DeepSeek releases V4.1-Flash-1017, a new version of its open-weight V4.1 Flash model

DeepSeek's V4.1-Flash-1017 promises shorter outputs and faster task completion, and its weights are published on Hugging Face and ModelScope.

FUFerhat UğurluYazSis TeamPublished: October 17, 20262 min read
In short

On 17 October 2026 DeepSeek released DeepSeek-V4.1-Flash-1017. The company says the new version scores higher on benchmarks, finishes tasks faster and writes shorter outputs than the previous one. It is available on DeepSeek's website, app and API, and the weights are on Hugging Face and ModelScope.

AI company DeepSeek announced DeepSeek-V4.1-Flash-1017 on 17 October 2026 on the news page of its API documentation. The company says the new version performs better on benchmarks, completes tasks faster and produces shorter outputs than the previous release. According to the API docs, the deepseek-flash model name now points to this version.

  • The model was trained with reinforcement learning from self-feedback for end-to-end project work, from game development to 3D modeling.
  • It is optimized for DeepSeek Harness, Tencent CodeBuddy and WorkBuddy.
  • Weights are on Hugging Face and ModelScope as deepseek-ai/DeepSeek-V4.1-Flash-1017; the announcement does not state a license.
  • The Search and Fetch APIs are now generally available, priced at 0.75 dollars per 1,000 calls for each endpoint.

Why it matters

Open-weight models can let companies run a model on their own servers and test it without sending data outside. That could be relevant for software teams in Türkiye looking for low-cost options for code generation and internal tools. However, the announcement gives neither a license nor a token price; before commercial use, teams should read the license on the model card and, if they plan to use the DeepSeek API, check where data is processed and how KVKK rules on transfers abroad apply.

Sources

FU
Ferhat Uğurlu · YazSis Team

Builds enterprise software, AI and digital transformation products together with the YazSis team.