DeepSeek V4.1 Flash hands-on tests and comparisons

Project maya offloads experts and ngrams to 103.6GB of system RAM while KV cache, active experts and attention stay on 96GB of VRAM.

On the same rocket-launch countdown brief, Claude Opus 5.5 took 37 minutes and cost $5.15 while DeepSeek V4.1 Flash 1017 finished in 80 seconds for 2 cents, roughly 331x cheaper, with different visual styles.

A short take that DeepSeek V4.1 Flash 1017 is the best open-source model right now and is surprisingly close to frontier intelligence.

A Hugging Face repo hosts TensorFold-quantized DeepSeek-V4.1-Flash-1017 weights aimed at running locally on M5 Ultra hardware.

The post claims DeepSeek's inference-side hardware optimization lets a single GB300 rack, costing about $7M, serve OpenCode's 6-7T daily token usage, explaining free access on platforms.

On four DGX Sparks, DeepSeek V4.1 Flash 1017 runs 71.6 tok/s on default SGLang and 103.1 tuned, versus 110.9 on TensorFold; tuned SGLang also wins 131K cold prefill.

In a three.js test building an 18th-century Brown Bess flintlock from scratch, DeepSeek V4.1 Flash produced 91 parts versus 51 for Kimi K3 and 65 for Claude Opus 5.5.

Running DeepSeek V4.1 Flash locally on an RTX PRO 6000 96GB with mixed IQ1/IQ2 experts and speculative decoding gives about 70 tok/s on prose and 77 on code at 16K context.

The author repacked a GSQ-RCO quant into upstream GGUF format and ran DeepSeek V4.1 Flash 748B on one RTX PRO 6000 plus RAM at about 40 tok/s, with no fork or third-party engine.

Promotes a desktop coding agent with end-to-end encryption and hardware-enclave execution that can run uncensored DeepSeek V4.1 Flash 1017 for cyberdefense work.

Using a $2 Blender render as a hook, the post notes V4.1 Flash ships open weights under MIT at the same low price with better reasoning, coding and agents, and asks whether open weights are now the default.

A user says DeepSeek V4.1 Flash 1017 is the best open model available, especially given its relatively friendly size, and is now waiting for the next GLM.