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Google Gemini

Gemini is Google DeepMind's family of multimodal large language models, built to understand and generate text, images, audio, and video in a single model. It's currently the #1 most-referenced AI model tag in developer and product usage data, at roughly 29.2% share. Its momentum comes from being baked directly into Google's own products (the Gemini app, Search AI Overviews, Workspace) while also being openly available to developers through an API — giving it both consumer scale and builder reach at once.

Quick facts
Type: Multimodal large language model family (text, image, audio, video)
Made by: Google DeepMind
License/Access: Proprietary — free tier in Google AI Studio + paid usage-based API
Language/Platform: Accessed via REST API / SDKs (Python, Node, Go, Java); used inside Google apps
Primary use case: General-purpose AI assistant tasks — chat, coding help, document/image understanding, and app integrations via API

Jump to: Example Getting started Best for

Example

The Gemini API is called through Google's official Python SDK. This sends a prompt to the gemini-2.0-flash model and prints the generated text.

from google import genai

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Explain what a REST API is in two sentences."
)

print(response.text)

Getting started

Get a free API key from Google AI Studio, install the SDK, then make your first call.

# 1. Get a free API key at aistudio.google.com/apikey

# 2. install the SDK
pip install google-genai

# 3. set your key and run a Python script that calls generate_content()
export GEMINI_API_KEY="your-key-here"

Best for: Teams building AI features that need to reason over mixed media — a single Gemini call can read an uploaded image or PDF and a text question together — and for anyone already inside the Google Cloud/Workspace ecosystem who wants tight integration.