Core Insights - DeepSeek has launched its V3.2 version, which reportedly matches the inference capabilities of OpenAI's GPT-5 while being significantly cheaper [1][22] - The V3.2 version includes two variants: a free version for users and a Speciale version that supports API access, which boasts enhanced reasoning capabilities [2][22] Performance Enhancements - DeepSeek V3.2-Speciale has demonstrated superior performance in various competitions, achieving gold medal results in IMO 2025, CMO 2025, ICPC World Finals 2025, and IOI 2025, outperforming GPT-5 High in all tests [4][22] - The introduction of the DeepSeek Sparse Attention (DSA) mechanism has fundamentally improved the efficiency of attention in AI models, reducing computational costs by over 60% and increasing inference speed by approximately 3.5 times [6][12] Cost Efficiency - The DSA mechanism allows for a significant reduction in the cost of processing long sequences, with costs dropping from $0.7 to $0.2 per million tokens during the pre-fill phase and from $2.4 to $0.8 during the decoding phase [12][22] - This cost reduction positions DeepSeek V3.2 as one of the most affordable models for long-text inference in its category [12][22] Tool Utilization - DeepSeek V3.2 allows the AI model to call tools during its reasoning process without requiring additional training, enhancing its general performance and compatibility with user-created tools [13][22] - The model demonstrates the ability to break down complex tasks and utilize different tools effectively, showcasing its decision-making capabilities [20][22] Market Impact - The release of DeepSeek V3.2 challenges the notion that open-source models lag behind closed-source counterparts, as it offers competitive performance at a fraction of the cost [22][23] - The DSA mechanism's cost revolution is expected to significantly impact the commercialization of AI models, making advanced AI applications more accessible to smaller enterprises and consumers [22][23]

你用DeepSeek看过病了吗？ 打开它，说出自己的不舒服或拍照上传检查结果，几秒后就能得到诊断和治疗建议。继续问这个病是怎么回事或药怎么用，它还能给出更详细易懂的解 释，有问必答。 不花钱、不用抢号，还比医生耐心得多，是不是以后看病找DeepSeek就行？如果问DeepSeek本人，它会回答： DeepSeek对自己可不可以看病的回答 ｜ DeepSeek截图 实际让DeepSeek看一次病，你会在回复的末尾见到一个提示框： 3. 开出另外几项检查，分辨表现相近的疾病、确定诊断； 问其他问题的时候，一般不会出现这个提示框 ｜ DeepSeek截图 "不能""不应""仅供参考"，这是DeepSeek太过谦虚，还是看病这件事有什么特殊的地方？ 下面，我们来看看到底能不能用DeepSeek看病，和怎么用它把病看得更好（附详细提问模板）。 能不能用AI看病？当专家不能，当助手很能 有一种用DeepSeek等人工智能助手（AI）看病的方法是，得到它的回复之后就给自己确诊，然后听从AI建议开始吃药，就像刚刚找医学专家看过病。 可是，医学专家看病时很少单凭几句描述或者一张检查单，就给出一个确定的诊断，接来下可能还会做这些 ...

进入2025年末，全球大模型赛道的技术焦点几乎被Google重新夺回。Gemini 3 Pro横空出世，在多个权 威基准上超越所有开源模型，重新确立了闭源阵营的技术高地。一时间，业内关于"开源模型是否已到 极限""Scaling Law是否真的撞墙"的质疑声再起，一股迟滞情绪在开源社区弥漫。 但就在此时，DeepSeek没有选择沉默。12月1日，它一口气发布了两款重磅模型：推理性能对标GPT-5 的DeepSeek-V3.2，以及在数学、逻辑和多轮工具调用中表现异常强势的Speciale版本。这不仅是对技术 能力的集中展示，也是在当前算力资源并不占优的前提下，对闭源"新天花板"的正面回应。 这不是一次简单的模型更新。DeepSeek试图在后Scaling时代找出一条全新路径：如何用架构重塑弥补 预训练差距？如何通过"工具使用中的思考链"实现低token高效率的智能体表现？更关键的是，Agent为 何从附属功能变成了模型能力跃迁的核心引擎？ 本文将围绕这三条主线展开分析：DeepSeek是如何在技术瓶颈下突破的？为何率先在开源阵营中重注 Agent？而这是否意味着，开源模型仍有穿透闭源护城河的那条路？ 这背后的 ...

