約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."
メタデータ
仕様
州
Deprecated
建築
MoE
キャリブレートされた
いいえ
専門家の混合
はい
合計パラメータ
229B
アクティブ化されたパラメータ
推論
いいえ
Precision
FP8
コンテキスト長
197K
Max Tokens
他のModelsと比較
他のモデルに対してこのModelがどのように比較されるかを見てください。
MiniMaxAI
chat
MiniMax-M2.1
Total Context:
197K
Max output:
131K
Input：
$
0.29
/ M Tokens
Output:
$
1.2
/ M Tokens
MiniMaxAI
chat
MiniMax-M2
Total Context:
197K
Max output:
131K
Input：
$
0.3
/ M Tokens
Output:
$
1.2
/ M Tokens
MiniMaxAI
chat
MiniMax-M1-80k
Total Context:
131K
Max output:
131K
Input：
$
0.55
/ M Tokens
Output:
$
2.2
/ M Tokens
MiniMaxAI
chat
MiniMax-M3
Total Context:
1049K
Max output:
131K
Input：
$
0.3
/ M Tokens
Output:
$
1.2
/ M Tokens
MiniMaxAI
chat
MiniMax-M2.5
Total Context:
197K
Max output:
Input：
$
0.3
/ M Tokens
Output:
$
1.2
/ M Tokens
