在将 AI 领先地位定为国家战略的政府推动下，深度求索、阿里巴巴集团和月之暗面等 发布接近美国头部企业系统能力的 AI 模型。为此，中国正推出一揽子慷慨的国家支持 设全国互联的数据处理枢纽网络。 China's ascendance has set off alarm bells in Silicon Valley and Washington. To stay ah Trump administration put out an AI Action Plan in July calling for cutting red tape to data centers and tap into enough electricity to power them. The US will do "whatever i the world in artificial intelligence," President Donald Trump said in a speech. 中国的崛起已让硅谷和华盛顿拉响警报。为保持领先优势，特朗普政府于 7 月发布《人 计划》，呼吁减少繁文缛节以建设更多 AI 数据中心，并确保充足 ...

Core Insights - The Qwen team has released two new models, Qwen3-4B-Instruct-2507 and Qwen3-4B-Thinking-2507, which are designed to enhance performance on various tasks, particularly in reasoning and general capabilities [2][3][5]. Model Performance - Qwen3-4B-Thinking-2507 achieved a score of 81.3 in the AIME25 assessment, outperforming competitors like Gemini 2.5 Pro and Claude 4 Opus [4][5][23]. - The new models support a context length of 256k, significantly improving context awareness and understanding [3][17]. Model Specifications - Qwen3-4B-Instruct-2507 is a non-reasoning model that enhances general capabilities and multi-language support, while Qwen3-4B-Thinking-2507 is a reasoning model tailored for expert-level tasks [7][16]. - The 4B parameter size is particularly friendly for edge devices, allowing for deployment on small hardware like Raspberry Pi [2][8][26]. Comparative Analysis - In various tests, Qwen3-4B-Instruct-2507 outperformed smaller closed-source models like GPT-4.1-nano and showed comparable performance to larger models like Qwen3-30B-A3B [13][15]. - The models exhibit significant improvements in areas such as instruction following, logical reasoning, and text generation, with enhanced alignment to user preferences [17][24]. Deployment Recommendations - The Qwen team has provided deployment suggestions for local use, including applications like Ollama and MLX-LM, and recommended using a quantized version for very small devices [27][28]. - For optimal performance, especially in reasoning tasks, it is advised to use a context length greater than 131,072 tokens [29]. Community Engagement - The Qwen team has encouraged community feedback and interaction, with links provided for accessing the new models on platforms like Hugging Face and ModelScope [26][36].