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-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]

DeepSeek updated an experimental AI model Monday in what it called a step toward next-generation artificial intelligence https://t.co/jGu6RIkgj9 ...

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]

Core Viewpoint - DeepSeek has launched the DeepSeek-V3.2-Exp model, which introduces a Sparse Attention mechanism to enhance training and inference efficiency for long texts, while maintaining output quality similar to its predecessor, V3.1-Terminus [2][4]. Group 1: Model Performance - The DeepSeek-V3.2-Exp model shows comparable performance to the V3.1-Terminus across various benchmark datasets, with specific scores indicating slight variations in certain areas [5]. - In the MMLU-Pro benchmark, both models scored 85.0, while in the General GPQA-Diamond benchmark, V3.1 scored 80.7 and V3.2-Exp scored 79.9 [5]. - The Sparse Attention mechanism has led to significant improvements in training and inference efficiency without compromising model output [4]. Group 2: Recent Developments - DeepSeek has been active recently, with the V3.1-Terminus model being released on August 21, which introduced a hybrid reasoning architecture and improved efficiency and agent capabilities [8]. - A research paper on the DeepSeek-R1 reasoning model was published in the prestigious journal Nature, marking a significant achievement for Chinese AI research [8][9]. - The new pricing policy for the DeepSeek API has reduced costs for developers by over 50%, making it more accessible [4].

Core Insights - DeepSeek, known as the "price butcher," has significantly reduced its pricing for the newly released DeepSeek-V3.2-Exp model, with output prices dropping by 75% and overall API costs for developers decreasing by over 50% [1][3]. Pricing Changes - Input pricing for DeepSeek-V3.2-Exp has been adjusted: - Cache hit price decreased from 0.5 yuan per million tokens to 0.2 yuan per million tokens - Cache miss price reduced from 4 yuan per million tokens to 2 yuan per million tokens - Output pricing has been slashed from 12 yuan per million tokens to 3 yuan per million tokens [3]. Model Performance and Features - The V3.2-Exp model is an experimental version that introduces DeepSeek Sparse Attention, enhancing training and inference efficiency for long texts without compromising output quality [3][6]. - Performance evaluations show that DeepSeek-V3.2-Exp maintains comparable results to the previous V3.1-Terminus model across various public benchmark datasets [3][4][5]. Community Support and Open Source - DeepSeek has open-sourced GPU operators designed for the new model, including TileLang and CUDA versions, encouraging community research and experimentation [6]. - The model is now available on platforms like Huggingface and has been updated across official applications and APIs [5][6]. Industry Context - Following the recent release of DeepSeek-V3.1-Terminus, there is speculation about the future of the V4 and R2 versions, with industry voices expressing anticipation for major updates [6].

Group 1 - DeepSeek has launched the DeepSeek-V3.2-base model on Huggingface as of September 29 [1] - Zhiyu's next-generation flagship model GLM-4.6 is set to be released soon, with the current flagship model GLM-4.5 available on Z.ai's official website [1]

Core Viewpoint - DeepSeek has launched its updated model DeepSeek-V3.2-Exp, which significantly reduces API costs for developers by over 50% due to lower service costs associated with the new model [1][9]. Model Release and Features - The DeepSeek-V3.2-Exp model was officially released on September 29 and is available on the Hugging Face platform, marking an important step towards the next generation architecture [3]. - This version introduces the DeepSeek Sparse Attention (DSA) mechanism, which optimizes training and inference efficiency for long texts while maintaining model output quality [5][8]. - The model supports a maximum context length of 160K, enhancing its capability for handling extensive data [4]. Cost Structure and API Pricing - The new pricing structure for the DeepSeek API includes a cost of 0.2 yuan per million tokens for cache hits and 2 yuan for cache misses, with output priced at 3 yuan per million tokens, reflecting a significant reduction in costs for developers [9]. Open Source and Community Engagement - DeepSeek has made the DeepSeek-V3.2-Exp model fully open source on platforms like Hugging Face and ModelScope, along with related research papers [11]. - The company has retained API access for the previous version, V3.1-Terminus, to allow developers to compare performance, with the same pricing structure maintained until October 15, 2025 [11]. Upcoming Developments - There are indications that the new model GLM-4.6 from Z.ai will be released soon, which is expected to offer greater context capabilities [15][16].

Core Insights - DeepSeek has launched the DeepSeek-V3.2-Exp model on Hugging Face, introducing the DeepSeek Sparse Attention (DSA) mechanism to enhance training and inference efficiency for long texts [1][3] - Huawei Cloud has adapted the DeepSeek-V3.2-Exp model, supporting a maximum context length of 160K [2] - The DSA technology significantly improves training and inference efficiency for long text scenarios with minimal impact on model output [3] - The training settings of DeepSeek-V3.2-Exp were strictly aligned with the previous version, V3.1-Terminus, showing comparable performance across various benchmarks [5] - The new model has led to a reduction of over 50% in API costs, with immediate price adjustments implemented [8] - DeepSeek has made the DeepSeek-V3.2-Exp model fully open-source on Hugging Face and ModelScope, with related research papers also published [9] - The company has retained API access for the V3.1-Terminus version for comparison purposes until October 15, 2025 [9] - Additionally, DeepSeek has open-sourced GPU operators designed for the new model, recommending the use of the TileLang version for research experiments [10]

Core Insights - DeepSeek has launched its latest experimental model, DeepSeek-V3.2-Exp, which incorporates the revolutionary DeepSeek Sparse Attention (DSA) technology aimed at significantly enhancing long text processing efficiency [2][6][7]. Group 1: Technical Innovations - The introduction of the DeepSeek Sparse Attention (DSA) mechanism allows for fine-grained sparse attention, achieving a substantial increase in long text training and inference speed with minimal impact on model output quality [6][7]. - A rigorous evaluation was conducted to align the training settings of DeepSeek-V3.2-Exp with V3.1-Terminus, showing that the performance of DeepSeek-V3.2-Exp is comparable to V3.1-Terminus across various public benchmarks [10]. Group 2: Cost Reduction - The efficiency improvements have led to a significant reduction in API call costs, with a decrease of over 50%, benefiting developers by allowing them to build more powerful applications at a lower cost [4][12]. Group 3: User Engagement and Testing - DeepSeek has retained access to the V3.1 model's API for a limited time until October 15, 2025, allowing users to compare the new and old versions while enjoying the same pricing for both [15][16]. - Users are encouraged to participate in testing the experimental version and provide feedback, which is crucial for further refinement [15][18].