Core Insights - DeepSeek has released the experimental version DeepSeek-V3.2-Exp, which introduces Sparse Attention for improved training and inference efficiency on long texts [1] - The API pricing has been reduced by over 50% due to significant cost savings from the new model [1] - Cambricon has adapted to DeepSeek-V3.2-Exp and open-sourced the vLLM-MLU inference engine, allowing developers to experience the new model on their platform [1][2] - Huawei Ascend has also quickly adapted to DeepSeek-V3.2-Exp, open-sourcing all inference code and achieving optimized deployment on the CANN platform [3] Group 1 - DeepSeek-V3.2-Exp is an experimental version that builds on the previous V3.1-Terminus, focusing on optimizing long text processing [1] - The new model's API pricing reduction is a strategic move to enhance developer engagement and usage [1] - Cambricon's rapid adaptation to the new model demonstrates its commitment to software ecosystem development and performance optimization [2] Group 2 - Huawei's deployment of DeepSeek-V3.2-Exp BF16 model showcases its capability in handling large sequence processing with low latency and high throughput [3] - The continuous iteration of DeepSeek models indicates a proactive approach to addressing user feedback and improving model performance [3]

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]

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 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 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]

9月29日，华为云表示，目前已完成对 DeepSeek-V3.2-Exp 模型的适配工作，最大可支持160K长序列上 下文长度。目前，该模型已正式上架华为云大模型即服务平台 MaaS。 目前，该模型已正式上架华为云大模型即服务平台 MaaS。 ...

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 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]