DeepSeek-V3.2

DeepSeek releases V3.2, an open model that matches GPT-5 on reasoning benchmarks while introducing significant architectural and training innovations. The high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and achieves gold-medal performance in both the 2025 IMO and IOI competitions. - **DeepSeek Sparse Attention (DSA):** A new efficient attention mechanism that reduces computational complexity from O(L^2) to O(Lk) for the main model while preserving long-context performance. Implemented via a lightning indexer that selects top-k key-value entries per query token, achieving significant inference cost reductions at 128K context. - **Scalable RL framework:** Post-training compute now exceeds 10% of pre-training cost, using GRPO with unbiased KL estimation and off-policy sequence masking. Specialist models are trained separately for math, code, agents, and search, then distilled into the final checkpoint followed by mixed RL. - **Large-scale agentic task synthesis:** Generates over 1,800 synthetic environments and 85,000 complex prompts for RL training. Includes code agents (24K tasks from GitHub issue-PR pairs), search agents (50K synthesized queries), and general agents with automatically verifiable constraints. - **Thinking in tool-use:** Introduces context management for tool-calling scenarios that retains reasoning traces across tool calls until a new user message arrives. Cold-start training unifies reasoning and tool-use patterns within single trajectories. - **Benchmark results:** DeepSeek-V3.2-Thinking scores 93.1% on AIME 2025 and 73.1% on SWE-Verified. The Speciale variant achieves 96.0% on AIME 2025, 99.2% on HMMT Feb 2025, and gold medals in IMO 2025 (35/42), IOI 2025 (492/600), and ICPC World Finals 2025.