MiniMax: MiniMax M2.5 highspeed

MiniMax: MiniMax M2.7 highspeed

M2.7 highspeed: Same performance, faster, more agile

MiniMax: MiniMax M3.1-Flash

MiniMax-M3.1-Flash is a frontier multimodal language model with a 1M-token context window, designed for agentic reasoning, tool use, coding, and long-context tasks. It supports text, image, and video inputs, and offers tunable thinking depth through effort levels to balance reasoning quality, latency, and token usage.

MiniMax: MiniMax M3

MiniMax M3 is the first open-weights flagship that brings coding, agentic reasoning, million-token context, and native multimodality together in one model. It handles autonomous task decomposition, tool use, and multi-step reasoning with ease — and writes code that's meant to ship, not code that just runs. Built on MiniMax's proprietary Sparse Attention architecture, M3 natively supports million-token context windows for long-horizon agents, large codebases, and long-video understanding. Its multimodality isn't bolted on — it's trained in from day one, with text and vision deeply aligned at the semantic level.

MiniMax: MiniMax M2.7

M2.7 delivers outstanding performance in real-world software engineering, including end-to-end complete project delivery, log analysis and bug triaging, code security, machine learning, and more. On the benchmark SWE-Pro, M2.7 scores 56.22%, nearly matching the level of Opus. This capability also extends to end-to-end complete project delivery scenarios (VIBE-Pro 55.6%) and deep understanding of complex engineering systems on Terminal Bench 2 (57.0%). In the professional office domain, we have improved the model's specialized knowledge and task delivery capabilities across various fields. On GDPval-AA, its ELO score is 1495, the highest among open-source models. M2.7's ability to perform complex editing in the Office suite (Excel/PPT/Word) has significantly improved, enabling better multi-round revisions and high-fidelity editing. M2.7 is capable of interacting with complex environments. Across 40 complex skills (> 2000 tokens) cases, M2.7 still maintains a 97% skill adherence rate. In OpenClaw usage, M2.7 has shown significant improvement compared to M2.5, scoring close to the latest Sonnet 4.6 in the MMClaw evaluation. M2.7 possesses excellent identity retention capabilities and emotional intelligence. Beyond productivity use cases, it also opens up space for innovation in interactive entertainment scenarios.

MiniMax: MiniMax M2.5

MiniMax-M2.5 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world capability while maintaining exceptional latency, scalability, and cost efficiency. Compared to its predecessor, M2.1 delivers cleaner, more concise outputs and faster perceived response times. It shows leading multilingual coding performance across major systems and application languages, achieving 49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual, and serves as a versatile agent “brain” for IDEs, coding tools, and general-purpose assistance. To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns.

MiniMax: MiniMax M2-her

M2-her text chat model, designed for role-playing, multi-turn conversations and dialogue scenarios.