Structured outputs

You can configure Gemini models to generate responses that adhere to a provided JSON Schema. This ensures predictable, type-safe results and simplifies extracting structured data from unstructured text.

Using structured outputs is ideal for:

  • Data extraction: Pull specific information like names and dates from text.
  • Structured classification: Classify text into predefined categories.
  • Agentic workflows: Generate structured inputs for tools or APIs.

In addition to supporting JSON Schema in the REST API, the Google GenAI SDKs allow defining schemas using Pydantic (Python) and Zod (JavaScript).

This example demonstrates how to extract structured data from text using basic JSON Schema types like object, array, string, and integer.

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List, Optional

class Ingredient(BaseModel):
    name: str = Field(description="Name of the ingredient.")
    quantity: str = Field(description="Quantity of the ingredient, including units.")

class Recipe(BaseModel):
    recipe_name: str = Field(description="The name of the recipe.")
    prep_time_minutes: Optional[int] = Field(description="Optional time in minutes to prepare the recipe.")
    ingredients: List[Ingredient]
    instructions: List[str]

client = genai.Client()

prompt = """
Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.
"""

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Recipe.model_json_schema()
    },
)

recipe = Recipe.model_validate_json(interaction.steps[-1].content[0].text)
print(recipe)

JavaScript

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const ingredientSchema = z.object({
  name: z.string().describe("Name of the ingredient."),
  quantity: z.string().describe("Quantity of the ingredient, including units."),
});

const recipeSchema = z.object({
  recipe_name: z.string().describe("The name of the recipe."),
  prep_time_minutes: z.number().optional().describe("Optional time in minutes to prepare the recipe."),
  ingredients: z.array(ingredientSchema),
  instructions: z.array(z.string()),
});

const client = new GoogleGenAI({});

const prompt = `
Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.
`;

const interaction = await client.interactions.create({
  model: "gemini-3-flash-preview",
  input: prompt,
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: zodToJsonSchema(recipeSchema)
  },
});

const recipe = recipeSchema.parse(JSON.parse(interaction.steps.at(-1).content[0].text));
console.log(recipe);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": "Please extract the recipe from the following text.\nThe user wants to make delicious chocolate chip cookies.\nThey need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,\n1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,\n3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.\nFor the best part, they will need 2 cups of semisweet chocolate chips.\nFirst, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,\nbaking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar\nuntil light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry\ningredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons\nonto ungreased baking sheets and bake for 9 to 11 minutes.",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "recipe_name": {
              "type": "string",
              "description": "The name of the recipe."
            },
            "prep_time_minutes": {
                "type": "integer",
                "description": "Optional time in minutes to prepare the recipe."
            },
            "ingredients": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "name": { "type": "string", "description": "Name of the ingredient."},
                  "quantity": { "type": "string", "description": "Quantity of the ingredient, including units."}
                },
                "required": ["name", "quantity"]
              }
            },
            "instructions": {
              "type": "array",
              "items": { "type": "string" }
            }
          },
          "required": ["recipe_name", "ingredients", "instructions"]
        }
      }
      }
    }'

Example Response:

{
  "recipe_name": "Delicious Chocolate Chip Cookies",
  "ingredients": [
    { "name": "all-purpose flour", "quantity": "2 and 1/4 cups" },
    { "name": "baking soda", "quantity": "1 teaspoon" },
    { "name": "salt", "quantity": "1 teaspoon" },
    { "name": "unsalted butter (softened)", "quantity": "1 cup" },
    { "name": "granulated sugar", "quantity": "3/4 cup" },
    { "name": "packed brown sugar", "quantity": "3/4 cup" },
    { "name": "vanilla extract", "quantity": "1 teaspoon" },
    { "name": "large eggs", "quantity": "2" },
    { "name": "semisweet chocolate chips", "quantity": "2 cups" }
  ],
  "instructions": [
    "Preheat the oven to 375°F (190°C).",
    "In a small bowl, whisk together the flour, baking soda, and salt.",
    "In a large bowl, cream together the butter, granulated sugar, and brown sugar until light and fluffy.",
    "Beat in the vanilla and eggs, one at a time.",
    "Gradually beat in the dry ingredients until just combined.",
    "Stir in the chocolate chips.",
    "Drop by rounded tablespoons onto ungreased baking sheets and bake for 9 to 11 minutes."
  ]
}

Streaming results

You can stream structured outputs, allowing you to start processing the response as it's being generated. The streamed chunks are valid partial JSON strings that can be concatenated to form the final JSON object.

Python

from google import genai
from pydantic import BaseModel
from typing import Literal

class Feedback(BaseModel):
    sentiment: Literal["positive", "neutral", "negative"]
    summary: str

client = genai.Client()
prompt = "The new UI is incredibly intuitive. Add a very long summary to test streaming!"

