DeepSeek 又更新了,这次 V4.1-Flash-1017 跑智能体任务比更大的模型还强,成本还降了,还附带 Search API,Claude Code 里能免费用。
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vLLM 官方晒出 DeepSeek-V4.1-Flash-1017 的服务成绩:每美元 1.3B tokens,单卡 830K tok/s,跑智能体循环很省钱。
有人用 DeepSeek V4.1-Flash-1017 做了个 SVG 相机,作者说效果像 Sonnet 5.5,想看实际效果的可以点开视频。
DeepSeek 刚更了 V4.1-Flash-1017,写代码和跑 Agent 任务明显变强,专项基准还赢了 GPT-5.6-Sol,不过综合推理还是 Claude Opus 5 更稳。
DeepSeek 更新了 V4.1-Flash-1017,编程测试涨到 77.0,还专门改了话痨问题,输出更精简,权重直接开放下载。
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 把 V4.1-Flash-1017 的权重直接放上 Hugging Face 和 ModelScope 了,想本地跑的可以去下载试试。
DeepSeek 更新了 V4.1-Flash-1017,跑分更高、出结果更快、输出还更省,写代码工具也做了适配,可以直接在 API 里换上试试。
DeepSeek 刚发了 V4.1-Flash-1017,跑分更高还更快,输出更短更省钱,用 CodeBuddy 的人可以直接升级试试。
DeepSeek 刚发的 V4.1-Flash-1017,跑分更高还输出更短,用 CodeBuddy 或 Harness 的可以升级试试。
DeepSeek 新出的 V4.1-Flash-1017 是开源的,DeepSWE 1.1 跑分 77%,把 GPT-5.6 Sol 和 Opus 5 都比下去了,价格还便宜。
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。
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。
DeepSeek 的 V4.1 Flash 换了新版本号 1017,网页和 API 都自动切过去了,不用重新配置就能用上。
DeepSeek 的 Harness 框架更新了,能自定义插件索引还能让智能体自己造插件,API 里还闪现了个 v4.1-flash 快照,像是新模型在偷偷测试。
DeepSeek 把搜索和网页抓取直接做成了 API,每千次 5 元,Agent 和 RAG 应用接上就能用,还顺带更新了 Harness 的插件系统。
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。
把知识写进模型记忆到底行不行?这篇论文在 Qwen 和 DeepSeek 上做了实测,命中率数据挺扎眼,做记忆机制研究的值得看数据。
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。
The benchmark shows that outcome-only evaluation masks invalid execution, since models can hit the final goal while violating constraints mid-run — a gap that matters most in regulated domains like payments where intermediate errors carry financial or compliance consequences. Agent builders can use the 50-task suite and deterministic checks to measure constraint compliance and tool-call overhead, not just task success. Teams choosing models for workflow automation get a direct comparison of frontier and open models under identical conditions.
DeepSeek 这轮融资从 500 亿直接翻倍到 1000 亿元,估值却没涨,新进来的全是吉利、中芯聚源这种产业资本,还能顺带看看它科创板上市的筹备细节。
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 approach solves a critical bottleneck in long-context AI serving by enabling more efficient memory usage while maintaining high throughput. The technology allows models to handle significantly larger contexts without hitting memory limits, which directly impacts real-world applications requiring extensive context windows. Developers working with large language models should consider this optimization technique to improve performance in production environments.
This benchmark result demonstrates the practical effectiveness of combining enterprise frameworks with specialized models for coding assistance. Developers working on complex software engineering tasks can leverage this combination as a verified reference point for building their own coding assistants. The performance compares favorably to other coding assistants in the market.
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.
想用 LLM 智能体做黑盒优化的可以看看,五个领域统一评测,还拆了哪些设计选择真有用、哪些是伪收益。
拿 Minecraft 重建曼哈顿、白金汉宫这些真实地点来考模型找路,GPT-6 Astra 考了 85.6 分,开源的 DeepSeek-V4.1-Flash 才 23.9,差距很直观。
Benchmark averages can mask periodic failure modes in long-context retrieval, so a model that looks solid on mean scores may still drop retrievals depending on where content sits in the compression window. Engineers running DeepSeek or similar KV-compressed models on RAG or agent pipelines should test retrieval across phase positions rather than relying on aggregate accuracy. The arXiv paper (2609.36322) offers the evaluation methodology to replicate.
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。
FYAN introduces evidence-grounded semantic auditing that ensures formal statements preserve informal specifications through explicit proof-transfer obligations. Researchers working on formal verification and theorem proving can use this approach to improve accuracy in mathematical formalization, while developers can leverage ODENumLib, the 9,355-line Lean library for numerical analysis of ordinary differential equations that FYAN built.