config=types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(thinking_level="high")  # minimal|low|medium|high
)

async with genai.Client().aio as client:
    response = await client.models.generate_content(...)

# Async chat
chat = client.aio.chats.create(model="gemini-3-flash-preview")

client = genai.Client(http_options=types.HttpOptions(api_version='v1alpha'))

types.Part(
    inline_data=types.Blob(mime_type="image/jpeg", data=image_bytes),
    media_resolution={"level": "media_resolution_high"}  # low|medium|high|ultra_high
)

config=types.GenerateContentConfig(
    tools=[{"google_search": {}}],    # Web search
    # tools=[{"url_context": {}}],    # Fetch URL content
    # tools=[{"code_execution": {}}], # Run code
)

from enum import Enum

class Category(Enum):
    A = "A"
    B = "B"

config={
    "response_mime_type": "text/x.enum",
    "response_schema": Category,
}

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="A cyberpunk city",
    config=types.GenerateContentConfig(
        response_modalities=["IMAGE"],
        image_config=types.ImageConfig(aspect_ratio="16:9", image_size="4K"),
    ),
)
image = response.parts[0].as_image()
image.save("out.png")

response.text           # Text content
response.parsed         # Auto-parsed JSON (when using response_json_schema)
response.function_calls # List of function calls (cleaner than candidates drilling)