DeepSeek新模型降价：优化推理效率，API价格降超50%

Core Insights - DeepSeek has launched the new DeepSeek-V3.2-Exp model, which significantly reduces API costs by over 50% [2][3][4] Group 1: Model Release and Features - The DeepSeek-V3.2-Exp model is an experimental version that builds on the previous V3.1-Terminus, introducing the DeepSeek Sparse Attention mechanism to enhance training and inference efficiency for long texts [3][4] - The new model maintains performance levels comparable to V3.1-Terminus across various public evaluation datasets, despite the introduction of the sparse attention mechanism [4] Group 2: Cost Reduction and Pricing - The introduction of the new model has led to a substantial decrease in service costs, with API pricing dropping by more than 50%. Specific price changes include input cache hits reduced from 0.5 yuan to 0.2 yuan per million tokens, cache misses from 4 yuan to 2 yuan per million tokens, and output costs from 12 yuan to 3 yuan per million tokens [4] Group 3: Research and Development - The development of the DeepSeek-V3.2-Exp model involved designing new GPU operators and utilizing the TileLang programming language for rapid prototyping, which supports deeper exploration of model capabilities [4] - DeepSeek's research on the DeepSeek-R1 model, which focuses on incentivizing reasoning capabilities in large language models through reinforcement learning, was featured on the cover of the prestigious journal Nature [7]