DeepSeek released V4.1-Flash on September 14, 2026, introducing a model built around one central goal: making long-context artificial intelligence cheaper to run. The Chinese artificial intelligence startup redesigned both the model architecture and the inference stack, combining several changes that reduce the amount of computing and memory needed during use.
The result is a 552B-parameter Mixture-of-Experts model with a 1-million-token context window. That size sounds enormous, but V4.1-Flash does not activate every parameter for every token. It uses 8B active parameters per token during prefill and 16B during decoding, while supporting both text and images.
A Large Model With a Smaller Working Footprint
Mixture-of-Experts, or MoE, lets a model contain many parameters without using all of them for every part of a request. In V4.1-Flash, the full model contains 552B parameters, but the active count stays at 8B during prefill and rises to 16B during decoding. That split gives the model different operating costs at different stages of handling a prompt.
Prefill is the stage where the system processes the incoming context. Decoding follows as the model produces its response. By assigning different active parameter counts to those stages, DeepSeek has designed V4.1-Flash around the way a long prompt and an answer place different demands on the system.
The model also introduces a Causal Encoder-Decoder architecture with a 20-layer encoder and a 20-layer decoder. This design sits alongside MoE, cheaper prefill, and efficient decoding as part of DeepSeek’s broader effort to reduce the cost of long-context AI.
The model was trained from scratch on 45 trillion multimodal tokens and includes a separate 196B-parameter Engram conditional-memory component. That component is part of the model’s architecture, adding another dedicated system for handling information within the broader 552B-parameter design.
Why the KV Cache Matters
Long-context models need memory to keep track of the information they have already processed. That memory is stored in a key-value, or KV, cache. As the context window grows, the cache can become a major part of the cost of running the model.
V4.1-Flash reduces its global KV cache to 890 bytes per token. For a full 1-million-token context, that equals roughly 890 MB of global KV data. The figure gives the model a way to support a very long context without requiring a much larger cache for every token.
DeepSeek says the global KV cache is roughly one-quarter the size of V4-Flash’s. The reduction comes from CSA2, which cuts duplication in the KV information. Instead of carrying the same information in repeated forms, the system is designed to store it with less duplication.
That change matters because the context window and the cache are tied together. A 1-million-token window gives the model room to process a large amount of information, but the practical cost depends on how much memory the system needs to retain while doing so. V4.1-Flash addresses that cost directly through cache compression and the rest of its inference design.
What the Release Signals
V4.1-Flash brings several efficiency ideas into one open-source model release: Causal Encoder-Decoder architecture, MoE routing, KV cache compression, CSA2, cheaper prefill, and efficient decoding. Each part targets a different pressure point, from the parameters active for each token to the memory needed for a million-token context.
The architecture also separates the model’s full parameter count from the amount used at each step. That distinction helps explain why a 552B-parameter model can focus on lower active counts during operation while still supporting a wide range of text and image inputs.
DeepSeek’s release arrives as the company prepares for an initial public offering on Shanghai’s STAR Market. The model therefore represents both a technical release and a clear statement about the startup’s approach: build large systems, then redesign the path from input to output so those systems cost less to run.
For developers and organizations working with long-context AI, the key details are practical. V4.1-Flash offers a 1-million-token context window, 890 bytes of global KV data per token, and separate active-parameter counts for prefill and decoding. Together, those choices show how an open model can pursue scale without treating every parameter and every stored token as an equal operating expense.
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