DeepSeek V4.1 Flash hands-on tests and comparisons

On ValsAI's finance benchmark, DeepSeek V4.1 Flash 1017 scores 4.7 points below Opus 5.5 while costing $0.42 per test run, about 4% of Opus's $11.67.

A 19-year-old developer used screen-recording tool REA to trace cursor tracking and auto-zoom behavior, then had DeepSeek V4.1 Flash rebuild and test the code, producing a working prototype in three hours.

Axiom Bio used Modal's training library to run 64k-context DeepSeek V4.1 Flash reinforcement learning on 64 B300 GPUs.

Opus 5.5 directed a six-model collaboration—Gemini 4 Argon, DeepSeek V4.1 Flash, Kimi K3, Codex 6 Astra, Grok 4.7 and Claude Opus 5.5—to produce a one-line sand-drawing and music video.

Post outlines V4.1-Flash-1017 specs (1M context, MIT weights, $0.15/$0.60 per 1M tokens) and private access via Ollama Cloud, claiming 20–30× cheaper coding-agent runs.

A short image post reports DeepSeek V4.1 Flash running at about 150 tok/s, without specifying hardware, quantization, or context length.

A detailed thread explains DeepSeek's open-source DeepInfer engine: FlashMLA, DeepGEMM and DeepEP merged under Apache 2.0, DSpark speculative decoding with a 5-token draft model, V4.1 Flash only for now, and no independent speed benchmarks yet.

AgentSpaceAI runs the same prompt through DeepSeek V4.1 Flash and Claude Opus 5.5: a pelican on a bicycle carrying 20 stacked objects, hitting a pothole and looping forever.

DeepInfer ships under Apache 2.0 for Blackwell and Ascend 950, claimed to serve 155T tokens/day per cluster, the same day DHS ordered agencies to remove all DeepSeek products.

Rauch says Vercel Token Pass now includes DeepSeek V4.1 Flash 1017, pitched as fast, extremely cheap and zero-data-retention, ideal for subagents executing plans from larger models.

Hands-on test runs the large-MoE DeepSeek V4.1 Flash on a V100 32GB, which the poster says makes it runnable on a roughly ¥350,000 machine previously needing a 256GB Mac or dual DGX Spark.

The free opencode deepseek-v4.1-flash-free tier is capped at 200K context and 32K output versus 1M/384K paid, which limits large repos.