Seek .(US:SKLTY) 3 6 Ke·2025-09-29 12:12Core Insights - DeepSeek-V3.2-Exp model has been officially released and open-sourced, featuring significant updates in architecture and efficiency [1][4] - The introduction of DeepSeek Sparse Attention (DSA) aims to enhance training and inference efficiency for long texts without compromising output quality [1][5] - The API costs for developers have been reduced by over 50% due to the new model's service cost decrease [4] Group 1: Model Features - DeepSeek-V3.2-Exp is an experimental version that builds on V3.1-Terminus, incorporating a sparse attention mechanism [1] - The model achieves fine-grained sparse attention, significantly improving long text training and inference efficiency [1] - The new model's performance is comparable to V3.1-Terminus across various public evaluation datasets [5] Group 2: Development and Implementation - The development of the new model required the design and implementation of numerous new GPU operators, utilizing TileLang for rapid prototyping [2] - The open-sourced operators include both TileLang and CUDA versions, with a recommendation for the community to use the TileLang version for easier debugging [2] Group 3: Previous Versions and Improvements - DeepSeek-V3.1 was released on August 21, featuring a mixed inference architecture and improved efficiency compared to DeepSeek-R1-0528 [4] - The subsequent update to DeepSeek-V3.1-Terminus on September 22 addressed user feedback, enhancing language consistency and agent capabilities [4]