DeepSeek v4.1 Flash (1017)

DeepSeek V4.1 Flash is DeepSeek's efficiency-oriented multimodal Mixture-of-Experts model, and this catalog entry is the 1017 revision of that release. It is a sparse MoE model with a 552B-parameter backbone that accepts interleaved text and images and generates text, with a context window of roughly one million tokens.

The headline change versus DeepSeek V4 Flash 0731 and DeepSeek V4 Flash 0423 is architectural. V4.1 Flash is built on a Causal Encoder-Decoder design: 40 transformer layers split into a 20-layer causal encoder and a 20-layer decoder, where the decoder's global KV cache is projected from the encoder's final hidden states instead of computed layer by layer. That asymmetry means only about 8B parameters activate per input token and 16B per output token. Vision is also native here, with a from-scratch encoder trained alongside text rather than bolted on afterwards.

Compared with the much larger DeepSeek V4 Pro, V4.1 Flash trades total capacity for far lower per-token compute while keeping the million-token window. DeepSeek's own reported instruct-model evaluations include 90.9 on GPQA Diamond and 90.6 pass@1 on Terminal-Bench 2.1. Reasoning effort is continuously adjustable on a 1-to-100 scale, and the model supports function calling and web search. Weights are published openly under the MIT license. For a higher-capacity option, see DeepSeek V4 Pro 0813.

Sources

DeepSeek-V4.1-Flash· build.nvidia.comdeepseek-ai/DeepSeek-V4.1-Flash · Hugging Face· huggingface.co

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