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Feedback.model_json_schema()
    },
    stream=True
)
for event in stream:
    if event.event_type == "step.delta" and event.delta.text:
        print(event.delta.text, end="")

JavaScript

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const client = new GoogleGenAI({});
const feedbackSchema = z.object({
  sentiment: z.enum(["positive", "neutral", "negative"]),
  summary: z.string(),
});

const stream = await client.interactions.create({
  model: "gemini-3-flash-preview",
  input: "The new UI is incredibly intuitive. Add a very long summary!",
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: zodToJsonSchema(feedbackSchema)
  },
  stream: true,
});

for await (const event of stream) {
  if (event.type === "step.delta" && event.delta?.text) {
    process.stdout.write(event.delta.text);
  }
}

Structured outputs with tools

Gemini 3 lets you combine Structured Outputs with built-in tools, including Grounding with Google Search, URL Context, Code Execution, File Search, and Function Calling.

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List

class MatchResult(BaseModel):
    winner: str = Field(description="The name of the winner.")
    final_match_score: str = Field(description="The final match score.")
    scorers: List[str] = Field(description="The name of the scorer.")

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.1-pro-preview",
    input="Search for all details for the latest Euro.",
    tools=[{"type": "google_search"}, {"type": "url_context"}],
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": MatchResult.model_json_schema()
    },
)

result = MatchResult.model_validate_json(interaction.steps[-1].content[0].text)
print(result)

JavaScript

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const client = new GoogleGenAI({});
const matchSchema = z.object({
  winner: z.string().describe("The name of the winner."),
  final_match_score: z.string().describe("The final score."),
  scorers: z.array(z.string()).describe("The name of the scorer.")
});

const interaction = await client.interactions.create({
  model: "gemini-3.1-pro-preview",
  input: "Search for all details for the latest Euro.",
  tools: [{type: "google_search"}, {type: "url_context"}],
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: zodToJsonSchema(matchSchema)
  },
});

const match = matchSchema.parse(JSON.parse(interaction.steps.at(-1).content[0].text));
console.log(match);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.1-pro-preview",
    "input": "Search for all details for the latest Euro.",
    "tools": [{"type": "google_search"}, {"type": "url_context"}],
    "response_format": {
      "type": "text",
      "mime_type": "application/json",
      "schema": {
        "type": "object",
        "properties": {
            "winner": {"type": "string"},
            "final_match_score": {"type": "string"},
            "scorers": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["winner", "final_match_score", "scorers"]
      }
    }
  }'

JSON schema support

To generate a JSON object, configure response_format with an object (or an array containing an object) of type text and set its mime_type to application/json. The schema should be provided in the schema field.

Gemini's structured output mode supports a subset of the JSON Schema specification.

The following values of type are supported:

  • string: For text.
  • number: For floating-point numbers.
  • integer: For whole numbers.
  • boolean: For true or false values.
  • object: For structured data with key-value pairs.
  • array: For lists of items.
  • null: To allow a property to be null, include "null" in the type array (e.g., {"type": ["string", "null"]}).

These descriptive properties help guide the model:

  • title: A short description of a property.
  • description: A longer and more detailed description of a property.

Type-specific properties

For object values:

  • properties: An object where each key is a property name and each value is a schema for that property.
  • required: An array of strings, listing which properties are mandatory.
  • additionalProperties: Controls whether properties not listed in properties are allowed. Can be a boolean or a schema.

For string values:

  • enum: Lists a specific set of possible strings for classification tasks.
  • format: Specifies a syntax for the string, such as date-time, date, time.

For number and integer values:

  • enum: Lists a specific set of possible numeric values.
  • minimum: The minimum inclusive value.
  • maximum: The maximum inclusive value.

For array values:

  • items: Defines the schema for all items in the array.
  • prefixItems: Defines a list of schemas for the first N items, allowing for tuple-like structures.
  • minItems: The minimum number of items in the array.
  • maxItems: The maximum number of items in the array.

Model support

Model Structured Outputs
Gemini 3.1 Pro Preview ✔️
Gemini 3 Flash Preview ✔️
Gemini 2.5 Pro ✔️
Gemini 2.5 Flash ✔️
Gemini 2.5 Flash-Lite ✔️
Gemini 2.0 Flash ✔️*
Gemini 2.0 Flash-Lite ✔️*

* Gemini 2.0 requires an explicit propertyOrdering list.

Structured outputs versus function calling

Feature Primary Use Case
Structured Outputs Formatting the final response. Use when you want the model's answer in a specific format.
Function Calling Taking action during conversation. Use when the model needs to ask you to perform a task before providing a final answer.

Best practices

  • Clear descriptions: Use the description field to guide the model.
  • Strong typing: Use specific types (integer, string, enum).
  • Prompt engineering: Clearly state what you want the model to do.
  • Validation: While output is syntactically correct JSON, always validate values in your application.
  • Error handling: Implement robust error handling for schema-compliant but semantically incorrect outputs.

Limitations

  • Schema subset: Not all JSON Schema features are supported.
  • Schema complexity: Very large or deeply nested schemas may be rejected.