This podcast introduces DeepSeek-V3.2, a novel open Large Language Model engineered to balance high computational efficiency with cutting-edge reasoning and agent capabilities, aiming to reduce the performance gap with frontier proprietary systems. A core technical innovation is the implementation of DeepSeek Sparse Attention (DSA), an efficient mechanism that substantially reduces computational complexity for long-context sequences without sacrificing performance. The model was trained using a robust, scalable Reinforcement Learning framework and a large-scale agentic task synthesis pipeline designed to enhance generalization in complex tool-use scenarios. Standard variants of DeepSeek-V3.2 demonstrate performance comparable to GPT-5 on reasoning benchmarks and significantly improve upon existing open models in diverse agentic evaluations. Furthermore, the high-compute variant, DeepSeek-V3.2-Speciale, achieved performance parity with Gemini-3.0-Pro and secured gold-medal status in the 2025 International Mathematical Olympiad and Informatics Olympiad. The authors ultimately conclude that despite these achievements, future work must focus on closing remaining gaps in world knowledge and improving token efficiency.