--- pipeline_tag: text-generation license: other license_name: other license_link: https://github.com/MiniMax-AI/MiniMax-M2.7/blob/main/LICENSE library_name: transformers base_model: - MiniMaxAI/MiniMax-M2.7 --- Upcasted version from original fp8 weights to bf16 for easier quantization/pruning 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](https://www.minimax.io/news/minimax-m27-en). ## 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 role identity and autonomous decision-making. ## Professional Work M2.7 achieved an **ELO score of 1495** on GDPval-AA (highest among open-weight models), surpassing GPT5.3. It handles Word, Excel, and PPT with high-fidelity multi-round editing, producing editable deliverables. On Toolathon, M2.7 reached **46.3%** accuracy (global top tier), and maintains **97% skill compliance** across 40+ complex skills on MM Claw. On the MM Claw end-to-end benchmark, M2.7 achieved **62.7%**, close to Sonnet 4.6. ## Entertainment M2.7 features strengthened character consistency and emotional intelligence. We open-sourced [OpenRoom](https://github.com/MiniMax-AI/OpenRoom), an interactive demo that places AI interaction within a Web GUI space with real-time visual feedback and scene interactions. Try it at [openroom.ai](https://www.openroom.ai/). ## How to Use - MiniMax Agent: https://agent.minimax.io/ - MiniMax API: https://platform.minimax.io/ - Token Plan: https://platform.minimax.io/subscribe/token-plan ## Local Deployment Guide Download the model from HuggingFace repository: https://huggingface.co/MiniMaxAI/MiniMax-M2.7 We recommend using the following inference frameworks (listed alphabetically) to serve the model: ### SGLang We recommend using [SGLang](https://docs.sglang.io/) to serve MiniMax-M2.7. Please refer to our [SGLang Deployment Guide](./docs/sglang_deploy_guide.md). ### vLLM We recommend using [vLLM](https://github.com/vllm-project/vllm) to serve MiniMax-M2.7. Please refer to our [vLLM Deployment Guide](./docs/vllm_deploy_guide.md). ### Transformers We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M2.7. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md). ### ModelScope You also can get model weights from [modelscope](https://modelscope.cn/models/MiniMax/MiniMax-M2.7). ### NVIDIA NIM MiniMax M2.7 is also available on [NVIDIA NIM Endpoint](https://build.nvidia.com/minimaxai/minimax-m2.7). ### Inference Parameters We recommend using the following parameters for best performance: `temperature=1.0`, `top_p = 0.95`, `top_k = 40`. Default system prompt: ``` You are a helpful assistant. Your name is MiniMax-M2.7 and is built by MiniMax. ``` ## Tool Calling Guide Please refer to our [Tool Calling Guide](./docs/tool_calling_guide.md). ## Contact Us Contact us at [model@minimax.io](mailto:model@minimax.io).
