MiniMax-M2: A Compact MoE for Coding & Agentic Workflows

MiniMax-M2, developed by MiniMaxAI, is a 229 billion total parameter Mixture-of-Experts (MoE) model that activates only 10 billion parameters per inference. This design prioritizes efficiency, delivering powerful performance for coding and agentic tasks with reduced latency and cost, making it highly deployable.

Key Capabilities

• Superior General Intelligence: Achieves a #1 composite score among open-source models globally on Artificial Analysis benchmarks, covering mathematics, science, instruction following, coding, and agentic tool use.
• Advanced Coding: Excels in end-to-end developer workflows, including multi-file edits, coding-run-fix loops, and test-validated repairs. Demonstrates strong performance on Terminal-Bench and SWE-Bench style tasks.
• Robust Agent Performance: Capable of planning and executing complex, long-horizon toolchains across shell, browser, retrieval, and code runners, with consistent recovery from flaky steps.
• Efficient Design: With only 10 billion active parameters, it offers faster feedback cycles, more concurrent runs, and simpler capacity planning, optimizing for responsive agent loops and better unit economics.

Good for

• Developers needing high-performance coding assistance in terminals, IDEs, and CI environments.
• Building interactive agents that require fast inference and robust tool-use capabilities.
• Use cases demanding frontier-style coding and agentic features without incurring frontier-scale costs.
• Applications benefiting from streamlined plan-act-verify loops and efficient resource utilization.