DeepSeek-V4.1-Flash: more efficient prefill for coding agents

DeepSeek has released the open-weight DeepSeek-V4.1-Flash, a 552-billion-parameter multimodal mixture-of-experts model designed for coding agents that supports text and image inputs, text output, and a one-million-token context window, and it is available through Hugging Face and Baseten’s Model API. Although it has 552B total parameters, it activates 8B parameters during input prefill and 16B during output decoding through a Causal Encoder-Decoder architecture, which splits computation between encoder and decoder stages to reduce prefill costs and improve efficiency for agentic workflows with repeated context. DeepSeek reports that the model improves on earlier V4-Flash and V4-Pro models in coding, long-horizon agent tasks, and visual reasoning, while using fewer active parameters, though benchmark results indicate that human oversight remains advisable for complex automation and consequential image-based reasoning. Its KV cache is reported to require about one-quarter the memory of V4-Flash’s cache through architectural changes, compressed sparse attention, and lower-precision FP4 caching, potentially improving cache reuse, throughput, and time to first token. DeepSeek is replacing V4-Flash platform traffic with V4.1-Flash and plans to reroute V4-Pro traffic as well, while Baseten supports deployment using KV-cache-aware routing intended to send requests to replicas that already hold relevant context.

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