Every LLM vendor publishes benchmark charts, and almost none of them will tell you which model your product actually needs. The right question is not which model is smartest, but which model is the cheapest and fastest one that clears your quality bar for a specific task.
Start from the task, not the model
Classification, extraction, and reformatting are undemanding tasks where small, fast models shine. Multi-step reasoning, nuanced writing, and code generation justify frontier models. Most products contain a mix, so plan for a portfolio of models rather than a single choice.
The three axes that matter
- Quality: measured on your data with your prompts, never on public benchmarks alone.
- Latency: a chat interface can hide two seconds; an autocomplete feature cannot hide two hundred milliseconds.
- Cost: price per token times tokens per request times requests per day. Long context windows quietly multiply this.
Build an evaluation harness first
Before comparing models, collect fifty to a hundred representative inputs and define what a good output looks like for each. Even a crude rubric scored by a stronger model will rank candidates more honestly than intuition. Re-run the harness whenever vendors ship updates, because model behavior drifts under the same API name.
Keep your exit open
Abstract the model call behind a thin internal interface so you can swap providers without rewriting features. Log prompts and completions from day one; that corpus becomes your evaluation set, your fine-tuning data, and your negotiating leverage. Teams that treat the model as a replaceable component consistently ship faster than teams that weld their product to one vendor.