Choosing the Right LLM: A Practical Framework

Ask which model is "the best" and you'll get a leaderboard. Ask which model is best for your task, your budget, and your latency target, and you'll get a much more useful — and much more boring — answer. Model selection isn't about chasing the top of a chart; it's about fit.

Start with the task, not the model

A model that's overkill for classification and a model that's underpowered for multi-step reasoning are both the wrong choice. Define what the task actually requires — accuracy, reasoning depth, context length, structured output — before you look at any model at all. The requirements should narrow the field; the leaderboard shouldn't widen it.

Match capability to need

Lots of production work — extraction, routing, summarization, tagging — runs beautifully on smaller, cheaper, faster models. Save the frontier models for the genuinely hard reasoning, and you'll often cut cost by an order of magnitude with no drop in quality where it counts.

The three numbers that decide it

Once a model clears the quality bar, the decision usually comes down to three numbers: cost per request, latency per request, and reliability under load. A model that's marginally smarter but twice as slow and three times the price is the wrong call for most user-facing features. Optimize for the experience, not the benchmark.

• Quality — does it clear the bar on your evals, not someone else's?
• Cost — what does it cost at your real request volume?
• Latency — is it fast enough for the experience you're building?
• Portability — can you switch providers without a rewrite?

Build your own evals

Public benchmarks measure general capability on generic tasks. They tell you almost nothing about how a model performs on your data, your edge cases, your tone. A small, honest eval set built from your real workload is worth more than every leaderboard combined — it's the only test that measures the thing you actually ship.

“The right model is the cheapest, fastest one that still passes your evals. Everything above that is paying for capability you don't use.”

Revisit the decision

The model landscape moves monthly. A choice that was optimal at launch can be beaten on price or speed a quarter later. If you've abstracted cleanly and kept your evals, re-evaluating is cheap — and that optionality is itself a competitive advantage.