For agent memory, I keep choosing the boring DeepSeek pass
热度趋势
趋势数据积累中
百分比基于当前可用热度信号，而非评论数或独立用户人数。
Qwen 相关模型动态已经出现，适合跟踪能力变化、生态影响和后续可用性。
DeepSeek V4 Flash 0731在代理记忆任务中表现出色，尤其适用于“无聊的非思考性通过”，例如记忆分析和总结，在重复工作流程中，速度至关重要。尽管DeepSeek V4在复杂推理方面的上限常被讨论，但其在这些不那么引人注目但高吞吐量的任务中的实用性是一个显著优势。用户还提到Qwen 3.5 Flash是另一个不错的非思考模型选择，尤其适用于中文，并使用ZenMux作为API网关，以便轻松切换模型。
Most DeepSeek V4 discussion I see is about the ceiling. People push thinking mode, difficult coding, and long reasoning. In my own work on agent memory, the useful surprise has been much less dramatic. DeepSeek V4 Flash 0731 is extremely good at the boring nonthinking pass.
My common input is roughly 1,000 cached prompt tokens plus about 2,000 tokens of memory material. I need the model to pull out what matters, keep the relationships straight, and move on. In that narrow setup, Flash has been both fast and unusually sharp for me. I have preferred its summaries to the low and medium runs I tried from Luna, and to Terra on low effort.
Qwen 3.5 Flash was the other nonthinking model I liked, especially for Chinese. I did not test Qwen 3.7 or 3.8, so I have no opinion there. My old coding radar also gave the nonthinking V4 Flash and Pro route 50 points while Luna low got 8, but that was my own scoring system, not a public benchmark.
I use ZenMux as a single API gateway for DeepSeek and the other models in this workflow, so I can change the model without rebuilding the integration. I have been comparing Chinese AI models in this narrow memory workflow, and this boring pass is why DeepSeek stays in my rotation. For memory analysis and summarization, the speed matters because it sits inside a repeated workflow, not at the end of a single chat.