Choosing the Right Model

The Fundamental Trade-Off

Frontier AI models exist on a spectrum from small-and-fast to large-and-capable. Bigger models generally produce better outputs on complex tasks — but they cost more per token and respond more slowly. Smaller models are cheap and fast — but they struggle with nuanced reasoning, edge cases, and long-context tasks. The right choice depends on your task's complexity, your latency requirements, and your cost tolerance. Using a frontier model for tasks a smaller model handles perfectly is just paying more for no gain.

Capability vs Cost

A model like Claude Opus or GPT-4o is typically 50–100x more expensive than a small model like GPT-4o mini or Claude Haiku, per token. For a high-volume application that processes millions of requests per day — classifying customer support tickets, extracting entities from receipts, generating short product descriptions — that cost difference is enormous. Small models handle these tasks well. Reserve frontier models for tasks that genuinely require their capabilities: complex multi-step reasoning, nuanced creative writing, in-depth analysis of long documents.

Latency Considerations

Response speed matters in user-facing applications. Small models typically respond in under a second for short prompts; frontier models can take 3–15 seconds for complex requests. For real-time chat interfaces, latency is as important as quality. For batch processing pipelines running overnight, throughput and cost per call matter more than response time. Always test your latency requirements before committing to a model choice.

A Simple Decision Framework

• Routine classification, extraction, or FAQ: small model (Haiku, GPT-4o mini)
• General chat, summaries, moderate-complexity writing: mid-tier (GPT-4o, Claude Sonnet)
• Complex reasoning, long-context analysis, expert-level tasks: frontier (Claude Opus, GPT-4o)
• Ultra-low cost, high volume, simple tasks: consider open-source models (Llama 3, Mistral) hosted on your own infrastructure