DeepSeek V4.1 Flash hands-on tests and comparisons

A user ran head-to-head real-world agent tasks between Ornith 1.5 122B EXL3 on four Intel B70s and DeepSeek V4.1 Flash 1017 EXL3 on two Sparks.

A four-model volcano rendering comparison (Kimi K3.1 Pro, GPT-5 Astra Max, DeepSeek-V4.1-Flash Max, GLM-5.3-Flash Max) finds Kimi's 3D modeling notably more detailed, with per-model costs listed.

A weekly roundup notes DeepSeek open-sourced its in-house DeepInfer inference framework on 10/20 for serving V4.1 Flash at scale, alongside four trending GitHub projects for auditing, agent management and code graphs.

The author compared four models on Volcano, calling Kimi K3.1 Pro the most detailed with the biggest 3D modeling gain over Astra Max, and listed each model's cost.

Using the API with max thinking, the author produced a single-shot, single-turn explosion and destruction simulation, praising the 3D builds with no harness involved.

A user says the 1017 update clearly improves reasoning, coding and agent tasks with shorter outputs and faster completion, arguing it covers most daily work without a flagship model, and promotes a Hilinkup discount.

In a legacy-code bug-fix benchmark, DeepSeek V4.1 Flash 1017 solved the hardest bug only on the last round — the same as Opus 5.5 — despite being a small flash model.

Kernel work on four CMP 170HX cards yields roughly 16% faster code and 23% faster prose from a new attention and drafter, plus about 9% cumulative bit-exact gains, with benchmarks linked.

Building an interactive 3D V8 engine lab in Three.js, DeepSeek V4.1 Flash used 67.2M tokens (~$0.86) while Kimi K3.1 Pro used 3.5M (~$2.16) and produced a more polished interface.

A user plans to run DeepSeek V4 Flash 1017 with Strata and GLM-5.3 Flash with Project Maya on wumbojets to gauge how much it will cost over a week.

An economics comparison putting V4.1 Flash at $38 revenue per GB200 GPU-hour versus Kimi K3.1 Pro at $8.34 per MI355X GPU-hour, arguing its Engram, causal encoder-decoder and KV reuse architecture will be widely adopted.

A 763B MoE compressed for local runs via non-uniform quantization; the GSQ-RCO build balances size and accuracy and ships as GGUF.