DeepSeek V4.1 Flash hands-on tests and comparisons

Letta added DeepSeek V4.1 Flash to its FrontierMemory leaderboard, where it beats GPT-6.1 Sol and Gemini 4 Argon at a fraction of the cost, while Kimi K3.1 Pro ties Opus 5.5 and GLM-5.3 for first.

Same prompt to rebuild the Apple website: LFM3 ran locally for $0 while DeepSeek V4.1 Flash cost $0.07, and the author calls LFM3's output insane.

A user running DeepSeek 1017 locally reports peak single-stream decoding of 90 tok/s and says the local model beats the online version without quality degradation.

On CursorBench 4.0, DeepSeek V4.1 Flash scores 44.9% at $0.60 per task on Low up to 47.7% at $0.98 on Max, while Opus 5.5 Low gets 43.7% at $1.17.

A user reports running a 748B DeepSeek V4.1 Flash build locally on stock llama.cpp and links GSQ-RCO upstream GGUF quantized weights on Hugging Face.

The release enables DeepSeek V4.1 Flash on Ryzen AI Max Windows, speeds up DeepSeek prompt loading and multi-R9700 setups, adds selectable 512K/1M context (8K default) and image input on by default, and speeds up sub-12GB NVIDIA cards.

Local inference tool Project Maya ships v1.1.34, adding parallel requests for DeepSeek-V4.1-Flash and MiniMax-M3.1-Flash plus speed and stability fixes.

A user reports that V4.1 Flash 1017 optimized its own inference on H200s, yielding a 26% speedup in a single day, with a screenshot.

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.