MiniMax M2.7
by MiniMaxAI
Open source · 258k downloads · 933 likes
3.7
(933 reviews)ChatAPI & LocalAbout
MiniMax M2.7 est un modèle de langage avancé conçu pour générer des réponses précises et nuancées dans des contextes variés. Il excelle dans la compréhension et la production de texte, que ce soit pour des conversations naturelles, la rédaction de contenus ou l'analyse de données textuelles. Ses capacités incluent la génération de réponses contextualisées, la synthèse d'informations complexes et l'adaptation à différents styles de communication. Ce modèle est particulièrement adapté aux professionnels cherchant à automatiser des tâches rédactionnelles, aux développeurs intégrant des fonctionnalités de langage dans leurs applications, ou aux entreprises ayant besoin d'outils d'assistance conversationnelle. Ce qui le distingue, c'est son équilibre entre performance et accessibilité, offrant une solution efficace sans nécessiter des ressources computationnelles excessives.
Documentation
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 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, 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.
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 to serve MiniMax-M2.7. Please refer to our SGLang Deployment Guide.
vLLM
We recommend using vLLM to serve MiniMax-M2.7. Please refer to our vLLM Deployment Guide.
Transformers
We recommend using Transformers to serve MiniMax-M2.7. Please refer to our Transformers Deployment Guide.
ModelScope
You also can get model weights from modelscope.
NVIDIA NIM
MiniMax M2.7 is also available on NVIDIA NIM Endpoint.
Inference Parameters
We recommend using the following parameters for best performance: temperature=1.0, top_p = 0.95, top_k = 40. Default system prompt:
C#
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.
Contact Us
Contact us at [email protected].
Capabilities & Tags
transformerssafetensorsminimax_m2text-generationconversationalcustom_codeeval-resultsendpoints_compatiblefp8
Links & Resources