Gemini 3 Developer Guide

Gemini 3 is our most intelligent model family to date, built on a foundation of state-of-the-art reasoning. It is designed to bring any idea to life by mastering agentic workflows, autonomous coding, and complex multimodal tasks. This guide covers key features of the Gemini 3 model family and how to get the most out of it.

Explore our collection of Gemini 3 apps to see how the model handles advanced reasoning, autonomous coding, and complex multimodal tasks.

Get started with a few lines of code:

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.1-pro-preview",
    input="Find the race condition in this multi-threaded C++ snippet: [code here]",
)

print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

async function run() {
  const interaction = await client.interactions.create({
    model: "gemini-3.1-pro-preview",
    input: "Find the race condition in this multi-threaded C++ snippet: [code here]",
  });

  console.log(interaction.steps.at(-1).content[0].text);
}

run();

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": "Find the race condition in this multi-threaded C++ snippet: [code here]"
  }'

Meet the Gemini 3 series

Gemini 3.1 Pro is best for complex tasks that require broad world knowledge and advanced reasoning across modalities.

Gemini 3 Flash is our latest 3-series model, with Pro-level intelligence at the speed and pricing of Flash.

Nano Banana Pro (also known as Gemini 3 Pro Image) is our highest quality image generation model, and Nano Banana 2 (also known as Gemini 3.1 Flash Image) is the high-volume, high-efficiency, lower price-point equivalent.

Gemini 3.1 Flash-Lite is our workhorse model built for cost-efficiency model and high-volume tasks.

All Gemini 3 models are currently in preview.

Model ID Context Window (In / Out) Knowledge Cutoff Pricing (Input / Output)*
gemini-3.1-flash-lite-preview 1M / 64k Jan 2025 $0.25 (text, image, video), $0.50 (audio) / $1.50
gemini-3.1-flash-image-preview 128k / 32k Jan 2025 $0.25 (Text Input) / $0.067 (Image Output)**
gemini-3.1-pro-preview 1M / 64k Jan 2025 $2 / $12 (<200k tokens)
$4 / $18 (>200k tokens)
gemini-3-flash-preview 1M / 64k Jan 2025 $0.50 / $3
gemini-3-pro-image-preview 65k / 32k Jan 2025 $2 (Text Input) / $0.134 (Image Output)**

* Pricing is per 1 million tokens unless otherwise noted. ** Image pricing varies by resolution. See the pricing page for details.

For detailed limits, pricing, and additional information, see the models page.

New API features in Gemini 3

Gemini 3 introduces new parameters designed to give developers more control over latency, cost, and multimodal fidelity.

Level of thinking

Gemini 3 series models use dynamic thinking by default to reason through prompts. You can use the thinking_level parameter, which controls the maximum depth of the model's internal reasoning process before it produces a response. Gemini 3 treats these levels as relative allowances for thinking rather than strict token guarantees.

If thinking_level is not specified, Gemini 3 will default to high. For faster, lower-latency responses when complex reasoning isn't required, you can constrain the model's thinking level to low.

Thinking Level Gemini 3.1 Pro Gemini 3.1 Flash-Lite Gemini 3 Flash Description
minimal Not supported Supported (Default) Supported Matches the "no thinking" setting for most queries. The model may think very minimally for complex coding tasks. Minimizes latency for chat or high throughput applications. Note, minimal does not guarantee that thinking is off.
low Supported Supported Supported Minimizes latency and cost. Best for simple instruction following, chat, or high-throughput applications.
medium Supported Supported Supported Balanced thinking for most tasks.
high Supported (Default, Dynamic) Supported (Dynamic) Supported (Default, Dynamic) Maximizes reasoning depth. The model may take significantly longer to reach a first (non thinking) output token, but the output will be more carefully reasoned.

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.1-pro-preview",
    input="How does AI work?",
    generation_config={"thinking_level": "low"},
)

print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    model: "gemini-3.1-pro-preview",
    input: "How does AI work?",
    generationConfig: {
      thinking_level: "low",
    },
  });

console.log(interaction.steps.at(-1).content[0].text);

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": "How does AI work?",
    "config": {
      "thinking_level": "low"
    }
  }'

Temperature

For all Gemini 3 models, we strongly recommend keeping the temperature parameter at its default value of 1.0.

