MiniMax M2.1 aims to address the shortcomings of coding agents in real-world software development, particularly in handling multiple programming languages and complex tasks. Developers frequently find that tools excel in languages like Python but struggle with enterprise-level projects, where understanding intricate structures and dependencies is essential. MiniMax-M2.1 built a robust training pipeline using data from over ten mainstream programming languages, pulling from GitHub issues and pull requests. This approach highlights the challenges in creating effective multi-language environments, especially for compiled languages like Java or C++, which require navigating complex compilation processes and toolchains. The training also focused on enhancing multi-task capabilities beyond simple bug fixes. It incorporated test generation, code performance optimization, and code review functionalities. In particular, the model's ability to write efficient tests was identified as a major hurdle in the previous version, MiniMax-M1. The updated model not only improved test generation using insights from GitHub but also achieved a notable performance boost of 3.1% in execution efficiency. The introduction of an internal benchmark, SWE-Review, evaluates the accuracy of code reviews, demanding high precision to minimize false positives while identifying defects. Another key area of focus for MiniMax-M2.1 is generalization across different development scaffolds. Developers often use various frameworks, and if a model is tailored to one, its effectiveness in others diminishes. The training emphasizes long-range instruction following and adaptability, which are crucial for maintaining performance regardless of the environment. By addressing these specific challenges, MiniMax-M2.1 aspires to enhance the overall efficacy of coding agents in diverse and demanding software development scenarios.