Artificial Analysis serves as a comprehensive benchmark for assessing the reasoning abilities of models, with the newly released MiniMax M2 model achieving high rankings among both open-source and all models. The project focuses on the quality of Chain of Thought (CoT) and responses, emphasizing logical completeness without redundancy to avoid overfitting and enhance capability generalization. The research highlights the importance of diverse data, including math and code, to improve reasoning across domains such as logical reasoning and creative tasks. The team found that using complex queries and scaling data effectively enhances model performance, leading to the creation of verifiable and non-verifiable data pipelines. Future work aims to explore compound capabilities and integrate different tasks and reasoning domains, while the predominantly intern-composed team invites further community engagement and collaboration.