MiniMax-M2.5

MiniMax-M2.5

정보에 대해서MiniMax-M2.5

MiniMax-M2.5 is MiniMax's latest large language model, extensively trained with reinforcement learning across hundreds of thousands of complex real-world environments. Built on a 229B-parameter MoE architecture, it achieves SOTA performance in coding, agentic tool use, search, and office work, scoring 80.2% on SWE-Bench Verified with 37% faster inference than M2.1

Leverage MiniMax-M2.5's 229B MoE architecture and reinforcement learning optimizations to power high-performance, cost-effective agentic workflows.

Architect-Level System Design

Move beyond snippets to full-lifecycle development. M2.5 plans system architecture, UI design, and backend logic across multiple platforms.

Use Case Example:

"Designed a cross-platform Rust and Flutter real-time messaging app, generating the complete backend API schema and environment setup in a single session."

Autonomous Professional Research

Deploy agents for deep web exploration. M2.5 uses RISE-level search capabilities to synthesize information from dense, professional-grade sources.

Use Case Example:

"Conducted a deep-dive competitive analysis on solid-state battery patents, navigating 50+ technical papers to produce a synthesized market landscape report."

High-Value Office Automation

Automate complex deliverables in finance and law. M2.5 handles professional-grade Excel modeling, Word drafting, and PowerPoint generation.

Use Case Example:

"Built a dynamic Excel financial model for a Series B startup, integrating automated sensitivity analysis and professional-grade investor slide decks."

High-Throughput Code Auditing

Perform massive-scale codebase reviews at 100 tokens per second. Identify logical flaws and optimize performance with 'too cheap to meter' efficiency.

Use Case Example:

"Audited a 100,000-line Go microservices codebase for concurrency bugs, identifying three critical race conditions with 37% faster turnaround than previous models."

메타데이터

생성하다

라이센스

MODIFIED-MIT

공급자

MiniMaxAI

허깅페이스

사양

주

Deprecated

건축

MoE

교정된

아니요

전문가의 혼합

네

총 매개변수

229B

활성화된 매개변수

추론

아니요

Precision

FP8

콘텍스트 길이

197K

Max Tokens

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AI 개발을 가속화할 준비가 되셨나요?

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