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Autonomous coding and agentic tasks.
MiniMax develops self-evolving foundation models and intelligent agent architectures. The MiniMax M3 and M2.7 models power responsive reasoning and dynamic workflow automation.
Strict Zero Data Retention (ZDR): Requests are processed ephemerally in RAM. No user data, prompts, or completions are stored, indexed, or used for model training.
Available under ARMES unified subscription without separate API keys or billing agreements.
MiniMax-M3.1-Flash is a low-cost, open-weight sparse MoE multimodal model (23B active / ~428B total params) with a 1,048,576-token context, built for high-throughput agentic, coding, computer-use, and cybersecurity workloads at $0.15/$0.60 per 1M tokens.
MiniMax-M3 is a natively multimodal sparse Mixture-of-Experts foundation model (~428B total / ~22–23B active parameters) from MiniMax, built for frontier coding, long-horizon agentic work, and 1M-token context tasks. It combines Grouped-Query Attention with a blockwise MiniMax Sparse Attention scheme, accepts text/image/video input with text output, and runs at a measured ~97 tokens/sec. Additive exact-variant scores verified here include SWE-bench Verified 80.5%, SWE-bench Pro 59.0%, SWE-fficiency 34.8%, KernelBench Hard 28.8%, SAGE 50.6%, and SkillsBench 51.5%.
PinchBench: 86.2% (5th overall, within 1.2pts of Opus 4.6), SWE-Pro: 56.22%, GDPval-AA: 1495 Elo (highest open-source), MLE Bench Lite: 66.6%, Terminal Bench 2: 57.0% | Agentic coding, full-project delivery, complex engineering systems, autonomous debugging, production incidents, office productivity.
SWE-bench: 80.2% — rivals Claude Opus and GPT-5.2 | Coding, debugging, refactoring, full-stack development, front-end components, test coverage, technical implementation.
— | Previous generation coding and productivity
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