Model Comparison

Introduction

Choosing the right LLM is a cost/quality/latency trade-off. There's no single 'best' model — only the best for your task. Always benchmark on your data.

Beginner analogy: Like choosing a car — Ferrari (GPT-4) for tough jobs, Toyota (GPT-4o-mini) for daily driving, F1 racer (o1) for the hardest reasoning.

Understanding the topic

Core concepts to understand:

• GPT-4o — top all-rounder, multimodal, great latency.
• Claude 3.5 Sonnet — best at coding & long context.
• Gemini 1.5 Pro — biggest context (1M+), great multimodal.
• GPT-4o-mini / Haiku / Flash — cheap, fast workhorses.
• o1, o3, DeepSeek R1 — frontier reasoning (slow, expensive).

Syntax reference

Visual workflow / architecture:

Cost      │ Frontier reasoning (o1, o3, R1)
   ▲      │ Top quality (GPT-4o, Claude 3.5)
   │      │ Workhorse (mini / Flash / Haiku)
   │      │ Open source (Llama, Mistral)
   └──────┴────────────────────────────►  Quality

Real-world use

Production systems often use multiple models: cheap models for first-pass, expensive models for hard cases, fallback chains for outages.

Best practices

• Start with a cheap model; escalate only when accuracy fails.
• Build a model-agnostic abstraction so you can swap providers.
• Track per-task accuracy/cost in evals.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How do you pick an LLM?
• Q2. When would you use multiple models in one product?
• Q3. Why benchmark on your own data?