minimax-m2 API

minimax-m2 is a compact and efficient large language model, optimized for end-to-end programming and agent workflows, with 10 billion active parameters (230 billion total parameters), performing near state-of-the-art in general Inference, Tool use, and multi-step task execution, while maintaining low latency and high deployment efficiency. The model excels in code generation, multi-file editing, compile-run-fix loops, and defect repair in test verification, achieving excellent results in benchmarks such as SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench, and demonstrating competitiveness in long-cycle task planning, information retrieval, and execution error recovery in agent evaluations like BrowseComp and GAIA. Rated by Artificial Analysis, MiniMax-M2 ranks in the top tier of open-source models in comprehensive intelligence areas such as mathematics, scientific Inference, and instruction following. Its small active parameter count enables fast Inference, high concurrency, and better unit economics, making it ideal for large-scale agent deployment, developer auxiliary Tools, and Inference-driven applications requiring response speed and cost efficiency.