The release of DeepSeek V3.2 marks the official entry of the Chinese AI market into the &#34;second wave impact&#34; phase.
On December 6th, according to Hard AI news, JP Morgan stated in a research report that this is not just a model iteration, but a structural revolution targeting reasoning costs and hardware ecology. DeepSeek has once again reduced API prices by 30-70% through architectural innovation, resulting in a dramatic drop in long-context reasoning costs by 6-10 times.
The research report emphasizes that, more critically, V3.2-Exp has achieved &#34;Day-0&#34; support for non-CUDA ecosystems (Huawei Ascend, Cambricon, Haiguang), completely breaking the dependency path of cutting-edge models on NVIDIA hardware.
According to JP Morgan's analysis, beneficiaries include cloud operators Alibaba, Tencent, Baidu, as well as chip manufacturers Zhongwei Company, Northern Huachuang, Huaqin Technology, and Inspur Information. It is expected that the V3.2 model will further enhance the popularity of generative AI in China over the next few quarters.
According to an article from Wall Street News, on December 1st, DeepSeek released two models from the V3.2 series and made them open source. The V3.2 model focuses on daily applications, achieving reasoning capabilities at the GPT-5 level, and for the first time integrates thinking modes with tool invocation; V3.2-Speciale focuses on extreme reasoning, winning gold medals in four international competitions: IMO, CMO, ICPC, and IOI.
<h2>Performance and Architecture: Extreme Efficiency and &#34;Agent&#34; Evolution</h2>
DeepSeek V3.2 is not merely a stacking of parameters, but achieves a qualitative change in efficiency through innovations at the algorithmic level. This model continues the mixed expert (MoE) architecture from V3.1 but introduces the DeepSeek Sparse Attention mechanism (DSA).
JP Morgan pointed out that as a follow-up product to the experimental V3.2-Exp model first released on September 29th, the V3.2 model has introduced the DeepSeek Sparse Attention mechanism (DSA) through continuous training, which is the only architectural change, reducing long-context computation while maintaining standards in public benchmark tests. <img src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/cbac3ed3-068f-4fc8-b119-2cc664cc3c70.png?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/cbac3ed3-068f-4fc8-b119-2cc664cc3c70.png"/>
Specifically, it includes the following four aspects:
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Architectural Breakthrough: The DSA mechanism selects key key-value entries through a lightning indexer, reducing the computational complexity of long-context scenarios from quadratic (O(L2)) to quasi-linear (O(L·k)).
Performance Data: In a long text environment of 128k tokens, the reasoning speed of V3.2 has improved by 2-3 times compared to the previous generation, GPU memory usage has decreased by 30-40%, and the model's performance has not only not declined but has maintained an extremely high level
Agent Positioning: V3.2 is clearly positioned as a &#34;reasoning-first model built for Agents.&#34; It implements a deep interleaving of &#34;thinking + tool invocation&#34;—the model can combine thinking chains and tool invocation (API, search, code execution) in a single trajectory.
<img src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/56fb7e9a-b4d6-4cf9-87b2-837b81c2a086.png?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/56fb7e9a-b4d6-4cf9-87b2-837b81c2a086.png"/>
High-end Version: The Speciale version performs exceptionally well in Olympic-level mathematics competitions and competitive programming, with its reasoning benchmarks comparable to Gemini 3.0 Pro and GPT-5 level systems.
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<h2>Pricing Revolution: The Deflationary Economics of Reasoning</h2>
JP Morgan pointed out that the release of DeepSeek V3.2 has once again established its position as a &#34;price butcher,&#34; especially in comparison with top models in the United States, showcasing an astonishing cost-performance advantage.
The research report believes that the efficiency improvements brought by the DSA architecture have directly translated into structural price reductions for APIs. Specifically manifested as:
<blockquote>
Specific Pricing: The input price for V3.2 Reasoning has dropped to $0.28 per million tokens, and the output price has dropped to $0.42.
<img src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/6afd5e67-4332-4dc7-b37a-8d78343d601d.png?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/6afd5e67-4332-4dc7-b37a-8d78343d601d.png"/>
Comparison of Reductions: Compared to the V3.1 Reasoning released in September 2025 (input $0.42/output $1.34), the output cost has plummeted by 69%, and the input cost has decreased by 33%. Compared to the R1 model from January 2025, the price advantage has expanded exponentially.
<img src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/b878ba76-2aeb-4194-82fc-000e9565a191.png?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://wpimg-wscn.awtmt.com/b878ba76-2aeb-4194-82fc-000e9565a191.png"/>
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According to JP Morgan's analysis, based on third-party benchmarks, the actual costs of some long-context reasoning workloads have decreased by 6-10 times. This pricing strategy forces the market to redefine the cost benchmarks for &#34;cutting-edge&#34; capabilities, creating significant downward pricing pressure on all competitors.
In the comparison of intelligent indices and prices by ArtificialAnalysis, DeepSeek V3.2 is positioned in the quadrant of &#34;high intelligence, extremely low price.&#34;
<h2>Ecological Reconstruction: The &#34;Day-0&#34; Moment of Domestic Chips</h2>
According to the research report, DeepSeek V3.2 marks a shift for Chinese AI models from purely relying on the NVIDIA CUDA ecosystem to actively adapting to domestic hardware.
JP Morgan stated that V3.2-Exp is one of the first cutting-edge models optimized for non-CUDA ecosystems on the day of its release (Day-0), supporting hardware including Huawei's CANN stack and Ascend, Cambricon's vLLM-MLU, and Haiguang's DTK.
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This sends a strong signal to the market—GPT-5 level open-source models can run efficiently on domestic accelerators. This will reduce the execution risk for Chinese AI buyers from the bottom up and directly drive incremental demand for domestic AI chips and servers
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JP Morgan stated that DeepSeek V3.2 achieves architectural revolution through sparse attention mechanisms, with reasoning capabilities reaching GPT-5 levels. The cost is only $0.28 per million tokens input, and API prices have decreased by 30-70%, with long text reasoning costs plummeting by 6-10 times. More importantly, it has achieved "Day-0" support for domestic chips such as Huawei Ascend and Cambricon for the first time, breaking the dependence on Nvidia

The second wave of DeepSeek impact: V3.2 rewrites China's cloud ecosystem and chip ecosystem