Core Viewpoint - The news highlights the performance of the ChiNext 50 ETF (159949), which has shown a slight increase of 0.48% to 1.467 CNY, amidst a broader market fluctuation, indicating ongoing investor interest and activity in the growth sector [1][6]. Market Performance - As of 10:20 AM on December 3, the ChiNext 50 ETF (159949) was trading at 1.467 CNY, with a trading volume of 4.22 billion CNY and a turnover rate of 1.66% [1][6]. - The ETF has experienced a cumulative trading amount of 323.05 billion CNY over the last 20 trading days, averaging 16.15 billion CNY per day, and a total of 3,205.79 billion CNY over 222 trading days this year, averaging 14.44 billion CNY per day [7][10]. Top Holdings - The top ten holdings of the ChiNext 50 ETF (159949) include leading companies such as CATL, Zhongji Xuchuang, Dongfang Caifu, Xinyi Technology, Sungrow Power, Shenghong Technology, Huichuan Technology, Mindray, Yiwei Lithium Energy, and Tonghuashun [3][8]. Industry Insights - Longcheng Securities reports that the continuous implementation of AI applications will drive the acceleration of computing infrastructure, particularly in the AIDC industry chain, which includes optical modules, PCBs, and main equipment manufacturers, indicating a strong demand release and potential for performance and valuation growth [10]. - The report suggests that the demand for edge computing modules will steadily increase as AI applications continue to develop, transitioning from traditional data transmission modules to intelligent and computing modules [10]. Investment Recommendations - The ChiNext 50 ETF (159949) is presented as a convenient and efficient investment tool for investors looking to capitalize on the long-term growth of China's technology sector, with recommendations for dollar-cost averaging or phased investment strategies to mitigate short-term volatility [10].

AI 产业速递：从 DeepSeek V3.2 看强化学习的新变化 20251202 摘要 Deepseek V3.2 通过 DSA 机制优化推理效率，减少冗余计算，尤其在 复杂任务中表现突出，取代了之前的 MLA 机制。 Deepseek V3.2 的 C9 版本在后训练阶段通过投入 10%的预训练计算 量，显著提升了模型在复杂任务（如代码调试）中的强化学习能力，达 到全球领先水平。 V3.2 采用高效的上下文管理策略，智能处理用户频繁开启新任务、多轮 对话及模糊输入，有效降低推理成本。 V3.2 使用大量人类专家编写并增量训练生成的高难度合成数据，比例较 之前增加一倍以上，对后续强化学习阶段至关重要，并消耗了大量算力。 Deepseek 在后训练阶段的创新，包括开源后训练结果和支持 Agent 调 用能力，使得开源模型在功能上可与闭源模型媲美，可能引领开源项目 的新趋势。 DeepMind 的新框架结合 Rubik's 规则提示机制，提高了强化学习效率， 促使大型科技公司加速探索多模态视频和图像领域的应用，推动 2025 年相关模型的发展。 稀疏化技术降低了训练算力要求，并提升了训练上限，预计到 2026 ...

华龙证券指出，我们认为，太空算力产业奇点临近，算力竞争有望开辟"新角逐场"。另外，随着AI技术 应用的广泛普及与硬件性能的持续迭代形成良性循环，全球AI产业进入共振期。 云计算ETF天弘（517390，场外C类019170）深度追踪中证沪港深云计算产业指数，独享A股与港股两 大市场的投资便利，一键网罗沪、港、深三地最具竞争力的云计算核心资产。 消息面上，据DeepSeek官微，12月1日，DeepSeek发布两个正式版模型：DeepSeek-V3.2和DeepSeek- V3.2-Speciale。DeepSeek-V3.2强化Agent能力，官方网页端、App和API均已更新为正式版DeepSeek- V3.2。Speciale版本目前仅以临时API服务形式开放，以供社区评测与研究。 据央视新闻，国内首个光量子计算机制造工厂11月24日正式在广东深圳南山区落成。该工厂11月24日正 式进入到小批量生产阶段，总面积约5000平方米，集研发、制造、测试于一体，用于实现光量子计算机 的工程化、标准化和规模化生产。 12月2日，A股市场全天震荡调整，深成指、创业板指一度双双跌超1%。截至当天收盘，中证计算机主 题指 ...

Core Insights - DeepSeek has launched two new models, DeepSeek V3.2 and DeepSeek-V3.2-Speciale, which are designed to compete with leading models like GPT-5 and Gemini [1][5][20]. Model Performance - DeepSeek V3.2 has shown competitive performance in various benchmarks, achieving scores close to or surpassing those of GPT-5 and Gemini in several tests [6][20]. - The model's performance in specific benchmarks includes: - AIME 2025: DeepSeek V3.2 scored 93.1, while DeepSeek V3.2-Speciale scored 96.0 [6]. - HMMT Feb 2025: DeepSeek V3.2 scored 92.5, and DeepSeek V3.2-Speciale scored 99.2 [6]. - Overall, DeepSeek V3.2-Speciale is noted for its ability to compete effectively with Gemini 3 [20][27]. Technological Innovations - DeepSeek has implemented Sparse Attention (DSA) in its models, which allows for more efficient processing of longer texts by reducing computational complexity [9][13]. - The company has focused on enhancing post-training processes for open-source models, investing over 10% of total training compute to improve model performance in challenging tasks [17][21]. - DeepSeek V3.2 Speciale encourages longer reasoning without penalizing the model for extended thought processes, enhancing its ability to tackle complex problems [18][20]. Cost Efficiency - Despite higher token consumption compared to competitors, DeepSeek offers a more cost-effective solution, with a significant price advantage over models like Gemini [32][33]. - For example, using 8077 tokens on DeepSeek costs approximately $0.0032, while using 4972 tokens on Gemini costs around $0.06, highlighting a 20-fold price difference [33]. Industry Context - The gap between open-source and closed-source models is reportedly widening, but DeepSeek is actively working to close this gap through innovative approaches and cost-saving measures [35][36]. - The company's strategy emphasizes algorithmic improvements over merely increasing computational power, aligning with industry insights on the importance of efficient model training [38][39].

