DeepSeek 周末更新的 V4.1-Flash-1017,性能更强还降价,搜索直接做成 API,5 元一千次,Agent 开发者可以马上试试。
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DeepSeek 给 V4.1-Flash 做了次小更新,推理、编程、看图都变强,跑任务还更快更省 token,开源党可以关注下。
有人发现 DeepSeek 这两个版本号可能其实是同一个模型,用 DeepSeek 的朋友可以看看自己的 API 版本对应关系。
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。
有人实测了 Meta 新开源的 Muse Spark Open,结论是打不过 DeepSeek-v4.1-flash,还特能幻觉,想本地部署的先看看再决定。
Meta 开源了 816B 的 Muse Spark Open,主打 1M 上下文和 Agent 任务,作者还算清了要跑起来得 700GB 显存,看看你的卡够不够。
有人用 Codex 加 Deepseek-V4.1-Flash,让模型只写 JSON 就能自动生成会动的架构图,还能导出 GIF 和 MP4,画时序图、Sankey 图都行,做技术分享很实用。
This price reduction makes long-context RL training and evaluation more accessible, potentially accelerating the development of specialized agent models. Developers building intelligent systems can now run more iterations with the same budget, while researchers can evaluate models on longer contexts without prohibitive costs.
This release significantly improves inference efficiency through advanced attention mechanisms and memory management, enabling faster processing of large models. Developers working with LLMs in production environments will benefit from the optimized serving capabilities and experimental features like engine snapshots, particularly for models requiring high-throughput processing.
一个人用 ARTEX 加上 DeepSeek、GLM、Grok 和 Claude Code 就能打穿韩国多家银行,CrowdStrike 的报告把工具链讲得很细。
SWA bounded replay shows how KV-cache reuse for agent loops can be engineered around sliding-window attention, a pattern many teams will copy as agent traffic grows. Engineers running agent stacks on vLLM can apply it to reduce TTFT under high concurrency. The writeup also signals that DeepSeek-V4.1-Flash inference performance is being actively tuned by the community rather than relying on the lab alone.
vLLM 花三周把 DeepSeek-V4.1-Flash 跑快了 1.9 倍,AgentX 上吞吐 5.3 倍,还附交互图表拆解过程,做部署的可以看看。
Cline 暂停了 DeepSeek-V4.1-Flash 免费额度,说是被滥用太狠了,用这家的朋友可以看下还能不能白嫖。
The performance gap between open-source and closed models is narrowing faster than expected, with GLM-5.3 demonstrating competitive vulnerability exploitation capabilities. Developers and security researchers should evaluate this model for ethical hacking and security testing applications, especially given its open weights nature that allows for customization and inspection.
Qwen3.8-Max 实际修复服务器案例,对比 Grok-4.7 和 DeepSeek-V4.1-Flash 表现更优。
有人在比 GPT-6-Luna 和 DeepSeek-V4.1-Flash 的实际成本,短上下文选 Luna,272K 以上大输入选 DeepSeek,做 agent 的可以看看怎么省钱。
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。
DeepSWE测试发现DeepSeek V4.1-Flash的输入token消耗远高于输出,缓存命中率高,能大幅降低成本。
DeepSeek 发布了新模型,性能提升 4.87%,成本只有 0.07 美元,比很多模型便宜很多。
DeepSeek新发布的V4.1-Flash (Max)模型,任务成本比之前低,性能还提升了4.87%,在性能和成本上都有优势。
DeepSeek 新出的 V4.1-Flash (Max) 模型在 Agent Arena 排名第三,成本还特别低,适合做任务。
This new model is more capable, faster, and more cost-effective than previous DeepSeek models, offering a better alternative for users seeking a powerful AI tool.
This is a new version of the DeepSeek-V4.1-Flash model that is now available for Pro plan subscribers on Ollama's cloud.
DeepSeek 新出的 V4.1-Flash 模型,在 Ollama 云端上线了,比之前的小模型更智能、更快,还能处理图片,适合 Max 和 Team 帐户的用户。
朋友,Deepseek 新出的 552B 大模型现在在 Fireworks 上能用,做编程和网络安全很厉害,比 GPT 5.6 Sol 便宜很多。
朋友,Deepseek 新出的 552B 大模型现在在 Fireworks 上能用,编程和网络安全方面比 Opus 5 强,还便宜很多。
Claude和GPT-6的新版本让专有模型在网页开发上又领先了,想了解最新差距的可以看看。
DeepSeek新出的V4.1-Flash模型参加Agent Arena竞赛,你可以去投票看看它的表现,这个模型设计得更智能、更快、更高效。
For users looking for a high-performing, cost-effective AI model, DeepSeek-V4.1-Flash offers a strong alternative to models like Opus 5, with its open-weight nature and improved efficiency.
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.
This new model from DeepSeek is more efficient and faster than previous versions, which could be beneficial for users looking for improved performance.
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.
Priced same as V4 Flash with multimodal support, offering better value for developers in Southeast Asia working with visual data.