Core Viewpoint - The article discusses the performance of various AI models, particularly focusing on their capabilities in mathematics and coding, highlighting the strengths and weaknesses of models like Qwen, MiMo, and MindGPT. Group 1: Model Performance - Qwen-7B outperforms MiMo in elementary mathematics tasks, which is unusual given that Qwen is a lower-tier model compared to MiMo [2] - The performance of models in the AIME (American high school mathematics competition) shows a significant disparity, with MiMo scoring high while struggling in other areas [2][5] - The results indicate that the pre-training of models like MiMo is heavily focused on mathematics and coding, potentially at the expense of other capabilities [1] Group 2: Model Comparison - MindGPT is noted to have a much larger parameter size compared to MiMo, making direct comparisons challenging [3] - The strategy of using smaller parameter models for specific metrics is seen as a way to showcase capabilities, although it may not reflect overall performance [3] - There is speculation that MiMo may have utilized distillation techniques for training, which could explain its performance discrepancies [4] Group 3: Community Insights - Discussions within the community suggest that the strategies employed by various teams, including the use of distillation, are common across the industry [7] - The community expresses a desire for genuine performance and capabilities rather than just marketing hype [3]