Core Insights - DeepSeek officially launched the DeepSeek-V3.2-Exp model on September 29, introducing the self-developed DeepSeek Sparse Attention (DSA) mechanism, which optimizes training and inference efficiency for long texts [1][7] - The release of the new model has led to a significant reduction in service costs, with DeepSeek API prices dropping by over 50% [2][10] - The open-sourcing of the TileLang version operator has garnered considerable attention within the industry [3] Technical Innovations - The DSA mechanism is an optimization technique for the Transformer architecture, addressing the computational complexity associated with traditional dense attention mechanisms, which grow exponentially with text length [6][7] - The V3.2-Exp model has achieved substantial improvements in training and inference efficiency for long texts while maintaining performance levels comparable to the previous V3.1-Terminus model [7] Market Impact - DeepSeek has made the V3.2-Exp model fully open-source on platforms like HuggingFace and ModelScope, with related research papers also published [5] - The collaboration with domestic hardware providers such as Huawei, Cambricon, and Haiguang demonstrates the synergy between AI software and hardware ecosystems in China [11][12] - The adoption of TileLang, a programming language designed to simplify GPU operator development, is expected to enhance the efficiency of AI operator development significantly [12]

Core Viewpoint - DeepSeek has released its experimental version DeepSeek-V3.2-Exp, which significantly improves long text training and inference efficiency while maintaining output quality compared to its predecessor V3.1-Terminus [5][6]. Group 1: Model Performance - DeepSeek-V3.2-Exp introduces DeepSeek Sparse Attention (DSA), achieving a 2-3 times increase in long text inference speed and a 30%-40% reduction in memory usage, along with a 50% improvement in training efficiency [5]. - In benchmark tests, DeepSeek-V3.2-Exp performs comparably to V3.1-Terminus, with scores of 85.0 in MMLU-Pro and a slight improvement in AIME 2025, scoring 89.3 compared to 88.4 [5][6]. Group 2: Pricing Adjustments - Due to the reduced service costs associated with the new model, DeepSeek has lowered its API pricing by over 50%, with input prices dropping from 0.5 yuan to 0.2 yuan per million tokens for cache hits, and from 4 yuan to 2 yuan for cache misses. Output prices have decreased from 12 yuan to 3 yuan per million tokens [7].

Core Insights - DeepSeek has released the V3.2-Exp model, which introduces a Sparse Attention mechanism aimed at optimizing training and inference efficiency for long texts [1] - The official app, web version, and mini-program have all been updated to DeepSeek-V3.2-Exp, and the API has seen a significant price reduction [1] - Under the new pricing policy, the cost for developers to access the DeepSeek API will decrease by over 50% [1] - The performance of DeepSeek-V3.2-Exp on various public evaluation datasets is comparable to that of V3.1-Terminus [1]

Core Insights - The new pricing policy will reduce the cost for developers using the DeepSeek API by over 50% [2][3] - The release of the DeepSeek-V3.2-Exp model on September 29, 2025, introduces the DeepSeek Sparse Attention mechanism, enhancing training and inference efficiency for long texts [2] - The V3.2-Exp model maintains performance levels comparable to the previous V3.1-Terminus model across various benchmarks [2][3] Performance Comparison - In the MMLU-Pro benchmark, DeepSeek-V3.1-Terminus scored 85.0, while V3.2-Exp maintained the same score [3] - For the BrowseComp search benchmark, V3.2-Exp improved to 40.1 from 38.5 in V3.1-Terminus [3] - The Codeforces-Div1 benchmark saw an increase from 2046 in V3.1-Terminus to 2121 in V3.2-Exp [3] Accessibility and Development - The V3.2-Exp model has been made open-source on Huggingface and Modao platforms, allowing users to access and develop further [5] - The updated version is available on the official app, web, and mini-programs [2][3]

Core Insights - DeepSeek has released the DeepSeek-V3.2-Exp model, which is an experimental version aimed at transitioning to a new generation architecture [1] - The new model introduces DeepSeek Sparse Attention, focusing on optimizing training and inference efficiency for long texts [1] - The official app, web version, and mini-program have all been updated to DeepSeek-V3.2-Exp, and the API costs have been significantly reduced by over 50% for developers [1] - The performance of DeepSeek-V3.2-Exp on various public evaluation sets is comparable to that of V3.1-Terminus [1]

北京商报讯（记者 魏蔚）9月29日，深度求索正式发布 DeepSeek-V3.2-Exp 模型，在 V3.1-Terminus 的 基础上引入了 DeepSeek Sparse Attention（一种稀疏注意力机制），针对长文本的训练和推理效率进行 了探索性的优化和验证。目前，官方 App、网页端、小程序均已同步更新为 DeepSeek-V3.2-Exp，同时 API （应用程序编程接口）大幅度降价。在新的价格政策下，开发者调用 DeepSeek API 的成本将降低 50% 以上。 ...

