How is the Kimi large model's crawling preference and efficiency when handling long texts and complex queries?

When processing long texts and complex queries, the crawling preference of the Kimi large model usually tends to structured information and logical coherence, and its efficiency is reflected in the in-depth understanding of context and the ability to integrate information in multi-turn interactions. In terms of crawling preferences, Kimi is more sensitive to paragraphs with clear topic sentences and distinct layers in long texts, and can prioritize identifying key information such as core viewpoints, data support, and logical relationships, while having a certain filtering mechanism for redundant or repetitive content. In terms of efficiency performance, it has a fast response speed when processing long texts and can maintain a long context window (usually supporting tens of thousands of words of input). In complex queries, it can disassemble the problem logic and gradually derive answers through multi-turn interactions, reducing information omissions. Users can improve Kimi's processing efficiency by inputting long texts in sections and clarifying the core of the problem. For example, in complex queries, providing background information first and then raising specific questions helps the model capture key information more accurately.