人工智能专题：后R1时代，DeepSeek发展的三大阶段

Investment Rating - The report maintains an "Outperform" rating for the computer industry, indicating an expected increase of over 10% relative to the CSI 300 index in the next six months [41]. Core Insights - DeepSeek has gained significant attention since the release of its R1 model earlier this year, and it has since focused on incremental updates rather than launching a more advanced R2 model. The development is categorized into three main stages: performance enhancement, hybrid reasoning architecture implementation, and cost reduction with accelerated domestic adaptation [7][10]. - The introduction of the V3.2-Exp model has led to a substantial reduction in API calling prices, with input cache hit prices dropping to 20% of R1's cost and output prices to 19%, enhancing the model's cost-effectiveness and market competitiveness [33][34]. Summary by Sections Stage One: Performance Enhancement - In March, DeepSeek launched V3-0324 and in May, R1-0528, which improved model capabilities through post-training, bridging the gap with leading models [11][12]. Stage Two: Hybrid Reasoning Architecture and Agent Capability Enhancement - From August onwards, DeepSeek aligned with global trends by releasing V3.1 and V3.1-Terminus, significantly enhancing agent capabilities and reasoning efficiency through extensive training on the DeepSeek-V3.1-Base model [12][18]. Stage Three: Efficiency Improvement and Domestic Adaptation Acceleration - The V3.2-Exp model, released in September, introduced a new attention mechanism (DSA) that improved training and reasoning efficiency while significantly lowering costs. This model also marked a milestone in the domestic AI industry, achieving zero-day adaptation with domestic chips from Huawei and Cambrian [31][34].