Buildathons.ai

LLMs Deep Dive: GPT-4o, Claude, Gemini, Llama Course…

Compare the leading frontier models. Understand context windows, pricing, strengths and weaknesses. Pick the right model for your buildathon project. Follow…

Course overview

Compare the leading frontier models. Understand context windows, pricing, strengths and weaknesses. Pick the right model for your buildathon project.

The Frontier Model Landscape

| Model | Provider | Context | Best For | |-------|----------|---------|----------| | GPT-4o | OpenAI | 128K | Multimodal, function calling, broad tasks | | Claude 3.5 Sonnet | Anthropic | 200K | Long documents, coding, nuanced reasoning | | Gemini 1.5 Pro | Google | 1M (!) | Massive context, code, search grounding | | Llama 3.1 405B | Meta (open) | 128K | Self-hosted, fine-tuning, no API fees | | Mistral Large | Mistral | 32K | Fast, European data sovereignty | Key insight: No single model wins everything. Match model strengths to task types.

Choosing the Right Model

Framework for model selection: 1. Task type — Coding (Claude GPT-4o), Long docs (Gemini/Claude), Vision (GPT-4o Gemini) 2. Latency requirements — Use smaller models (GPT-4o-mini, Claude Haiku) for real-time responses 3. Cost — Claude Haiku and GPT-4o-mini are 10-50x cheaper than frontier models 4. Privacy — Use local Llama via Ollama if data can't leave your infrastructure 5. Context length — Gemini 1.5 Pro handles 1M tokens — full codebases, books, transcripts

Structured Outputs & Function Calling

Modern LLMs support structured JSON outputs, making it easy to parse responses reliably without prompt engineering hacks.