2026-10-19 11:28 UTC · deepseek/news · live page
Summary by fallback. It may be wrong; the diff below is what actually changed. Raw: 2c85c5ad3d-deepseek-news.diff · commit 2c85c5ad3d
diff --git a/snapshots/deepseek/news.md b/snapshots/deepseek/news.mdindex cad80a1..3f871aa 100644--- a/snapshots/deepseek/news.md+++ b/snapshots/deepseek/news.md@@ -3,4 +3,28 @@ # Change Log +DeepSeek's web and API search capabilities are powered by our in-house DeepSeek search engine, which reliably handles hundreds of millions of search requests from around the world every day. DeepSeek Search is an AI-native general-purpose search engine that indexes hundreds of billions of web pages, supports multilingual search, and returns highly relevant, authoritative, and timely results in real time. It filters SEO spam, GEO manipulation, and low-quality AIGC through strict detection mechanisms, ensuring authoritative and trustworthy information for users and LLMs. For usage details, see the Search & Fetch guide.++DeepSeek V4.1 Flash has been upgraded to DeepSeek-V4.1-Flash-1017. The official web and APP models have also been updated to the new version. The API calling method and pricing remain unchanged — simply use `deepseek-flash` to access the latest model.++Performance–cost Pareto improvement++Compared with the previous DeepSeek V4.1, the new model delivers substantial improvements across benchmarks:++- HLE (w/o tools): 42.6 (44.0*)+- Terminal-Bench 4.0: 42.3+- DeepSWE v1.1: 77.0+- ProgramBench: 29.1+- NL2Repo-Bench: 68.2+- Automation-Bench v1.0.6: 60.5+- Agents' Last Exam: 35.4+- Apex Bench v1.1: 72.1+- Chartography (w/tools): 83.0+- BabyVision (w/tools): 92.5+- ZeroBench-main (w/tools): 51.0++* Tested only on the pure-text subset of the HLE benchmark set. Terminal-Bench 4.0 was tested with the Claude Code framework, and DeepSWE with the Mini-SWE-V2 framework. ZeroBench-main was evaluated with Pass@5.++While improving performance, we also reduced the model's task runtime and output length, enabling users to complete tasks at lower cost and higher speed and achieving a new performance–cost Pareto optimum across multiple harnesses. We specifically optimized for DeepSeek Harness, Tencent CodeBuddy, WorkBuddy, and other platforms. Please upgrade to DeepSeek Harness 0.2.1-rc.1 or later for the best experience with the new model.+ Today, we officially release the DeepSeek-V4.1-Flash model. It is the smallest model in our new architecture family, with native multimodal visual understanding. The new architecture is designed for a higher capability ceiling, faster inference, higher throughput, and scaling to larger models.