MiniMax-M2.7 is a large-scale Mixture-of-Experts language model with roughly 228 billion parameters, designed from the ground up for autonomous, real-world productivity. Rather than operating as a simple chatbot, it leverages multi-agent collaboration to plan, execute, and refine complex tasks across dynamic environments. The architecture supports building elaborate agent harnesses, coordinating Agent Teams, and dynamically searching for and invoking tools as needed. Its sweet spot lies in coding workflows, live debugging, root cause analysis, and full document generation across Word, Excel, and PowerPoint, making it a workhorse for professional productivity and agentic applications.

The model demonstrates a notable commitment to continuous self-improvement, with documentation indicating it actively participates in its own evolution during training. It delivers strong results on developer-focused benchmarks, achieving 56.2% on SWE-Pro and 57.0% on Terminal Bench 2, while reaching 1495 ELO on the GDPval-AA evaluation. As an open-weights model available across multiple hosting platforms, M2.7 is accessible for teams building sophisticated AI systems without gatekeeping. Its combination of production-grade performance, multi-agent coordination capabilities, and real-world workflow handling positions it for teams deploying autonomous agents in software development, financial modeling, and complex document automation scenarios.