Meet the AI Platforms10 min read

Gemini

Google's multimodal AI that sees, hears, and thinks
scope:Core Conceptdifficulty:Beginner

The Company That Built the Brain

Here's a plot twist most people don't know: Google invented the technology behind every modern AI chatbot. In 2017, Google researchers published a paper called "Attention Is All You Need" — it introduced the Transformer, the architecture that powers ChatGPT, Claude, and yes, Gemini too.

But Google didn't rush to release a chatbot. They were careful. They were cautious. And then in November 2022, ChatGPT launched and took the world by storm. Suddenly Google was playing catch-up with their own invention.

First came Bard — Google's initial response. It was rushed, and it showed. Then Google went back to the lab, combined their two legendary AI research teams — Google Brain and DeepMind — and built something far more ambitious: Gemini.

What Makes Gemini Different: Multimodal From Birth

Most AI models started as text-only and had image understanding bolted on later. Gemini was different — it was multimodal from the ground up. That means it was trained from day one to understand:

  • Text — reading, writing, and reasoning with words
  • Images — seeing and understanding photos, diagrams, charts
  • Video — watching and comprehending video clips
  • Audio — listening to and understanding speech, music, sounds
  • Code — reading, writing, and debugging programs

Think of it like this: most AI models are like a person who learned to read first, and later learned to look at pictures. Gemini is like a person who grew up reading, watching, and listening all at once. The connections between senses are built into its core.

The Gemini Family

Google doesn't just make one Gemini. They make a whole family, each sized for different jobs:

  • Gemini Ultra — the biggest and most capable. Used for the hardest tasks: complex reasoning, advanced research, deep analysis. This powers Gemini Advanced.
  • Gemini Pro — the balanced middle child. Smart enough for most tasks, fast enough for real-time use. The default in most Google products.
  • Gemini Flash — the speed demon. Smaller and faster, designed for quick tasks where you need a response in milliseconds. Great for mobile devices and high-volume applications.
  • Gemini Nano — the tiny one that runs on your phone. No internet needed. Powers features like smart reply and summarization right on your device.
Note: Why model sizes matter: Bigger models are smarter but slower and more expensive. A question like "What's the weather?" doesn't need Ultra — Flash can handle it instantly. But "Analyze this 50-page legal document and find contradictions" needs the power of Ultra. Google routes your requests to the right size model automatically.

Gemini Inside Everything Google

Unlike ChatGPT, which lives mainly in one app, Google embedded Gemini everywhere:

  • Google Search — AI Overviews that summarize results instead of just showing links
  • Gmail — "Help me write" drafts emails from a quick description
  • Google Docs — generates, summarizes, and edits documents
  • Google Photos — ask questions about your photos in natural language
  • Android — Gemini Nano runs directly on Pixel phones for on-device AI
  • Google Cloud — businesses use the Gemini API to build their own AI products

This is Google's biggest advantage: they have distribution. Billions of people already use Gmail, Search, and Android every day. Google doesn't need people to download a new app — they put the AI where people already are.

The Long Context Window

One of Gemini's standout features is its massive context window — the amount of text it can process at once. Gemini 1.5 Pro can handle up to 1 million tokens, which is roughly 700,000 words or about 10 full-length novels.

This means you can feed Gemini an entire codebase, a full textbook, or hours of video, and ask questions about any part of it. Most other models have context windows of 100,000–200,000 tokens. Gemini's long context is a genuine technical achievement.

Using the Gemini API

# Using Google's Gemini API (google-generativeai library)
import google.generativeai as genai


# Configure with your API key
genai.configure(api_key="YOUR_API_KEY")


# === Basic text generation ===
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Explain quantum computing to a 10-year-old")
print("Text response:")
print(response.text[:200])


# === Multimodal: image + text ===
import PIL.Image


model_vision = genai.GenerativeModel('gemini-pro-vision')
image = PIL.Image.open('diagram.png')
response = model_vision.generate_content(
    ["What does this diagram show? Explain each part.", image]
)
print("\nImage analysis:")
print(response.text[:200])


# === Chat conversation ===
chat = model.start_chat(history=[])
response = chat.send_message("What is photosynthesis?")
print("\nChat response 1:", response.text[:100])


response = chat.send_message("Now explain it simpler.")
print("Chat response 2:", response.text[:100])


# === Comparing model sizes ===
models = ['gemini-1.5-flash', 'gemini-1.5-pro']
for m in models:
    model = genai.GenerativeModel(m)
    # Flash is faster, Pro is smarter
    print(f"\n{m}: ready for use")
Output
Text response:
Imagine a regular computer is like a light switch — it can
be ON or OFF. That's like a 'bit' which is either 0 or 1.
A quantum computer uses 'qubits' which can be ON, OFF, or
BOTH at the same time! It's like a magic coin that's heads
AND tails while spinning...

Image analysis:
This diagram shows the water cycle. Starting from the bottom:
1. Evaporation — water from oceans heats up and rises...
2. Condensation — water vapor forms clouds...

Chat response 1: Photosynthesis is the process by which green plants convert sunlight...
Chat response 2: Plants eat sunlight! They use light, water, and air to make food...

gemini-1.5-flash: ready for use
gemini-1.5-pro: ready for use

Gemini vs the Competition

How does Gemini stack up against ChatGPT and Claude?

  • Multimodal ability — Gemini was multimodal from birth, giving it deep cross-modal understanding. Others added vision later.
  • Context window — Gemini's 1M+ token window leads the industry. Useful for processing long documents and codebases.
  • Google integration — no other AI is built into Search, Gmail, Docs, Photos, and Android. This is Gemini's moat.
  • Reasoning — on benchmarks, Gemini Ultra is competitive with GPT-4 and Claude. The models trade places depending on the specific test.
  • Cost and speed — Gemini Flash is one of the cheapest and fastest API models available, making it a popular choice for developers.

The reality is that the top models — GPT-4, Claude, and Gemini — are all remarkably capable. The differences often come down to where you want to use the AI and which ecosystem you're already in.

Challenge

Quick check

What foundational technology did Google researchers invent that powers all modern LLMs?

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