<p>detailed overview of the <strong>DeepSeek-V3.2</strong> large language model, positioning it as an open-weight solution specifically engineered for agentic workloads. Its key architectural innovation is <strong>DeepSeek Sparse Attention (DSA)</strong>, which efficiently manages extremely long 128K context windows by only attending to a small, relevant subset of tokens, dramatically reducing computational costs from O(L²) to O(L·k). The model also relies on <strong>scaled reinforcement learning</strong> and extensive <strong>agentic task synthesis</strong> to enhance reasoning and generalization, addressing historical weaknesses in open models regarding robust agent behavior. Operationally, the model is designed to be economically disruptive, with its release tied to <strong>50%+ API price cuts</strong>, enabling developers to run complex, long-horizon agent loops that were previously too expensive.</p>