DeepSeek V4.1 Flash

Geeky Gadgets details local-deployment requirements for DeepSeek V4.1 Flash, citing its 552-billion-parameter MoE design that activates only 8–16 billion parameters per token. A 2-bit compressed build reportedly fits a 128 GB Mac Studio, while full-precision use demands high-end GPUs like the Nvidia RTX Pro 6000 priced above $32,000. Token throughput varies sharply by hardware, from 16–18 tokens per second for writing on modest setups up to 716 tokens per second for reading on high-end configurations. SSD streaming offers a lower-cost but slower alternative for local operation. The piece frames the privacy and offline benefits of local hosting against the practical cost advantages of cloud serving.

DeepSeek unveiled DeepSeek-V4.1-Flash, a Mixture-of-Experts model with 552 billion backbone parameters, 8 billion active on input and 16 billion active on output. The new model sharply reduces both active parameters and KV-cache size relative to V4-Pro while posting agentic and coding performance ahead of that flagship on several benchmarks. DeepSeek announced it will consolidate its existing V4-Pro API service into V4.1-Flash, making the new model a practical successor for production traffic. At maximum reasoning effort, V4.1-Flash outperformed GPT-5.6 Sol and Claude Opus 5 on Terminal-Bench 2.1, with additional claimed advantages on DeepSWE v1.1 and AutomationBench. Outside agentic and coding tasks, however, its relative edge is less pronounced.

DeepSeek launched DeepSeek-V4.1-Flash with a 552-billion-parameter mixture-of-experts backbone, native vision, and a 1-million-token context window. The model ships with open weights under an MIT License on Hugging Face, enabling commercial use by developers and enterprises. Its architecture is designed to make repeatedly reading large contexts cheaper for long-running workflows. DeepSeek prices V4.1-Flash at $0.003 per million input tokens on cache hits during off-peak hours, with off-peak cache-miss input at $0.15 and output at $0.60 per million. Peak hours roughly double those figures, with off-peak defined as outside DeepSeek's established peak windows on weekdays. The model also benchmarks ahead of GPT-5.6 Sol and Claude Opus 5 on key agentic and coding tests, sharpening the cost-versus-performance tradeoff for agent builders.

DeepSeek V4.1 Flash, released September 10, 2026, introduces a Causal Encoder-Decoder architecture that cuts KV cache requirements by roughly 4× relative to V4 Flash and 437× versus DeepSeek V1. The model carries 552B backbone parameters plus a 196B Engram conditional memory module, while activating only 8B parameters during prefill and 16B during decode. This asymmetric activation targets cost efficiency on long, input-heavy agent loops. The release is positioned as the first Flash-tier model that fully replaces a Pro-tier predecessor, outperforming V4 Pro across measured benchmarks despite one-third the total parameters and one-sixth the active parameters. Architectural components include Compressed Sparse Attention 2 with Full, Reindex, and Reuse modes, FP4 E2M1 KV cache compression, SWA Bounded Replay, and Single-Pass mHC residual connections. Native multimodal input is handled through a DeepSeek-ViT vision encoder integrated into the same backbone.

LLM Stats ranks DeepSeek-V4.1-Flash 14th on its composite leaderboard and places it in the top 2% for tool calling, with strong coding (7 of 274) and reasoning (18 of 370) standings. The aggregator reports a blended price of $0.24 per million tokens versus an LLM Stats Score of 51.3, framing the model in the cost-efficiency frontier. Per-benchmark scores sourced from the Hugging Face model card include a CodeForces rating of 3471.00 at maximum reasoning effort and a GPQA Diamond Pass@1 of 0.91. Terminal-Bench 2.1 results also appear in the scorecard, aligning with DeepSeek's claims of agentic strengths. These figures give developers a third-party benchmark anchor against frontier alternatives.

OpenRouter describes DeepSeek V4.1 Flash as a sparse mixture-of-experts model built on DeepSeek's new Causal Encoder-Decoder (CED) architecture, activating 8B parameters on input and 16B on output from a 552B-parameter backbone. Visual and text embeddings are trained jointly from pre-training start, and compressed KV caching cuts cache memory to roughly a quarter of the prior Flash generation. The model targets coding, terminal, and computer-use agents as well as long-horizon tasks. The listing confirms a 2026-09-10 release date and 1.0M token context, citing $0.099 per 1M input tokens and $0.60 per 1M output tokens as the headline pricing tier. DeepSeek positions V4.1 Flash as the cost-efficient tier of the V4.1 family, reporting it exceeds V4 Pro in performance, speed, and task completion time.

DeepSeek officially released DeepSeek-V4.1-Flash on 2026-09-10 as the smallest model in its new architecture family, featuring native multimodal visual understanding and a 1M-token context. The API now uses the model name "deepseek-flash," and previous V4 Flash and V4 Flash Vision Exp variants have been retired, with their old names temporarily routed to V4.1 Flash for compatibility. Benchmark scores reported by DeepSeek include GPQA Diamond 90.9, Codeforces rating 3471, Terminal-Bench 2.1 at 90.6, DeepSWE v1.1 at 74.2, and HLE with tools at 63.9. DeepSeek notes API pricing has been reduced alongside the launch, and DeepSeek V4 Pro API services will continue beyond September 14, 2026 with unchanged billing.