MiniMax M2.1: Built for Real-World Complex Tasks, Multi-Language Programming

MiniMax M2.1: Significantly Enhanced Multi-Language Programming, Built for Real-World Complex Tasks MiniMax has been continuously transforming itself in a more AI-native way. The core driving forces of this process are models, Agent scaffolding, and organization. Throughout the exploration process, we have gained increasingly deeper understanding of these three aspects. Today we are releasing updates to the model component, namely MiniMax M2.1, hoping to help more enterprises and individuals find more AI-native ways of working (and living) sooner. In M2, we primarily addressed issues of model cost and model accessibility. In M2.1, we are committed to improving performance in real-world complex tasks: focusing particularly on usability across more programming languages and office scenarios, and achieving the best level in this domain. Key Highlights of MiniMax M2.1: Exceptional Multi-Programming Language Capabilities Many models in the past primarily focused on Python optimization, but real-world systems are often the result of multi-language collaboration. In M2.1, we have systematically enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript, and other languages. The overall performance on multi-language tasks has reached industry-leading levels, covering the complete chain from low-level system development to application layer development. WebDev and AppDev: A Comprehensive Leap in Capability and Aesthetics Addressing the widely recognized weakness in mobile development across the industry, M2.1 significantly strengthens native Android and iOS development capabilities. Meanwhile, we have systematically enhanced the model's design comprehension and aesthetic expression in Web and App scenarios, enabling excellent construction of complex interactions, 3D scientific scene simulations, and high-quality visualization, making vibe coding a sustainable and deliverable production practice. Enhanced Composite Instruction Constraints, Enabling Office Scenarios As one of the first open-source model series to systematically introduce Interleaved Thinking, M2.1's systematic problem-solving capabilities have been further upgraded. The model not only focuses on code execution correctness but also emphasizes integrated execution of "composite instruction constraints," providing higher usability in real office scenarios. More Concise and Efficient Responses Compared to M2, MiniMax-M2.1 delivers more concise model responses and thought chains. In practical programming and interaction experiences, response speed has significantly improved and token consumption has notably decreased, resulting in smoother and more efficient performance in AI Coding and Agent-driven continuous workflows. Outstanding Agent/Tool Scaffolding Generalization Capabilities M2.1 demonstrates excellent performance across various programming tools and Agent frameworks. It exhibits consistent and stable results in tools such as Claude Code, Droid (Factory AI), Cline, Kilo Code, Roo Code, and BlackBox, while providing reliable support for Context Management mechanisms including Skill.md, Claude.md/agent.md/cursorrule, and Slash Commands. High-Quality Dialogue and Writing M2.1 is no longer just "stronger in coding capabilities." In everyday conversation, technical documentation, and writing scenarios, it also provides more detailed and structured responses. First Impressions "We're excited for powerful open-source models like M2.1 that bring frontier performance (and in some cases exceed the frontier) for a wide variety of software development tasks. Developers deserve choice, and M2.1 provides that much needed choice!" Eno Reyes Co-Founder, CTO of Factory AI “MiniMax M2.1 performed exceptionally well across our internal benchmarks, showing strong results in...

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