公开资料显示，DeepSeek，全称杭州深度求索人工智能基础技术研究有限公司，成立于2023年7月，专 注大语言模型及多模态AI技术研发。（记者张璇） 【纠错】 【责任编辑:薛涛】 据DeepSeek官方消息，12月1日晚间，深度求索公司（DeepSeek）宣布发布两个正式版模型：DeepSeek- V3.2和高计算版本DeepSeek-V3.2-Speciale。 DeepSeek方面介绍，企业推出DeepSeek-V3.2模型，该模型在保持卓越推理能力和智能体性能的同时， 实现了高计算效率的平衡。 ...

PriceSeek评析 瓦楞纸，多空评分：2 广东松炀再生资源股份有限公司宣布从12月8日起上调高强瓦楞纸价格50元/吨，包括已下订单，这表明 市场需求强劲或成本压力增加，对现货价格构成重大利好。此举可能带动市场整体价格上行，反映出供 应偏紧或下游包装行业需求提升。 【大宗商品公式定价原理】生意社基准价是基于价格大数据与生意社价格模型产生的交易指导价，又称 生意社价格。可用于确定以下两种需求的交易结算价： 1、指定日期的结算价 2、指定周期的平均结算价 定价公式：结算价=生意社基准价×K＋C K：调整系数，包括账期成本等因素。 C：升贴水，包括物流成本、品牌价差、区域价差等因素。 生意社12月02日讯广东松炀再生资源股份有限公司从12月8日8:30起，我司生产的高强瓦楞纸在原有价 格基础上调50元/吨(此次调价包含已下订单)。 生意社12月02日讯广东松炀再生资源股份有限公司从12月8日8:30起，我司生产的高强瓦楞纸在原有价 格基础上调50元/吨(此次调价包含已下订单)。 PriceSeek评析 瓦楞纸，多空评分：2 广东松炀再生资源股份有限公司宣布从12月8日起上调高强瓦楞纸价格50元/吨，包括已下订单， ...

Core Insights - DeepSeek has released two official models: DeepSeek-V3.2 and DeepSeek-V3.2-Speciale, with the former focusing on balancing reasoning ability and output length for everyday use, while the latter enhances long-form reasoning and mathematical proof capabilities [1][2][4] - The open-source large model ecosystem has seen significant growth, with DeepSeek's advancements posing a challenge to closed-source models, particularly in light of the recent release of Google Gemini 3.0, which has raised the competitive bar [2][15] - DeepSeek's models are positioned to bridge the gap between open-source and closed-source models through innovative architecture and training strategies, despite limitations in computational resources compared to industry giants [8][15][16] Model Performance - DeepSeek-V3.2 has achieved performance levels comparable to GPT-5 and is slightly below Google’s Gemini 3 Pro, demonstrating its effectiveness in reasoning tasks [6][7] - The Speciale version has outperformed Gemini 3 Pro in several reasoning benchmarks, including the American Mathematics Invitational Exam (AIME) and the Harvard-MIT Mathematics Tournament (HMMT) [7][8] - Speciale's design focuses on rigorous mathematical proof and logical verification, making it a specialized tool for complex reasoning tasks [6][8] Technological Innovations - DeepSeek employs a novel DSA (DeepSeek Sparse Attention) mechanism to optimize computational efficiency, allowing for effective long-context processing without sacrificing performance [8][12] - The concept of "Interleaved Thinking" has been integrated into DeepSeek's models, enhancing the interaction between reasoning and tool usage, which is crucial for AI agents [9][12] - The focus on agent capabilities signifies a strategic shift towards creating actionable AI, moving beyond traditional chat-based interactions to more complex task execution [13][14] Industry Context - The competitive landscape is shifting, with DeepSeek acknowledging the widening gap between open-source and closed-source models, particularly in complex task performance [15][16] - DeepSeek aims to address its limitations by increasing pre-training computational resources and optimizing model efficiency, indicating a clear path for future improvements [16][19] - The release of DeepSeek-V3.2 has been seen as a significant achievement in the open-source community, suggesting that the gap with leading closed-source models is narrowing [16][19]