Advanced Features

Content Caching

Cache large documents or contexts to reduce cost and latency.

Only use explicit caching if asked directly. Implicit caching is enabled by default and automatically provides cost savings when cache hits occur.

from google import genai
from google.genai import types

client = genai.Client()

content_cache = client.caches.create(
    model="gemini-3.5-flash",
    config=types.CreateCachedContentConfig(
        contents=[
            types.Content(
                role="user",
                parts=[
                    types.Part.from_uri(
                        file_uri="gs://your-bucket/large.pdf",
                        mime_type="application/pdf",
                    )
                ],
            )
        ],
        system_instruction="You are an expert researcher.",
        display_name="example-cache",
        ttl="86400s",
    ),
)

# Use the cache
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Summarize the pdf",
    config=types.GenerateContentConfig(cached_content=content_cache.name),
)

Batch Prediction

For processing large datasets asynchronously.

import time
from google import genai
from google.genai import types

client = genai.Client()

job = client.batches.create(
    model="gemini-3.5-flash",
    src="gs://your-bucket/prompts.jsonl",
    config=types.CreateBatchJobConfig(dest="gs://your-bucket/outputs"),
)

completed_states = {
    types.JobState.JOB_STATE_SUCCEEDED,
    types.JobState.JOB_STATE_FAILED,
    types.JobState.JOB_STATE_CANCELLED,
}
while job.state not in completed_states:
    time.sleep(30)
    job = client.batches.get(name=job.name)

Thinking (Reasoning)

Thinking is on by default for gemini-3.1-pro-preview (default HIGH / dynamic) and gemini-3.5-flash (default MEDIUM). gemini-3.1-flash-lite defaults to MINIMAL. It can be adjusted by using the thinking_level parameter.

• MINIMAL: Constrains the model to use as few tokens as possible for thinking and is best used for low-complexity tasks that wouldn't benefit from extensive reasoning. (Not supported for gemini-3.1-pro-preview)
• LOW: Constrains the model to use fewer tokens for thinking and is suitable for simpler tasks where extensive reasoning is not required.
• MEDIUM: Offers a balanced approach suitable for tasks of moderate complexity that benefit from reasoning but don't require deep, multi-step planning.
• HIGH: Maximizes reasoning depth. The model may take significantly longer to reach a first token, but the output will be more thoroughly vetted.

from google import genai
from google.genai import types

client = genai.Client()
response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="solve x^2 + 4x + 4 = 0",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(
            thinking_level=types.ThinkingLevel.HIGH,
        )
    ),
)

# Access thoughts if returned
for part in response.candidates[0].content.parts:
    if part.thought:
        print(f"Thought: {part.text}")
    else:
        print(f"Final Answer: {part.text}")

Model Context Protocol (MCP) support (experimental)

Built-in MCP support is an experimental feature. You can pass a local MCP server as a tool directly.

import os
import asyncio
from datetime import datetime
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

from google import genai
from google.genai import types

client = genai.Client()

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="npx",  # Executable
    args=["-y", "@philschmid/weather-mcp"],  # MCP Server
    env=None,  # Optional environment variables
)


async def run():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Prompt to get the weather for the current day in London.
            prompt = f"What is the weather in London in {datetime.now().strftime('%Y-%m-%d')}?"

            # Initialize the connection between client and server
            await session.initialize()

            # Send request to the model with MCP function declarations
            response = await client.aio.models.generate_content(
                model="gemini-3.5-flash",
                contents=prompt,
                config=types.GenerateContentConfig(
                    tools=[
                        session  # uses the session, will automatically call the tool using automatic function calling
                    ],
                ),
            )
            print(response.text)


# Start the asyncio event loop and run the main function
asyncio.run(run())