有人把 V4.1-Flash 去审查后跑分,结果挺有意思:抽象代数掉分,大学数学反而涨了,可以看看具体数字。
#V4.1-Flash
DeepSeek 把 V4.1-Flash-1017 转正了,Terminal-Bench 从 31.2 涨到 42.3,MIT 许可还能随便商用,跑智能体编程的可以试试。
DeepSeek 刚经历五小时宕机,转头就推了 V4.1-Flash,主打输出提速,用 DeepSeek 的可以关注下。
DeepSeek 这次更新挺实在:模型输出更短意味着 API 更省钱,还把自家搜索引擎开放成了 Search 和 Fetch 两个 API,做 Agent 的可以直接接上。
DeepSeek 更新了 V4.1-Flash,DeepSWE 跑到 77.0 分,速度更快输出更短,还把 Search API 开放出来了,可以试试。
DeepSeek 的 flash 模型指纹变了,疑似悄悄上了 V4.1-Flash-1017,有人说变聪明也更简洁了,可以自己试试对比。
This matters for developers and researchers working with large-scale text data, as it significantly reduces memory requirements for long-form processing.
DeepSeek 新发布的 V4.1-Flash 技术报告,优化了预填充计算、HBM 缓存、SSD/主机持久化缓存和缓存失效恢复,还用了近似的 SWA Bounded Replay 替代长期保存局部缓存状态。
For developers working with large language models, DeepSeek-V4.1-Flash offers a more cost-effective solution for long-context tasks by significantly reducing KV cache requirements.
This is a pricing change for DeepSeek's API that will affect developers using the V4-Pro model, as it will now be charged at the lower Flash rate until a new version is released.
This matters because it offers a cheaper alternative for building AI agents, with a quarter of the memory needs of its predecessor.
This update directly addresses a key cost factor for developers using AI agents, making the model more economical for deployment.
For users needing a smaller, faster AI model, DeepSeek-V4.1-Flash offers a new option that is more efficient than previous versions of the DeepSeek model family.
For users of the previous V4-Pro model, the new V4.1-Flash is a direct upgrade with better performance and lower costs.
For users of DeepSeek's API, the V4.1-Flash model offers better performance and lower costs compared to the previous V4-Pro version, with a smooth transition planned for September 14, 2026.