LLM Reference
Google AI Studio

Using Gemini Deep Research Preview on Google AI Studio

Implementation guide · Gemini 3.1 · Google DeepMind

Serverless

Google AI Studio exposes Gemini Deep Research Preview through model ID deep-research-preview-04-2026. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-06-15. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at Google AI Studio and generate an API key.
  2. 2
    Use the Google AI Studio SDK or REST API to call deep-research-preview-04-2026 — see the documentation for request format.
  3. 3
    You'll be billed $2.00/1M input, $12.00/1M output tokens. See full pricing.

Code Examples

Install
pip install google-genai
API key
GOOGLE_API_KEY
Model ID
deep-research-preview-04-2026

Use the model name directly, e.g. "gemini-2.0-flash", "gemini-1.5-pro", or "gemini-2.5-pro-preview-05-06".

import os
from google import genai

client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
response = client.models.generate_content(
    model="deep-research-preview-04-2026",
    contents="Hello"
)
print(response.text)

Pricing on Google AI Studio

TypePrice (per 1M)
Input tokens$2.00
Output tokens$12.00

Capabilities

VisionMultimodalJSON / Tool useStructured OutputsAudio

About Gemini Deep Research Preview

Google's agentic deep research model built on Gemini 3.1 Pro, released April 21, 2026. Designed for speed and efficiency in autonomous multi-step research: ingests text, images, PDFs, audio, and video to produce comprehensive cited reports from public web sources and private workspace data. Supports collaborative planning, visualization, MCP servers, and File Search. Context window: 1M tokens; max output: 65,536 tokens. Runs at Gemini 3.1 Pro rates ($2.00/$12.00 per MTok). API ID: deep-research-preview-04-2026.