DeepSeekMath-V2

DeepSeek-V4.1-Flash

DeepSeek

DeepSeek-V4.1-Flash is a natively multimodal MoE model trained from scratch on 45 trillion tokens. It uses a causal encoder-decoder layout, Compressed Sparse Attention 2, Engram conditional memory, and DSpark speculative decoding to reduce inference and KV-cache cost. Its backbone activates different parameter counts during prefill and decoding, while a separate 196B conditional-memory table is sparsely accessed. The model accepts images and text and generates text, supports million-token contexts and adjustable reasoning effort, and powers the official deepseek-flash API. MIT weights and model-specific inference code support self-hosting.

DeepSeek-V4-Flash-Vision-Exp

DeepSeek

DeepSeek-V4-Flash-Vision-Exp is the experimental vision-language specialization of the production V4 Flash model. It adds image understanding for agent tasks while retaining the text capabilities of V4 Flash, enabling workflows that inspect interfaces, diagrams, and other visual context. It was publicly accessible through a dedicated DeepSeek API model name before downloadable MIT weights were released. Following the September 10 V4.1 Flash launch, the experimental endpoint was retired and its alias redirected; the original checkpoint remains available for self-hosted research.

DeepSeek-V4-Flash

DeepSeek

DeepSeek-V4-Flash is the smaller, efficiency-focused tier of the V4 family, first publicly released as a preview on April 24, 2026. Its hybrid Compressed Sparse Attention and Heavily Compressed Attention architecture supports million-token contexts alongside manifold-constrained hyper-connections. It supports non-thinking and thinking modes for code, knowledge, and agent tasks. The 2026-07-31 production update (DeepSeek-V4-Flash-0731) adds substantially stronger agent behavior and DSpark speculative decoding, and is included as a named checkpoint variant. The original Flash API has retired and its alias now routes to V4.1 Flash; original and updated V4 weights remain downloadable.