While previous models often benefited from tuning temperature to control creativity versus determinism, Gemini 3's reasoning capabilities are optimized for the default setting. Changing the temperature (setting it below 1.0) may lead to unexpected behavior, such as looping or degraded performance, particularly in complex mathematical or reasoning tasks.

Thought signatures

Gemini 3 models use thought signatures to maintain reasoning context across API calls. These signatures are encrypted representations of the model's internal thought process.

  • Stateful Mode (Recommended): When using the Interactions API in stateful mode (providing previous_interaction_id), the server automatically manages conversation history and thought signatures.
  • Stateless Mode: If you are managing conversation history manually, you must include thought blocks with their signatures in subsequent requests to validate authenticity.

For detailed information, see the Thought Signatures page.

Image generation and editing

For gemini-3-pro-image-preview and gemini-3.1-flash-image-preview, thought signatures are critical for conversational editing. When you ask the model to modify an image it relies on the signature from the previous turn to understand the composition and logic of the original image.

  • Editing: Signatures are guaranteed on the first part after the thoughts of the response (text or inlineData) and on every subsequent inlineData part. You must return all of these signatures to avoid errors.

Code examples

Multi-step Function Calling (Sequential)

The user asks a question requiring two separate steps (Check Flight -> Book Taxi) in one turn.

Step 1: Model calls Flight Tool.
The model returns a signature <Sig_A>

// Model Response (Turn 1, Step 1)
  {
    "id": "interaction-123",
    "steps": [
      {
        "type": "function_call",
        "name": "check_flight",
        "arguments": {"flight": "AA100"},
        "signature": "<Sig_A>" // SAVE THIS
      }
    ]
  }

Step 2: User sends Flight Result
We must send back <Sig_A> to keep the model's train of thought.

// User Request (Turn 1, Step 2)
  {
    "model": "gemini-3-flash-preview",
    "previous_interaction_id": "interaction-123",
    "input": [
      {
        "type": "function_result",
        "call_id": "fc_check_flight",
        "name": "check_flight",
        "result": [{ "type": "text", "text": "{\"status\": \"delayed\", \"departure_time\": \"12 PM\"}" }]
      }
    ]
  }

Step 3: Model calls Taxi Tool
The model remembers the flight delay using <Sig_A> and now decides to book a taxi. It generates a new signature <Sig_B>.

// Model Response (Turn 1, Step 3)
  {
    "id": "interaction-123",
    "steps": [
      {
        "type": "function_call",
        "name": "book_taxi",
        "arguments": {"time": "10 AM"},
        "signature": "<Sig_B>" // SAVE THIS
      }
    ]
  }

Step 4: User sends Taxi Result
To complete the turn, you must send back the entire chain: <Sig_A> AND <Sig_B>.

// User Request (Turn 1, Step 4)
  {
    "model": "gemini-3-flash-preview",
    "previous_interaction_id": "interaction-123",
    "input": [
      {
        "type": "function_result",
        "call_id": "fc_book_taxi",
        "name": "book_taxi",
        "result": [{ "type": "text", "text": "{\"booking_status\": \"success\"}" }]
      }
    ]
  }

Parallel Function Calling

The user asks: "Check the weather in Paris and London." The model returns two function calls in one response.

// Model Response
  {
    "id": "interaction-456",
    "steps": [
      {
        "type": "function_call",
        "name": "check_weather",
        "arguments": { "city": "Paris" },
        "signature": "<Signature_A>" // INCLUDED on First FC
      },
      {
        "type": "function_call",
        "name": "check_weather",
        "arguments": { "city": "London" }
      }
    ]
  }

// User Request (Sending Parallel Results)
  {
    "model": "gemini-3-flash-preview",
    "previous_interaction_id": "interaction-456",
    "input": [
      {
        "type": "function_result",
        "call_id": "fc_paris",
        "name": "check_weather",
        "result": [{ "type": "text", "text": "15C" }]
      },
      {
        "type": "function_result",
        "call_id": "fc_london",
        "name": "check_weather",
        "result": [{ "type": "text", "text": "12C" }]
      }
    ]
  }

Migration from other models

If you are transferring a conversation trace from another model (e.g., Gemini 2.5) or injecting a custom function call that was not generated by Gemini 3, you won't have a valid signature.