Core Insights - DeepSeek has officially released and open-sourced the DeepSeek-V3.2-Exp model on the Hugging Face platform, with updates across its official app, web, and mini-programs [1] - The new model incorporates a sparse Attention architecture, which reduces computational resource consumption and enhances inference efficiency [1] - The introduction of the new model has led to a significant reduction in service costs, with API prices dropping by over 50% [1] Company Developments - On September 22, DeepSeek updated the DeepSeek-V3.1 model to the DeepSeek-V3.1-Terminus version, achieving a maximum single-item performance improvement of over 36% [3] - The API pricing for the previous version remained unchanged, with costs set at 0.5 yuan for 1 million tokens input and 12 yuan for output [3] - The rapid updates and enhancements in model performance are seen as crucial for retaining developers in a competitive open-source model landscape [3] Industry Impact - Analysts from Guosheng Securities highlight that DeepSeek's high performance, low cost, and innovative algorithms position it competitively against global standards, reshaping investor perceptions of Chinese tech companies' innovation capabilities [3] - The growth in AI model training and inference is expected to drive demand for AI computing power, benefiting various segments of the computing industry, including AI chips and servers [4]

Core Viewpoint - DeepSeek has released its new model DeepSeek-V3.2-Exp, which significantly reduces service costs and enhances model inference efficiency through a sparse Attention architecture [1][5]. Group 1: Model Updates - The DeepSeek-V3.2-Exp model was officially launched on September 29, with updates across its app, web, and mini-program platforms [1]. - The previous version, DeepSeek-V3.1, was updated to DeepSeek-V3.1-Terminus on September 22, showing a performance improvement of over 36% [5]. - The API pricing for DeepSeek has been reduced by over 50% due to the new model's cost efficiency [1]. Group 2: Market Impact - Following the release of DeepSeek-V3.1, there was a notable surge in stock prices for several domestic chip industry companies [5]. - Analysts highlight that the rapid updates and bug fixes in open-source models are crucial for retaining developers in a competitive landscape [5]. Group 3: Industry Insights - The AI computing demand is expected to grow due to large model training and inference, benefiting various segments of the computing industry, including AI chips and servers [6]. - DeepSeek's innovations and performance position it competitively against global leaders, reshaping investor perceptions of Chinese tech companies' capabilities [5].

Core Insights - DeepSeek officially released the experimental version DeepSeek-V3.2-Exp on September 29, which introduces a sparse attention architecture aimed at optimizing training and inference efficiency for long texts [1][2] - The new model has been integrated into various platforms including the official app, web, and mini-programs, with a significant reduction in API costs for developers [1] Group 1 - The DeepSeek-V3.2-Exp model builds on the V3.1-Terminus version and incorporates a fine-grained sparse attention mechanism called DeepSeek Sparse Attention (DSA), which enhances long text training and inference efficiency without compromising output quality [1] - The model is now available on Huawei Cloud's Model as a Service (MaaS) platform, utilizing a large EP parallel deployment scheme to optimize context parallel strategies while maintaining latency and throughput performance [1] Group 2 - The DeepSeek team conducted a rigorous evaluation of the impact of the sparse attention mechanism, ensuring that the training settings of DeepSeek-V3.2-Exp were aligned with V3.1-Terminus, resulting in comparable performance across various public evaluation datasets [2] - The introduction of the new model has led to a significant reduction in API service costs, with developer costs for accessing DeepSeek API decreasing by over 50% under the new pricing policy [2]

Core Insights - DeepSeek has officially launched the DeepSeek-V3.2-Exp model, with updates available on its official app, web platform, and mini-programs [1] - The new pricing policy significantly reduces the cost for developers using DeepSeek API by over 50% [1]