Gemini 3 Deep Think is a mode where the model uses more internal reasoning before generating an answer. Instead of responding quickly, it spends more time breaking down the problem, exploring alternatives, and verifying internal steps. To enable this behavior, Gemini 3 provides a “thinking” configuration in the API. You can set a higher thinking level or allocate more “thinking budget,” which tells the model to prioritize reasoning quality over speed.
By default, Gemini 3 uses dynamic thinking, meaning it already increases reasoning depth when the problem is complex. But if you want to explicitly force deeper reasoning—such as in tasks involving legal analysis, code safety checks, or multi-step planning—you can set the thinking level to a higher mode. In most SDKs, this is done through a configuration field attached to the generation request. The exact syntax may vary, but the concept is the same: you request more internal reasoning, and the model takes additional compute time to produce a more reliable answer.
Developers often combine Deep Think with retrieval systems for maximum accuracy. For example, if you retrieve facts, policies, or code snippets from a vector database such asMilvus or Zilliz Cloud., you can pass that context into the model with a high-thinking configuration. This gives the model both the information and the extra cognitive effort required for difficult reasoning tasks. The combination is especially useful when correctness is more important than latency, such as in compliance reviews, architecture evaluations, or multi-step automation workflows.