MiniMax-M2.5
О MiniMax-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."
Метаданные
Спецификация
Государство
Deprecated
Архитектура
MoE
Калибровка
Нет
Смешение экспертов
Да
Общее количество параметров
229B
Активированные параметры
Мышление
Нет
Точность
ФП8
Контекст length
197K
Максимум Tokens
Сравнить с другими Model
Посмотрите, как эта Model сравнивается с другими.
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Общий Контекст:
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Максимальный Output:
131K
Input:
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/ M Tokens
Output:
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1.2
/ M Tokens
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Общий Контекст:
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Максимальный Output:
131K
Input:
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0.3
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Output:
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1.2
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Общий Контекст:
131K
Максимальный Output:
131K
Input:
$
0.55
/ M Tokens
Output:
$
2.2
/ M Tokens
MiniMaxAI
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Общий Контекст:
1049K
Максимальный Output:
131K
Input:
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/ M Tokens
Output:
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1.2
/ M Tokens
MiniMaxAI
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Общий Контекст:
197K
Максимальный Output:
Input:
$
0.3
/ M Tokens
Output:
$
1.2
/ M Tokens