To bypass strict validation in these specific scenarios, populate the field with this specific dummy string: "signature": "context_engineering_is_the_way to_go"

Structured Outputs with tools

Gemini 3 models allow you to combine Structured Outputs with built-in tools, including Grounding with Google Search, URL Context, Code Execution, 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=[
        {"google_search": {}},
        {"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.")
});

async function run() {
  const interaction = await client.interactions.create({
    model: "gemini-3.1-pro-preview",
    input: "Search for all details for the latest Euro.",
    tools: [
      { googleSearch: {} },
      { urlContext: {} }
    ],
    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);
}

run();

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": [
      {"google_search": {}},
      {"url_context": {}}
    ],
    "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
            "type": "object",
            "properties": {
                "winner": {"type": "string", "description": "The name of the winner."},
                "final_match_score": {"type": "string", "description": "The final score."},
                "scorers": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "The name of the scorer."
                }
            },
            "required": ["winner", "final_match_score", "scorers"]
        }
    }
  }'

Image generation

Gemini 3.1 Flash Image and Gemini 3 Pro Image let you generate and edit images from text prompts. It uses reasoning to "think" through a prompt and can retrieve real-time data—such as weather forecasts or stock charts—before using Google Search grounding before generating high-fidelity images.

New & improved capabilities:

  • 4K & text rendering: Generate sharp, legible text and diagrams with up to 2K and 4K resolutions.
  • Grounded generation: Use the google_search tool to verify facts and generate imagery based on real-world information. Grounding with Google Image Search available for Gemini 3.1 Flash Image.
  • Conversational editing: Multi-turn image editing by simply asking for changes (e.g., "Make the background a sunset"). This workflow relies on Thought Signatures to preserve visual context between turns.

For complete details on aspect ratios, editing workflows, and configuration options, see the Image Generation guide.

Python

from google import genai
import base64

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3-pro-image-preview",
    input="Generate an infographic of the current weather in Tokyo.",
    tools=[{"google_search": {}}],
    response_format={
        "type": "image",
        "aspect_ratio": "16:9",
        "image_size": "4K"
    }
)

from PIL import Image
import io

image_blocks = [content_block for content_block in interaction.steps[-1].content if content_block.type == "image"]
if image_blocks:
    image_data = base64.b64decode(image_blocks[0].data)
    image = Image.open(io.BytesIO(image_data))
    image.save('weather_tokyo.png')
    image.show()

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const client = new GoogleGenAI({});

async function run() {
  const interaction = await client.interactions.create({
    model: "gemini-3-pro-image-preview",
    input: "Generate a visualization of the current weather in Tokyo.",
    tools: [{ googleSearch: {} }],
    responseFormat: {
      type: "image",
      aspectRatio: "16:9",
      imageSize: "4K"
    }
  });

  for (const contentBlock of interaction.steps.at(-1).content) {
    if (contentBlock.type === "image") {
      const buffer = Buffer.from(contentBlock.data, "base64");
      fs.writeFileSync("weather_tokyo.png", buffer);
    }
  }
}

run();

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-pro-image-preview",
    "input": "Generate a visualization of the current weather in Tokyo.",
    "tools": [{"google_search": {}}],
    "response_format": {
        "type": "image",
        "aspect_ratio": "16:9",
        "image_size": "4K"
    }
  }'

Example Response

Weather Tokyo

Code Execution with images

Gemini 3 Flash can treat vision as an active investigation, not just a static glance. By combining reasoning with code execution, the model formulates a plan, then writes and executes Python code to zoom in, crop, annotate, or otherwise manipulate images step-by-step to visually ground its answers.

Use cases:

  • Zoom and inspect: The model implicitly detects when details are too small (e.g., reading a distant gauge or serial number) and writes code to crop and re-examine the area at higher resolution.
  • Visual math and plotting: The model can run multi-step calculations using code (e.g., summing line items on a receipt, or generating a Matplotlib chart from extracted data).
  • Image annotation: The model can draw arrows, bounding boxes, or other annotations directly onto images to answer spatial questions like "Where should this item go?".

To enable visual thinking, configure Code Execution as a tool. The model will automatically use code to manipulate images when needed.

