Yes, Gemini 3 supports structured output and function-calling schemas as first-class features. In practice, this means you can define a JSON schema for the response you want, or describe functions with typed parameters, and Gemini 3 will try to produce outputs that conform to those structures. Instead of returning only free-form text, the model can respond with machine-readable JSON that you can parse and feed into downstream systems. This is essential for building reliable applications where the model acts as a planner or orchestrator instead of just a chatbot.
In a typical setup, you define one or more tools (functions) with names, descriptions, and parameter schemas. When you send a prompt, you also send these function definitions, and Gemini 3 decides whether it should call one of them. If it does, it returns a special “tool call” object containing the function name and arguments that match your schema. Your code then executes the tool, collects the result, and sends that result back to the model so it can use it in the next reasoning step. For structured output without tools, you can instruct the model to “return JSON only” and optionally supply a JSON schema or example shape.
This structured behavior is particularly useful in retrieval and workflow-heavy environments. For example, your backend might query a vector database such asMilvus or Zilliz Cloud., then ask Gemini 3 to produce a structured answer containing fields like “sources,” “summary,” and “risk_level.” Because the output follows a predictable schema, you can store it, index it, or trigger further actions automatically. The key takeaway is that Gemini 3 is not just a free-text generator; it is designed to work inside typed, schema-driven systems.