实测MiniMax M2.7：上能拆英伟达，下能演我爸妈

Core Insights - MiniMax has launched its M2.7 model, which emphasizes self-evolution in AI, marking a significant step in the industry towards recursive self-improvement and autonomous decision-making [1][2] - The model's capabilities have been benchmarked against various tasks, showing strong performance in software engineering and project execution, while still needing improvement in complex reasoning tasks [5][6] Group 1: Model Capabilities - M2.7 has demonstrated first-tier performance in engineering execution tasks, particularly in SWE Bench Pro and VIBE-Pro, indicating its ability to handle real-world coding challenges and end-to-end project tasks [5][6] - The model's performance in MM-ClawBench tests shows its capability to maintain context and execute multi-step tasks effectively, marking a significant advancement in its operational abilities [5][6] - However, M2.7 still has room for improvement in research-oriented tasks like MLE-Bench, which require higher levels of abstraction and systematic modeling [6] Group 2: Testing Scenarios - The model was tested in various scenarios, including simulating family conversations in a WeChat-like environment, showcasing its role-playing capabilities and understanding of character dynamics [8][9] - M2.7 successfully created a neon digital clock and a Snake game, demonstrating its ability to understand requirements, plan, code, and self-correct during the development process [22][25] - In a financial analysis task, M2.7 processed NVIDIA's FY2026 financial data to generate a comprehensive research report, interactive dashboard, and presentation, highlighting its proficiency in handling complex financial data and producing professional-grade outputs [41][43] Group 3: Future Directions - MiniMax is exploring new interactive systems like OpenRoom, which aims to enhance AI interaction in a web GUI space, indicating a shift towards more dynamic and engaging user experiences [44][45] - The evolution of M2.7 suggests a move away from traditional Q&A interactions towards a collaborative model where AI can autonomously progress tasks and self-correct, enhancing overall user experience [45][46]