Python

from google import genai
from google.genai import types
import requests
from PIL import Image
import io
import base64

image_path = "https://goo.gle/instrument-img"
image_bytes = requests.get(image_path).content
image = types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg")

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        image,
        "Zoom into the expression pedals and tell me how many pedals are there?"
    ],
    tools=[{"code_execution": {}}],
)

from IPython.display import display
from PIL import Image
import io

for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.type == "text":
                print(content_block.text)
            elif content_block.type == "image":
                 display(Image.open(io.BytesIO(base64.b64decode(content_block.data))))
    elif step.type == "code_execution_call":
        print(step.code)
    elif step.type == "code_execution_result":
        print(step.output)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

async function main() {
  const imageUrl = "https://goo.gle/instrument-img";
  const response = await fetch(imageUrl);
  const imageArrayBuffer = await response.arrayBuffer();
  const base64ImageData = Buffer.from(imageArrayBuffer).toString("base64");

  const interaction = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: [
      {
        inlineData: {
          mimeType: "image/jpeg",
          data: base64ImageData,
        },
      },
      {
        text: "Zoom into the expression pedals and tell me how many pedals are there?",
      },
    ],
    tools: [{ codeExecution: {} }],
  });

  for (const step of interaction.steps) {
    if (step.type === "model_output") {
      for (const contentBlock of step.content) {
        if (contentBlock.type === "text") {
          console.log("Text:", contentBlock.text);
        }
      }
    } else if (step.type === "code_execution_call") {
      console.log("Code:", step.code);
    } else if (step.type === "code_execution_result") {
      console.log("Output:", step.output);
    }
  }
}

main();

REST

IMG_URL="https://goo.gle/instrument-img"
MODEL="gemini-3-flash-preview"

MIME_TYPE=$(curl -sIL "$IMG_URL" | grep -i '^content-type:' | awk -F ': ' '{print $2}' | sed 's/\r$//' | head -n 1)
if [[ -z "$MIME_TYPE" || ! "$MIME_TYPE" == image/* ]]; then
  MIME_TYPE="image/jpeg"
fi

if [[ "$(uname)" == "Darwin" ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -b 0)
elif [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64)
else
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -w0)
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "'$MODEL'",
      "input": [{
        "parts":[
            {
              "inline_data": {
                "mime_type":"'"$MIME_TYPE"'",
                "data": "'"$IMAGE_B64"'"
              }
            },
            {"text": "Zoom into the expression pedals and tell me how many pedals are there?"}
        ]
      }],
      "tools": [{"code_execution": {}}]
    }'

For more details on code execution with images, see Code Execution.

Multimodal function responses

Multimodal function calling allows users to have function responses containing multimodal objects allowing for improved utilization of function calling capabilities of the model. Standard function calling only supports text-based function responses:

Python 

from google import genai
import requests
import base64

client = genai.Client()

# 1. Define the tool
get_image_tool = {
    "type": "function",
    "name": "get_image",
    "description": "Retrieves the image file reference for a specific order item.",
    "parameters": {
        "type": "object",
        "properties": {
            "item_name": {
                "type": "string",
                "description": "The name or description of the item ordered (e.g., 'instrument')."
            }
        },
        "required": ["item_name"],
    },
}

# 2. Send the request with tools
interaction_1 = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Show me the instrument I ordered last month.",
    tools=[get_image_tool],
)

# 3. Find the function call step
fc_step = next(s for s in interaction_1.steps if s.type == "function_call")
print(f"Tool Call: {fc_step.name}({fc_step.arguments})")

# Execute tool (fetch image)
image_path = "https://goo.gle/instrument-img"
image_bytes = requests.get(image_path).content
image_b64 = base64.b64encode(image_bytes).decode("utf-8")

# 4. Send multimodal function result back
interaction_2 = client.interactions.create(
    model="gemini-3-flash-preview",
    previous_interaction_id=interaction_1.id,
    input=[{
        "type": "function_result",
        "name": fc_step.name,
        "call_id": fc_step.id,
        "result": [
            {"type": "text", "text": "instrument.jpg"},
            {
                "type": "image",
                "mime_type": "image/jpeg",
                "data": image_b64,
            }
        ]
    }],
    tools=[get_image_tool],
    thinking_config={"include_thoughts": True},
)

print(f"\nFinal model response: {interaction_2.steps[-1].content[0].text}")

JavaScript 

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

// 1. Define the tool
const getImageTool = {
    type: 'function',
    name: 'get_image',
    description: 'Retrieves the image file reference for a specific order item.',
    parameters: {
        type: 'object',
        properties: {
            item_name: {
                type: 'string',
                description: "The name or description of the item ordered (e.g., 'instrument').",
            },
        },
        required: ['item_name'],
    },
};

// 2. Send the request with tools
const interaction1 = await client.interactions.create({
    model: 'gemini-3-flash-preview',
    input: 'Show me the instrument I ordered last month.',
    tools: [getImageTool],
});

