MiniMax releases M2.7, a 229B parameter model with self-evolving capabilities and agent teams

MiniMax Releases M2.7: Open-Source 229B Model With Self-Evolution Capability

MiniMax has released MiniMax-M2.7, a 229-billion parameter open-source model that introduces a self-evolution cycle, allowing the model to autonomously improve its own learning process during development.

Model Self-Evolution

M2.7's defining feature is its participation in its own development. During training, the model autonomously updated its memory, built dozens of complex skills for reinforcement learning experiments, and improved its learning process based on experimental results. In one internal benchmark, an M2.7 instance optimized a programming scaffold over 100+ iterations—analyzing failure trajectories, modifying code, running evaluations, and deciding whether to keep or revert changes—achieving a 30% performance improvement.

Software Engineering and System-Level Reasoning

M2.7 demonstrates strong capabilities in production engineering tasks spanning log analysis, bug troubleshooting, refactoring, and security analysis. The model shows system-level reasoning across monitoring metrics, trace analysis, and root-cause verification. MiniMax reports reducing live production incident recovery time to under three minutes on multiple occasions using M2.7.

On software engineering benchmarks:

• SWE-Pro: 56.22% (matching GPT-5.3-Codex)
• SWE Multilingual: 76.5%
• Multi SWE Bench: 52.7%
• Terminal Bench 2: 57.0%
• NL2Repo: 39.8%
• VIBE-Pro: 55.6% (near Opus 4.6 parity)

On machine learning competitions, M2.7 achieved 66.6% medal rate on MLE Bench Lite (22 competitions), second only to Opus-4.6 and GPT-5.4 according to MiniMax's claims.

Multi-Agent Capabilities and Professional Work

M2.7 natively supports Agent Teams for multi-agent collaboration with stable role identity and autonomous decision-making. The model demonstrates capability in document editing (Word, Excel, PowerPoint) with high-fidelity multi-round editing and produces editable deliverables.

On professional work benchmarks:

• GDPval-AA: 1495 ELO score (highest among open-source models)
• Toolathon: 46.3% accuracy
• MM Claw end-to-end: 62.7% (close to Sonnet 4.6)
• MM Claw skill compliance: 97% across 40+ complex skills

Deployment and Access

MiniMax-M2.7 is available as open-source weights on Hugging Face (229B parameters, supporting F32, BF16, and F8_E4M3 tensor formats). The model can be deployed via SGLang, vLLM, Transformers, or NVIDIA NIM endpoints.

MiniMax also provides API access through MiniMax Agent (agent.minimax.io) and the MiniMax API platform (platform.minimax.io). The company offers an interactive demo called OpenRoom (openroom.ai) featuring real-time visual feedback and scene interactions.

Recommended inference parameters: temperature=1.0, top_p=0.95, top_k=40.

What This Means

M2.7 represents a shift toward models that can improve themselves during development—a capability that could accelerate iteration cycles for specialized tasks. The strong SWE and system engineering benchmarks position it competitively against closed-source reasoning models on production engineering work. The open-source release makes these capabilities accessible for self-hosted deployments, though claims about self-evolution achieving 30% improvements and MLE Bench performance warrant independent verification. The multi-agent framework and tool compliance metrics suggest MiniMax is targeting enterprise automation workflows alongside development tooling.