# MiniMax M2.5

Production-scale agentic coding with full-stack development and office deliverables

[Deploy model](https://api.together.ai/endpoints/configure/MiniMaxAI/MiniMax-M2.5) [read docs](https://docs.together.ai/docs/quickstart)

## About model

MiniMax M2.5 is **SOTA in coding, agentic tool use, search, and office work**, extensively trained with reinforcement learning across **200,000+ complex real-world environments**. The model achieves **80.2% SWE-Bench Verified** while completing tasks **37% faster than M2.1**, matching Claude Opus 4.6's speed. M2.5 exhibits **architect-level planning capability**, actively decomposing and planning features, structure, and UI design before writing code—spanning the **entire development lifecycle from 0-to-1 system design through 90-to-100 comprehensive testing**. Trained on 10+ programming languages across full-stack platforms (Web, Android, iOS, Windows), M2.5 delivers **truly deliverable outputs in office scenarios** on Together AI's production infrastructure.

### SWE-Bench Verified

80.2%

### SOTA coding across 200K+ real-world environments

### Faster Than M2.1

37%

### Matching Opus 4.6 speed with efficient decomposition

### Real-World Training Environments

200K+

### Model key capabilities

- **Architect-Level Planning:** Spec-writing with feature decomposition and UI design before coding—spanning 0-to-1 system design through 90-to-100 comprehensive testing  
- **SOTA Agentic Coding:** 80.2% SWE-Bench Verified across 10+ languages and full-stack platforms—37% faster than M2.1, matching Opus 4.6 speed  
- **Office Deliverables:** Word documents, PowerPoint presentations, Excel models trained with industry experts—59.0% win rate vs mainstream models  
- **Production-Ready Infrastructure:** 99.9% SLA, available on serverless and dedicated infrastructure

### Quickstart guides

- [RAG\ \\ Building a RAG Workflow](https://docs.together.ai/docs/building-a-rag-workflow)  
- [Agents\ \\ Agent Workflows](https://docs.together.ai/docs/workflows)  
- [Apps\ \\ Next.js Chat Quickstart](https://docs.together.ai/docs/nextjs-chat-quickstart)

### Model card

**Architecture Overview:**
- SOTA agentic model trained with reinforcement learning across 200,000+ complex real-world environments
- Forge agent-native RL framework with 40x training speedup through asynchronous scheduling and tree-structured sample merging
- CISPO algorithm ensuring MoE model stability during large-scale RL training
- Process reward mechanism for end-to-end generation quality monitoring in long-context agent rollouts
- Optimal trade-off between intelligence and response speed through trajectory-based task completion time evaluation
- Trained on 10+ programming languages: Go, C, C++, TypeScript, Rust, Kotlin, Python, Java, JavaScript, PHP, Lua, Dart, Ruby

**Training Methodology:**
- Extensive RL training in hundreds of thousands of real-world coding, search, and office work environments
- Collaboration with senior professionals in finance, law, and social sciences for office deliverables training
- Industry expert-designed requirements, feedback, and standards contributing to data construction
- Architect-level planning emerged during training: spec-writing before coding with feature decomposition
- Trained for efficient reasoning and optimal task decomposition reducing token consumption by 5% vs M2.1
- Full development lifecycle training: 0-to-1 system design, 1-to-10 development, 10-to-90 iteration, 90-to-100 testing

**Performance Characteristics:**
- Coding Excellence: 80.2% SWE-Bench Verified, 51.3% Multi-SWE-Bench, 79.7% Droid, 76.1% OpenCode
- Agentic Leadership: 76.3% BrowseComp (with context management), 20% fewer search rounds vs M2.1
- Office Deliverables: 59.0% win rate in GDPval-MM evaluation vs mainstream models
- Speed: 37% faster than M2.1 on SWE-Bench Verified (22.8 min vs 31.3 min), matching Claude Opus 4.6
- Cost Efficiency: 10% cost of Claude Opus 4.6 per task, $1/hour continuous operation at 100 TPS
- Token Efficiency: 3.52M tokens/task vs M2.1's 3.72M, 5% reduction through better decomposition
- Additional Benchmarks: 86.3% AIME25, 85.2% GPQA-D, 70.0% IFBench, 44.4% SciCode

### Applications & use cases

**Full-Stack Software Development:**
- Architect-level planning: Spec-writing with feature decomposition, structure design, and UI planning before coding
- Complete development lifecycle: 0-to-1 system design and environment setup through 90-to-100 comprehensive testing
- 80.2% SWE-Bench Verified, 51.3% Multi-SWE-Bench across 10+ programming languages
- Full-stack platforms: Web, Android, iOS, Windows with server-side APIs, business logic, databases
- Complex system development beyond bug-fixing: feature iteration, code review, system testing
- Multi-environment generalization: 79.7% on Droid, 76.1% on OpenCode with different scaffoldings

**Agentic Search & Tool Use:**
- Industry-leading performance: 76.3% BrowseComp with context management
- Efficient decision-making: 20% fewer search rounds than M2.1 with better token efficiency
- Precise search rounds with optimal reasoning paths to results
- Stable performance across unfamiliar scaffolding environments
- Deep webpage exploration for information-dense professional tasks

**Office Deliverables & Productivity:**
- Word documents, PowerPoint presentations, Excel financial models as truly deliverable outputs
- Trained with senior professionals in finance, law, and social sciences
- 59.0% win rate vs mainstream models in GDPval-MM office work evaluation
- Industry-specific tacit knowledge integrated into training pipeline
- High-value workspace scenarios: financial modeling, legal documents, research reports
- Professional trajectory evaluation alongside deliverable quality assessment

**Enterprise Coding Agents:**
- Autonomous software development at production scale
- Multi-language, multi-platform development workflows
- Integration with Claude Code and major coding agent frameworks
- Repository-scale navigation, refactoring, and comprehensive testing
- Real-world deployment: 80% of MiniMax's newly committed code is M2.5-generated

**Knowledge Work Automation:**
- Automated research report generation with proper formatting
- Financial model creation following organizational standards
- Legal document preparation with industry compliance
- Presentation creation with professional design standards
- Real-world productivity: 30% of MiniMax company tasks autonomously completed by M2.5

### Related models

### MiniMax AI

- **Model provider**: MiniMax AI  
- **Main use cases**: Chat, Function Calling  
- **Features**: Function Calling, JSON Mode  
- **Speed**: Medium  
- **Intelligence**: Very High  
- **Deployment**: Dedicated  
- **Parameters**: 228.7B  
- **Context length**: 192K  
- **Input price**: $0.30 / 1M tokens, $0.06 (cached)/1M  
- **Output price**: $1.20 / 1M tokens  
- **Input modalities**: Text  
- **Output modalities**: Text  
- **Released**: February 11, 2026  
- **Last updated**: February 14, 2026  
- **Quantization level**: FP4  
- **External link**: [HuggingFace](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)