// 3. Find the function call step
const fcStep = interaction1.steps.find(s => s.type === 'function_call');
console.log(`Tool Call: ${fcStep.name}(${JSON.stringify(fcStep.arguments)})`);

// Execute tool (fetch image)
const imageUrl = 'https://goo.gle/instrument-img';
const response = await fetch(imageUrl);
const imageArrayBuffer = await response.arrayBuffer();
const base64ImageData = Buffer.from(imageArrayBuffer).toString('base64');

// 4. Send multimodal function result back
const interaction2 = await client.interactions.create({
    model: 'gemini-3-flash-preview',
    previousInteractionId: interaction1.id,
    input: [{
        type: 'function_result',
        name: fcStep.name,
        call_id: fcStep.id,
        result: [
            { type: 'text', text: 'instrument.jpg' },
            {
                type: 'image',
                mimeType: 'image/jpeg',
                data: base64ImageData,
            }
        ]
    }],
    tools: [getImageTool],
    thinking_config: { include_thoughts: true },
});

console.log(`\nFinal model response: ${interaction2.steps.at(-1).content[0].text}`);

REST 

IMG_URL="https://goo.gle/instrument-img"

MIME_TYPE=$(curl -sIL "$IMG_URL" | grep -i '^content-type:' | awk -F ': ' '{print $2}' | sed 's/\r$//' | head -n 1)
if [[ -z "$MIME_TYPE" || ! "$MIME_TYPE" == image/* ]]; then
  MIME_TYPE="image/jpeg"
fi

# Check for macOS
if [[ "$(uname)" == "Darwin" ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -b 0)
elif [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64)
else
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -w0)
fi

# 1. First interaction (triggers function call)
# 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": "Show me the instrument I ordered last month.", "tools": [...] }'

# 2. Send multimodal function result back (Replace INTERACTION_ID and CALL_ID)
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3-flash-preview",
    "previous_interaction_id": "INTERACTION_ID",
    "input": [{
      "type": "function_result",
      "name": "get_image",
      "call_id": "CALL_ID",
      "result": [
        { "type": "text", "text": "instrument.jpg" },
        {
          "type": "image",
          "mime_type": "'"$MIME_TYPE"'",
          "data": "'"$IMAGE_B64"'"
        }
      ]
    }],
    "thinking_config": { "include_thoughts": true }
  }'

Combine built-in tools and function calling

Gemini 3 allows the use of built-in tools (like Google Search, URL context, and more) and custom function calling tools in the same API call, allowing for more complex workflows.

Python

from google import genai
from google.genai import types

client = genai.Client()

getWeather = {
    "type": "function",
    "name": "getWeather",
    "description": "Gets the weather for a requested city.",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city and state, e.g. Utqiaġvik, Alaska",
            },
        },
        "required": ["city"],
    },
}

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="What is the northernmost city in the United States? What's the weather like there today?",
    tools=[
        {"google_search": {}},
        getWeather
    ],
)

# Find the function call step
fc_step = next((s for s in interaction.steps if s.type == "function_call"), None)

if fc_step:
    # Simulate a function result
    result = {"response": "Very cold. 22 degrees Fahrenheit."}

    final_interaction = client.interactions.create(
        model="gemini-3-flash-preview",
        input=[
            {"type": "function_result", "name": fc_step.name, "call_id": fc_step.id, "result": result}
        ],
        tools=[
            {"google_search": {}},
            getWeather
        ],
        previous_interaction_id=interaction.id,
    )

    print(final_interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI, Type } from '@google/genai';

const client = new GoogleGenAI({});

const getWeatherDeclaration = {
  type: 'function',
  name: 'getWeather',
  description: 'Gets the weather for a requested city.',
  parameters: {
    type: Type.OBJECT,
    properties: {
      city: {
        type: Type.STRING,
        description: 'The city and state, e.g. Utqiaġvik, Alaska',
      },
    },
    required: ['city'],
  },
};

const interaction = await client.interactions.create({
  model: 'gemini-3-flash-preview',
  input: "What is the northernmost city in the United States? What's the weather like there today?",
  tools: [
    { googleSearch: {} },
    getWeatherDeclaration
  ],
});

