Overview

DeepSeek-V4.1-Flash processes images and text natively and generates text. It uses a Causal Encoder-Decoder (CED) architecture, a 40-layer Transformer split into a 20-layer causal encoder and a 20-layer decoder, so that the decoder’s KV cache is projected from the encoder. DeepSeek says this lets the model activate only 8B parameters per token while compressing the KV cache heavily.

Highlights

  • 552B backbone parameters, 8B active per token
  • Native image understanding with a DeepSeek-ViT encoder trained from scratch
  • Compressed Sparse Attention 2 and Engram conditional memory
  • One-million-token context window
  • MIT license

Typical uses

Long-context and multimodal assistants, document understanding and cost-sensitive API workloads.

Things to consider

  • The architecture is new and support in local inference tools may lag.
  • Requires multi-GPU servers to self-host.

Specifications are taken from the model card on Hugging Face and the provider’s announcement. Benchmark results depend on evaluation settings; see how AI benchmarks work.