DeepSeek-V4.1-Flash: Frontier Agents on a Smaller Memory Budget

DeepSeek-V4.1-Flash: Frontier Agents on a Smaller Memory Budget Paper Model A lot of recent progress in language models has focused on better reasoning, larger reinforcement-learning runs, and increasingly sophisticated agent training. One of the many remaining bottlenecks is the context. Coding agents, research agents, and tool-using systems repeatedly process hundreds of thousands of tokens while generating comparatively little output. At that point, prefill compute and the KV cache become the most important infrastructure problems. DeepSeek-V4.1-Flash is essentially an attempt to redesign the Transformer around that workload. Its 552B-parameter MoE backbone uses a new Causal Encoder-Decoder (CED) architecture, activating only about 8B parameters per token during prefill and 16B during decoding. Compressed Sparse Attention 2 (CSA2) shares KV representations and sparse-attention decisions across layers; SWA Bounded Replay avoids persistently storing sliding-window KV states; and FP4…

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