Model Sizes and Variants

One size does not fit all in AI. You wouldn't use a Ferrari to tow a boat, and you wouldn't use a semi-truck to commute to the grocery store. Google provides a gradient of Gemini models so you can optimize for Latency, Cost, or Intelligence.

In this lesson, we break down the four main classes: Nano, Flash, Pro, and Ultra.

1. Gemini Nano (The Edge Model)

Nano is the smallest version, optimized to run locally on devices like smartphones (Pixel 8+, Samsung S24) and laptops.

• Where it runs: On the NPU (Neural Processing Unit) of the device. NO internet connection is needed.
• Privacy: 100% private. Data never leaves the user's phone.
• Latency: Near instant (no network round trip).
• Capabilities: Summarization, smart replies, grammar correction, basic reading comprehension.
• Limitations: Small context, limited reasoning, can't handle complex chain-of-thought.

2. Gemini Flash (The Speedster)

Flash (specifically gemini-1.5-flash) is a breakthrough model. It uses "Distillation"—it was taught by larger models how to be smart but efficient.

• Key Stat: It is the fastest and cheapest option in the API.
• Context: Supports the full 1M+ token window!
• Use Cases:
  • High-Volume Tasks: Classifying 10,000 emails.
  • Real-time Chat: Customer support bots where speed matters more than Einstein-level genius.
  • Video Analysis: Because it's cheap, you can afford to feed it hour-long videos.
• My Recommendation: Default to Flash. Only upgrade to Pro if Flash fails the task.

3. Gemini Pro (The Generalist)

Gemini Pro (gemini-1.5-pro) is the "flagship" model. It balances high intelligence with reasonable cost.

• Performance: Comparable to GPT-4 Turbo.
• Reasoning: Excellent at complex instructions, coding, and creative writing.
• Multimodality: Capable of very fine-grained video and image analysis (e.g., "Read the tiny text on the label in this blurry photo").
• Use Cases:
  • Complex coding agents.
  • Legal contract analysis.
  • Reasoning-heavy RAG applications.

4. Gemini Ultra (The Genius)

Ultra is the largest parameter model, designed for state-of-the-art (SOTA) performance on benchmarks like MMLU.

• Availability: Usually reserved for "Gemini Advanced" subscriptions or enterprise tiers.
• Strength: Solving novel problems, scientific nuances, and extremely subtle reasoning tasks.
• Trade-off: It is slower and significantly more expensive than Flash/Pro.

The Decision Matrix

How do you choose?

Requirement	Recommended Model
"I need it to run on a phone without Wi-Fi."	Nano
"I have 10,000 documents to process cheaply."	Flash
"The prompt requires creative nuances and logic."	Pro
"I need to answer questions about a 1-hour video."	Flash (Cost/Speed) or Pro (Accuracy)
"I am building a coding assistant."	Pro (Better at syntax/logic)

Code Example: Switching Models

In the SDK, switching is as simple as changing a string.

import google.generativeai as genai

# For speed
fast_model = genai.GenerativeModel('gemini-1.5-flash')
response = fast_model.generate_content("Quick summary of this text...")

# For complex logic
smart_model = genai.GenerativeModel('gemini-1.5-pro')
response = smart_model.generate_content("Analyze the legal implications of this clause...")

Summary

• Start with Flash. It is surprisingly capable and very cheap.
• Upgrade to Pro when you hit a reasoning ceiling.
• Use Nano only if you are building mobile/desktop native apps.

In the next lesson, we will look at Embeddings—the numerical representation of data that powers search and retrieval.