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MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real repositories and diverse programming languages. It is also optimized for multi-step agent tasks, planning and executing long toolchains that span shell commands, browsers, retrieval systems, and code runners. Benchmarks show that it achieves highly competitive scores on a wide range of intelligence and agent benchmarks, including SWE-Bench variants, Terminal-Bench, BrowseComp, GAIA, and several long-context reasoning suites.
Features
- MoE-based architecture with 230B total parameters and 10B active per token for strong performance at lower cost
- Optimized for coding workflows, including multi-file edits, compile–run–test loops, and bug-fixing in real repositories
- Strong agentic tool-use capabilities across shell, browser, retrieval, and code-execution environments
- Competitive benchmark performance on SWE-Bench, Terminal-Bench, BrowseComp, GAIA, and broader intelligence suites
- Open-weight release with deployment recipes for SGLang, vLLM, and MLX on local or cloud GPUs
- Anthropic-compatible API surface and integration with MiniMax Agent for quick adoption in existing stacks
Project Samples
