降价！DeepSeek，大消息！

Core Insights - DeepSeek has officially released the DeepSeek-V3.2-Exp model, which introduces the DeepSeek Sparse Attention mechanism to enhance training and inference efficiency for long texts [1][3] - The performance of DeepSeek-V3.2-Exp is comparable to its predecessor, DeepSeek-V3.1-Terminus, across various benchmark datasets [3][4] - The official app, web version, and mini-program have been updated to DeepSeek-V3.2-Exp, with a significant reduction in API costs by over 50% for developers [4] Model Performance - DeepSeek-V3.2-Exp maintains similar performance levels to DeepSeek-V3.1-Terminus in several benchmarks, such as MMLU-Pro (85.0), GPQA-Diamond (79.9), and SimpleQA (97.1) [4] - Notable improvements were observed in the BrowseComp and Codeforces-Div1 benchmarks, with scores of 40.1 and 2121 respectively for V3.2-Exp [4] Recent Developments - DeepSeek has been active recently, with the release of DeepSeek-V3.1 on August 21, which marked a step towards the "Agent era" with enhanced reasoning capabilities and efficiency [8] - A research paper on the DeepSeek-R1 reasoning model was featured on the cover of the prestigious journal Nature, highlighting significant advancements in AI technology from China [8][9] - Nature's editorial praised DeepSeek for breaking the gap in independent peer review for mainstream large models, marking a milestone for Chinese AI research [9]