DeepSeek V4.1 Flash hands-on tests and comparisons

A single prompt had V4.1 Flash 1017 build an interactive 3D West Lake sunset scene with an octagonal five-story pagoda, blended lake and hills, and switchable seasons, weather and time of day, which the author rates on par with Opus 5.5.

A user says V4.1 Flash 1017 got slower as users grew, but praises its Q2 quantization for running locally at practical speed on ~¥2M hardware (Q4 ~¥4M), with strong memory savings.

The author ran the same prompt through DeepSeek V4.1 and Opus 5.5; DeepSeek finished first, but that was its only win.

The author attributes the DEX drop to Iran news, not DeepSeek, and says V4.1 Flash 1017 is merely cheaper—more mistakes and slower at work than V4 Flash 0731, with no reason to switch from Opus 5.5.

The author explains local LLMs are not a subscription replacement but a way to host uncensored models and avoid shared compute, with fast DeepSeek V4.1 Flash 1017 as a main draw.

The author had Grok craft a test to check whether DeepSeek-v4.1-flash-1017 is a distillation of GPT Astra, and shares Grok's first-round answer as an image.

A Huatai analysis says open-weight releases get simplified automatic licensing and API/app services are outside controls, noting DeepSeek V4.1 Flash 1017 shipped via the open-source path.

A reply argues that if V4.1 Flash 1017 solves complex tasks with fewer output tokens while keeping code quality, inference costs and agent-loop efficiency would improve.

The October 17 roundup lists DeepSeek V4.1-Flash-1017 with MIT-licensed weights and a reported 77% on DeepSWE, among other items on TypeSafe, vLLM and OpenCode.

Design Arena announces DeepSeek-V4.1-Flash-1017 is available on its platform, describing RL with self-feedback for game and 3D tasks plus faster completion and shorter outputs.

A user says a friend used V4.1 Flash 1017 to code-generate a 130-second motion-design intro video in 26 minutes, sharing the prompt and calling it cheap and strong at motion design.

A user notes a Dwarfstar (ds4) GGUF build is on Hugging Face, warning that open models get repackaged per runtime and unknown conversions should be hash-checked first.