MiniMax-M2.7

Join Our 💬 WeChat | 🧩 Discord community. MiniMax Agent | ⚡️ API | CLI | MiniMax Website 🤗 Hugging Face | 🐙 GitHub | 🤖️ ModelScope | 📄 LICENSE MiniMax-M2.7** is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search. For more details, see our blog post. Model Self-Evolution M2.7 initiates a cycle of model self-evolution: during development, we let the model update its own memory, build dozens of complex skills for RL experiments, and improve its own learning process based on experiment results. An internal version of M2.7 autonomously optimized a programming scaffold over 100+ rounds — analyzing failure trajectories, modifying code, running evaluations, and deciding to keep or revert — achieving a **30% performance improvement**. On MLE Bench Lite (22 ML competitions), M2.7 achieved a **66.6% medal rate**, second only to Opus-4.6 and GPT-5.4. Professional Software Engineering M2.7 delivers outstanding real-world programming capabilities spanning log analysis, bug troubleshooting, refactoring, code security, and machine learning. Beyond code generation, M2.7 demonstrates strong system-level reasoning — correlating monitoring metrics, conducting trace analysis, verifying root causes in databases, and making SRE-level decisions. Using M2.7, we have reduced live production incident recovery time to **under three minutes** on multiple occasions. On SWE-Pro, M2.7 achieved **56.22%**, matching GPT-5.3-Codex, with even stronger performance on real-world engineering benchmarks: **SWE Multilingual (76.5)** and **Multi SWE Bench (52.7)**. On **VIBE-Pro (55.6%)**, M2.7 is nearly on par with Opus 4.6. On **Terminal Bench 2 (57.0%)** and **NL2Repo (39.8%)**, M2.7 demonstrates deep understanding of complex engineering systems. M2.7 also supports native **Agent Teams** for multi-agent collaboration with stable rol