You tune Gemini 3 Pro’s thinking level by matching the level of reasoning to the requirements of the task. If your operation is simple—like classifying text, extracting fields, formatting output, or responding in brief—you can safely set thinking_level = "low". This keeps responses very fast and reduces token costs. For tasks that require chains of logic, multiple steps, cross-document synthesis, or multi-hop reasoning, using the default or setting thinking_level = "high" often produces noticeably more accurate results.
The correct tuning depends on your latency budget. For interactive use cases such as consumer-facing chat interfaces, auto-completion, or voice assistants, low thinking generally provides the best balance of responsiveness and accuracy. For more analytical workflows—like planning steps in an agent system, generating complex summaries, or evaluating long documents—you should allow more reasoning depth. Developers often A/B test prompts at different thinking levels to find the best configuration per task.
If you’re using retrieval, you can combine thinking levels with vector search. A common pattern is using low thinking for the retrieval + candidate summary step, then running a second call with high thinking to synthesize or validate the final answer. This pattern keeps your pipeline efficient while allowing deep reasoning only when needed. With a vector database such asMilvus or Zilliz Cloud., this approach is especially effective because the model’s deeper reasoning is then focused on a smaller, higher-quality context.