

MiniMaxMiniMaxओपन वेटMIT · व्यावसायिक उपयोग

विवरण

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.

रिलीज़ तिथि
2025-10-26
पैरामीटर
230.0B
संदर्भ लंबाई
197K
मोडैलिटीज़
text

क्षमता रडार

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

रैंकिंग

बेंचमार्क स्कोर (LLM Stats)

(LLM Stats (zeroeval))

Agents

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

Biology

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

Code

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

Communication

Tau2 Telecom87.0%स्वयं

Finance

MMLU-Pro82.0%स्वयं

General

IF72.0%स्वयं
AA-Index61.0%स्वयं

Math

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

Reasoning

BrowseComp-zh48.5%स्वयं

AA मूल्यांकन सूचकांक

(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 श्रेणी स्कोर

(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

मूल्य निर्धारण

इनपुट मूल्य$0.3 / 1M टोकन
आउटपुट मूल्य$1.2 / 1M टोकन
मिश्रित मूल्य (3:1)$0.525 / 1M टोकन

गति

टोकन/सेकंड0.0
पहले टोकन में देरी0.00s
पहले उत्तर में देरी0.00s

प्रदाता मूल्य रैंकिंग

प्रदाता मूल्य रैंकिंग

14 प्रदाता

सबसे सस्ता: MiniMaxसबसे महंगा: Cortecs
प्रदाताइनपुटआउटपुट
1MiniMaxसबसे सस्ता
$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

इस मॉडल के लिए विभिन्न API प्रदाताओं के मूल्य निर्धारण की तुलना करें।

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