4,146 Downloads Updated 19 hours ago
MiniMax's M2-series model for coding, agentic workflows, and professional productivity.
ollama run minimax-m2.7:cloud
Readme
MiniMax M2.7 is the first model in the M2-series to deeply participate in its own evolution, capable of building complex agent harnesses and completing highly elaborate productivity tasks through Agent Teams, complex Skills, and dynamic tool search.
Highlights
Professional software engineering. M2.7 delivers outstanding performance in real-world engineering scenarios including end-to-end project delivery, log analysis and bug troubleshooting, code security, and machine learning. On SWE-Pro it scored 56.22%, matching GPT-5.3-Codex and nearly approaching Opus’s best level. On VIBE-Pro (55.6%) and Terminal Bench 2 (57.0%), it demonstrates deep understanding of complex engineering systems.
Professional work and complex environments. In the GDPval-AA evaluation across 45 models, M2.7 achieved an ELO score of 1495, the highest among open-source models. It handles complex editing in Excel, PPT, and Word with multi-round high-fidelity revisions, and maintains a 97% skill adherence rate across 40 complex skills (each exceeding 2,000 tokens). On Toolathon, M2.7 reached 46.3% accuracy, a global top-tier result.
Character consistency and entertainment. M2.7 possesses excellent character consistency and emotional intelligence. MiniMax has open-sourced OpenRoom, a Web GUI interaction system where conversation drives real-time visual feedback and scene interactions with characters proactively engaging their environment.
Benchmarks
Software Engineering
M2.7 reaches the level of state-of-the-art models across real-world programming tasks spanning multiple languages and system-level comprehension.
| Benchmark | M2.7 | Notes |
|---|---|---|
| SWE-Pro | 56.22% | Matches GPT-5.3-Codex |
| VIBE-Pro | 55.6% | Nearly on par with Opus 4.6 |
| Terminal Bench 2 | 57.0% | Deep system-level understanding |
| SWE Multilingual | 76.5 | Multi-language engineering |
| Multi SWE Bench | 52.7 | Multi-repo tasks |
| NL2Repo | 39.8 | Natural language to repository |
Professional Work & Office
| Benchmark | M2.7 | Notes |
|---|---|---|
| GDPval-AA (ELO) | 1495 | Highest among open-source models |
| Toolathon | 46.3% | Global top tier |
| MM Claw | 62.7% | Close to Sonnet 4.6 |
| Skill Adherence (40 skills) | 97% | Each skill >2,000 tokens |
Machine Learning (MLE Bench Lite)
In exploratory self-evolution tests across 22 ML competitions, M2.7 achieved an average medal rate of 66.6% across three 24-hour autonomous runs, second only to Opus 4.6 (75.7%) and GPT-5.4 (71.2%), tying with Gemini 3.1.