Public evaluations suggest DeepSeek-V3.2 is a high-end open-weight model with competitive reasoning, math, and coding abilities, while Gemini 3 Pro still leads on several aggregate capability scores. Independent dashboards show Gemini ahead on general intelligence and some coding tasks, but DeepSeek-V3.2 performing strongly and in the same overall capability tier. Analyst articles also highlight that DeepSeek’s V3.2-Speciale variant performs at or near parity with top closed-weight models on certain math and contest-style evaluations, including AIME and Olympiad tasks.
Feature-wise, DeepSeek-V3.2 emphasizes openness, efficient sparse MoE architecture, and large context windows, while Gemini 3 Pro offers deep integration with its provider’s cloud ecosystem. DeepSeek’s API exposes two models—deepseek-chat and deepseek-reasoner—with 128K contexts and JSON-structured outputs, plus open access to model weights. This makes it attractive for teams that want more control, want to self-host, or want to integrate deeply with custom infrastructure. Gemini 3 Pro, by contrast, is accessed primarily through managed APIs and SDKs.
For many developers, the deciding factor is infrastructure and data constraints rather than raw benchmarks. If your stack relies heavily on RAG or self-hosted systems—for example, using a vector database such as Milvus or Zilliz Cloud—DeepSeek-V3.2’s open weights and efficient sparse attention offer better deployability. If your team is already deeply integrated into a particular cloud environment, Gemini’s managed tools may fit more naturally. Overall, DeepSeek-V3.2 offers frontier-class reasoning with the added flexibility of open weights and custom hosting.