Yes. Gemini 3 includes a feature called dynamic thinking, which adjusts the depth of internal reasoning based on the complexity of the prompt. If the request is simple—like rewriting a sentence or extracting a short summary—the model uses minimal reasoning for fast responses. If the problem is complex—such as analyzing a multi-document policy, planning an automation workflow, or interpreting a long codebase—it increases internal reasoning automatically without the developer needing to configure anything.
This behavior helps keep latency manageable while improving accuracy when necessary. Developers can rely on dynamic thinking for general-purpose applications, because it provides a good balance across workloads. However, you can override this when needed. For example, if you want strict performance guarantees, you can force low-thinking mode. If you want maximum accuracy, you can increase the thinking level or thinking budget, effectively telling the model to think more deeply than usual.
Dynamic thinking integrates well with retrieval workflows. When a user asks a complex question, you can retrieve relevant information from a vector database likeMilvus or Zilliz Cloud., supply it as context, and let the model automatically decide how much reasoning to apply. This keeps the system responsive for simple lookups while enabling more thorough thinking for tasks that genuinely require deeper analysis. Overall, Gemini 3’s automatic adjustment makes it easier to build applications that behave intelligently without manually tuning every request.