// Find the function call step
const fcStep = interaction.steps.find(s => s.type === 'function_call');

if (fcStep) {
  const result = { response: "Very cold. 22 degrees Fahrenheit." };

  const finalInteraction = await client.interactions.create({
    model: 'gemini-3-flash-preview',
    input: [
      { type: 'function_result', name: fcStep.name, call_id: fcStep.id, result: result }
    ],
    tools: [
      { googleSearch: {} },
      getWeatherDeclaration
    ],
    previous_interaction_id: interaction.id,
  });

  console.log(finalInteraction.steps.at(-1).content[0].text);
}

Migration from Gemini 2.5

Gemini 3 is our most capable model family to date and offers a stepwise improvement over Gemini 2.5. When migrating, consider the following:

  • Thinking: If you were previously using complex prompt engineering (like chain of thought) to force Gemini 2.5 to reason, try Gemini 3 with thinking_level: "high" and simplified prompts.
  • Temperature settings: If your existing code explicitly sets temperature (especially to low values for deterministic outputs), we recommend removing this parameter and using the Gemini 3 default of 1.0 to avoid potential looping issues or performance degradation on complex tasks.
  • PDF & document understanding: If you relied on specific behavior for dense document parsing, test the new media_resolution_high setting to ensure continued accuracy.
  • Token consumption: Migrating to Gemini 3 defaults may increase token usage for PDFs but decrease token usage for video. If requests now exceed the context window due to higher default resolutions, we recommend explicitly reducing the media resolution.
  • Image segmentation: Image segmentation capabilities (returning pixel-level masks for objects) are not supported in Gemini 3 Pro or Gemini 3 Flash. For workloads requiring built-in image segmentation, we recommend continuing to utilize Gemini 2.5 Flash with thinking turned off or Gemini Robotics-ER 1.6.
  • Computer Use: Gemini 3 Pro and Gemini 3 Flash support Computer Use. Unlike the 2.5 series, you don't need to use a separate model to access the Computer Use tool.
  • Tool support: Combining built-in tools with function calling is now supported for Gemini 3 models. Maps grounding is also now supported for Gemini 3 models.

OpenAI compatibility

For users utilizing the OpenAI compatibility layer, standard parameters (OpenAI's reasoning_effort) are automatically mapped to Gemini (thinking_level) equivalents.

Prompting best practices

Gemini 3 is a reasoning model, which changes how you should prompt.

  • Precise instructions: Be concise in your input prompts. Gemini 3 responds best to direct, clear instructions. It may over-analyze verbose or overly complex prompt engineering techniques used for older models.
  • Output verbosity: By default, Gemini 3 is less verbose and prefers providing direct, efficient answers. If your use case requires a more conversational or "chatty" persona, you must explicitly steer the model in the prompt (e.g., "Explain this as a friendly, talkative assistant").
  • Context management: When working with large datasets (e.g., entire books, codebases, or long videos), place your specific instructions or questions at the end of the prompt, after the data context. Anchor the model's reasoning to the provided data by starting your question with a phrase like, "Based on the preceding information...".

Learn more about prompt design strategies in the prompt engineering guide.

FAQ

  1. What is the knowledge cutoff for Gemini 3? Gemini 3 models have a knowledge cutoff of January 2025. For more recent information, use the Search Grounding tool.

  2. What are the context window limits? Gemini 3 models support a 1 million token input context window and up to 64k tokens of output.

  3. Is there a free tier for Gemini 3? Gemini 3 Flash gemini-3-flash-preview has a free tier in the Gemini API. You can try Gemini 3.1 Pro and 3 Flash at no cost in Google AI Studio, but there is no free tier available for gemini-3.1-pro-preview in the Gemini API.

  4. Will my old thinking_budget code still work? Yes, thinking_budget is still supported for backward compatibility, but we recommend migrating to thinking_level for more predictable performance. Don't use both in the same request.

  5. Does Gemini 3 support the Batch API? Yes, Gemini 3 supports the Batch API.

  6. Is Context Caching supported? Yes, Context Caching is supported for Gemini 3.

  7. Which tools are supported in Gemini 3? Gemini 3 supports Google Search, Grounding with Google Maps, File Search, Code Execution, and URL Context. It also supports standard Function Calling for your own custom tools, and in combination with built-in tools.

  8. What is gemini-3.1-pro-preview-customtools? If you are using gemini-3.1-pro-preview and the model ignores your custom tools in favor of bash commands, try the gemini-3.1-pro-preview-customtools model instead. More info here.

Next steps