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MiniMax Launches M2.7, Its First Self-Evolving Agentic Model

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MiniMax-M2.7, MiniMax's latest model, is the first in their lineup built with an autonomous internal harness that collects feedback, builds evaluation sets, and iterates on its own architecture, skills, and memory mechanisms. On benchmarks, M2.7 scores 56.2% on SWE-Pro, 57% on Terminal Bench 2, and 62.7% on MM-ClawBench — with an 88% win-rate against M2.5 head-to-head. It achieves 97% skill adherence across 40+ complex skills in agent team settings.

This positions M2.7 as a direct competitor to frontier coding and agentic models. The model matches Claude Sonnet 4.6 in OpenClaw — the primary agentic coding benchmark cited. The self-evolving harness approach signals a shift — instead of purely human-curated training, models now help define the tasks they're evaluated and improved on.

Try M2.7 through the MiniMax Agent platform, which powers the Air, Max, and MaxClaw agents. API access is available for developers building their own agentic pipelines.

View the full update on agent.minimax.io
MiniMax (official)
MiniMax (official)
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Introducing MiniMax-M2.7, our first model which deeply participated in its own evolution, with an 88% win-rate vs M2.5 - Production-Ready SWE: With SOTA performance in SWE-Pro (56.22%) and Terminal Bench 2 (57.0%), M2.7 reduced intervention-to-recovery time for online incidents to 3-min on certain occasions. - Advanced Agentic Abilities: Trained for Agent Teams and tool search tool, with 97% skill adherence across 40+ complex skills. M2.7 is on par with Sonnet 4.6 in OpenClaw. - Professional Workspace: SOTA in professional knowledge, supports multi-turn, high-fidelity Office file editing. MiniMax Agent: https://t.co/aIzrFYcfUz API: https://t.co/fHRdSV7BwZ Token Plan: https://t.co/BDCycxepZw

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