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].

Core Insights - DeepSeek has launched its latest model, DeepSeek-V3.2-Exp, which introduces a new attention mechanism called DeepSeek Sparse Attention (DSA) [1][6] - The model aims to enhance long text processing and inference efficiency without significantly affecting output quality [7] - A significant price reduction for the official API has been announced, starting at 50% off [3][17] Model Updates - DeepSeek-V3.2-Exp is built on the previous version, DeepSeek-V3.1-Terminus, which focused on stability, tool invocation capabilities, language consistency, and error correction [9] - In benchmark comparisons, DeepSeek-V3.2-Exp shows comparable performance to DeepSeek-V3.1-Terminus across various evaluation sets [10] - The model demonstrates improved inference costs when handling 128K long contexts, particularly during the decoding phase [12] Technical Innovations - The introduction of DSA allows for fine-grained attention mechanisms, leading to significant improvements in processing efficiency [6][7] - DeepSeek has open-sourced GPU operators in both TileLang and CUDA versions, facilitating research and development [13][15] - The company recommends using the TileLang version for debugging and rapid iteration during experimental research [16] Community Engagement - The announcement includes a call to action for the community to engage with the new model and take advantage of the promotional pricing [18] - Links to access the model on platforms like HuggingFace and ModelScope have been provided [19]

Core Viewpoint - DeepSeek has released the experimental version DeepSeek-V3.2-Exp, which introduces a new sparse attention mechanism aimed at optimizing training and inference efficiency in long-context scenarios [3][5][10]. Summary by Sections Model Release - DeepSeek-V3.2-Exp has been open-sourced with a parameter count of 685 billion [3]. - The release includes a paper detailing the new sparse attention mechanism [5]. Sparse Attention Mechanism - The DeepSeek Sparse Attention (DSA) is the only architectural improvement in version 3.2, focusing on enhancing computational efficiency when processing extended text sequences [5][6][10]. - DSA achieves fine-grained sparse attention while maintaining nearly the same output quality as its predecessor, DeepSeek-V3.1-Terminus [9]. Performance Comparison - A comparison of benchmark results between DeepSeek-V3.1-Terminus and DeepSeek-V3.2-Exp shows that the new version performs comparably across various tasks [11]. - Specific benchmark results include: - MMLU-Pro: 85.0 (V3.1) vs. 85.0 (V3.2) - AIME 2025: 88.4 (V3.1) vs. 89.3 (V3.2) - Codeforces: 2046 (V3.1) vs. 2121 (V3.2) [11]. Future Developments - The upcoming release of Z.ai's GLM-4.6 model is noted, with GLM-4.5 being the previous flagship model [12].

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]

Core Insights - DeepSeek-V3.2-Exp model has been officially released and open-sourced on the Hugging Face platform, introducing a sparse Attention architecture that reduces computational resource consumption and enhances inference efficiency [1] - Huawei Cloud has completed the adaptation of the DeepSeek-V3.2-Exp model, supporting a maximum context length of 160K [2] - The official API pricing for DeepSeek has been significantly reduced, with costs for developers to access the API dropping by over 50% due to the new model's lower service costs [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% [1] - The performance of DeepSeek-V3.2-Exp on public evaluation sets is comparable to that of V3.1-Terminus [1]

Core Insights - The appearance of DeepSeek-V3.2 on the Hugging Face platform has sparked speculation among users [1] - DeepSeek has a history of releasing new versions and updates around significant holidays [2] - The most recent update prior to the speculation was DeepSeek-V3.1-Terminus, released on September 22, with an open-source announcement [3] Version Release History - DeepSeek V3 was released on December 27, 2024, just before New Year's [3] - DeepSeek-R1-0528 was launched on May 28, 2025, as a special gift for the Dragon Boat Festival [3] - The latest version, DeepSeek-V3.1-Terminus, was made available on September 22, 2023, along with an open-source model [3] Current Status - The Hugging Face interface related to DeepSeek is currently showing errors, and there has been no official response from DeepSeek regarding the situation [4]