DeepSeek V4.1 Flash hands-on tests and comparisons

A post citing an analysis says 96–99% of agentic coding cost is cache reads, and price cuts come mainly from cheap open-weight models like DeepSeek V4.1 Flash plus competition among inference providers, with OpenAI and Anthropic cutting up to 80%.

Developer Erik Zhang open-sourced the Resin inference scheduler, claiming he built standalone engines for DeepSeek-V4.1-Flash and GLM-5.3-Flash on Blackwell in 12 hours; his own test puts V4.1 Flash 5–10% slower than DeepInfer with no throughput numbers, and the project is not production-grade.

OpenRouter data for Oct 19–25 shows V4.1-Flash-1017 first at 33.6T tokens, up 801% week over week, with Chinese models taking all top five spots.

A user runs DeepSeek-V4.1-Flash-1017 as EXL3 3.75bpw on TensorFold TP3 across three DGX Sparks, getting near-full weights, a stable 4M context with no OOM, and uses it as a daily driver with a full recipe.

Cursor says V4.1 Flash is now selectable in Settings > Models, with every effort level on the CursorBench cost frontier at $0.60–$0.98 per task and no model scoring higher at that cost.

Towards AI's ToneBench ranks GPT-6.1 Terra 23rd of 84 and concludes that for pure cheapness, GLM-5.3 Flash ($0.022) and DeepSeek V4.1 Flash 1017 ($0.014) write better for less, though they use 7-15x more tokens per script and run slower.

Signal65 reports the 1017 build hits the original's max-effort score at default effort with 45% fewer weighted errors and 20,829 vs 347,031 output tokens per correct answer, holding 312 concurrent agents on one 8×B300 node at $0.15 per correct task.

Using SGLang, TP4, dual RoCE rails and speculative decoding on four DGX Sparks, V4.1 Flash hits 142.4 tok/s on code, 126.7 tok/s at 64K context, and 71/75 on qeval.

Ternary quantization shrinks the 510 GB base model to a 142 GB pack, speeds decode ~12.1x at concurrency 1 and keeps ~92% of FP4 intelligence, which the author argues suits coding and research agents.

GPT-6.1 Terra scores 48 and Kimi K3.1 Pro leads at 55, while DeepSeek V4.1 Flash ties Qwen3.8 Max and GLM-5.3 at 45.

On Browser Use's own benchmark Kimi K3.1 Pro ranks second at 76.8 behind GPT-6.1 Sol's 78.9 and ahead of Opus 5.5, while DeepSeek V4.1 Flash scores 70.6 as the next best Chinese model at roughly $0.2 per task.

A home-hardware comparison finds DeepSeek-V4.1-Flash-1017 notably independent and meticulous, set against quantized GLM-5.3-Flash and Qwen3.8-Flash-Next builds.