DeepSeek V4.1 Flash hands-on tests and comparisons

V4.1 Flash 1017 reaches 222 tok/s on four DGX Sparks, though the author notes this edge case relies on capping max tokens at 10–200.

A user criticizes Anthropic's safety filters: Opus 5.5 gets 0.8% on CyberGym with ~98% of tasks blocked, while open-weight DeepSeek V4.1 Flash 1070 reaches 22.9%; on CyberBench it's 55% vs 74%.

A user shows the 763B DeepSeek V4.1-Flash-1017 running on 3x Tesla V100 32GB plus DDR4, noting prefill speed will be improved next.

Using the same prompt and settings as a quoted post, DeepSeek-v4.1-Flash-1017 produced a markedly different 3D world, shown in a video.

A Turkish user shares a workflow where Claude Code delegates all coding to opencode-go/deepseek-v4.1-flash-max effort while they handle UI and review.

A Russian-speaking user reports ±30s latency swings on identical prompts, JSON schema violations with reasoning leaking into output, and calls it unusable for agents.

On the same GB10 boxes and engine, DeepSeek V4.1 Flash goes from 67 to 140.9 tok/s scaling one to eight chats, versus GLM 5.3 Flash at 88.1 to 177.2.

The author published a lecore-vq10sh12 GGUF quant of DeepSeek-V4.1-Flash-1017, claiming agent workloads can run at home without a 128GB machine, while plugging prebuilt rigs.

A developer says he is quiet because he is working on running DeepSeek V4.1 Flash 1017 on 128GB and 192GB VRAM configurations, with no results yet.

The same author reposts the GGUF repo for DeepSeek-V4.1-Flash-1017, stressing local agent use on modest hardware and selling $5006 machines.

Hands-on: DeepSeek Harness with ds4 on M3 Ultra produced a 0-shot Commodore 64 karate game in ~4 hours, 48.5M tokens, 99% cache hit, 43 t/s decode.

Spec rundown: 1017 is a Pareto update beating GLM 5.3 and Kimi K3 on every benchmark, with a 552B multimodal MoE, 1M context, adjustable reasoning effort and ~890 bytes/token KV.