<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> MiniMax-M2.5 is a state-of-the-art frontier model designed to address complex agentic tasks, excelling in coding, tool use, search, and office productivity. It targets developers and researchers seeking high performance and cost-efficiency, enabling the creation of innovative agentic applications with significantly reduced operational costs and faster task completion times.

How It Works

M2.5 is extensively trained using reinforcement learning across hundreds of thousands of real-world environments, emphasizing efficient reasoning and optimal task decomposition. Its architecture supports advanced agentic capabilities, including sophisticated tool calling and search, and an "architect-like" planning ability for software development. The model leverages an agent-native RL framework called Forge, which decouples training from inference and optimizes generalization. It also incorporates a process reward mechanism for improved generation quality and task completion time alignment with user experience.

Quick Start & Requirements

Models can be downloaded from the Hugging Face repository: https://huggingface.co/MiniMaxAI/MiniMax-M2.5. Serving is recommended via frameworks such as SGLang, vLLM, Transformers, or KTransformers, with specific deployment guides available. Hardware requirements are not explicitly detailed but are implied by the use of these inference frameworks.

Highlighted Details

• Achieves SOTA performance on benchmarks like SWE-Bench Verified (80.2%), Multi-SWE-Bench (51.3%), and BrowseComp (76.3%).
• Matches Claude Opus 4.6 runtime on SWE-Bench Verified (22.8 mins) while costing approximately 10% of its operational expense.
• Offers exceptional cost-effectiveness, with continuous operation at 100 tokens/sec costing $1/hour, enabling "intelligence too cheap to meter."
• Provides comprehensive full-stack development support across multiple platforms and languages, alongside advanced search and office productivity skills honed with industry professional input.

Maintenance & Community

Community channels include 💬 WeChat and 🧩 Discord. The project is accessible via Hugging Face, GitHub, and ModelScope. Further details on MiniMax Agent and API Platform are available at https://agent.minimax.io/ and https://platform.minimax.io/ respectively.

Licensing & Compatibility

The model is released under a "Modified-MIT" license. Specific details of the modifications to the standard MIT license should be reviewed for full compatibility, particularly concerning commercial use.

Limitations & Caveats

While positioned as a cost-effective frontier model, the "Modified-MIT" license requires careful examination for any non-standard restrictions. The README indicates ongoing efforts to push model capability frontiers, suggesting continuous development.