How to choose the right LLM for your task
"Which AI model should I use?" has no single answer, because the best model is the one that clears your bar on your task at a price and speed you can live with. Chasing whichever model topped a leaderboard this week is how teams overpay for capability they don't need. Here's a practical way to choose.
Start from your task, not the rankings
Different jobs reward different models. Hard reasoning, long-context analysis, and agentic tool use favor the largest frontier models; classification, extraction, summarization, and routing are often handled well by smaller, cheaper ones. Write down what you're actually asking the model to do most of the time — that profile, not a general "smartest model" label, should drive the choice.
Balance the four real constraints
Every choice trades off capability, cost, speed, and privacy. The frontier model is usually the most capable and the most expensive and often slower; a smaller or open model can be dramatically cheaper and faster and self-hostable. Decide which constraints are hard for you. A customer-facing feature at scale weighs cost and latency heavily; a research assistant weighs raw capability.
Open weights vs. closed APIs
Closed, hosted models (called via API) are the fastest to start with and usually lead on the hardest tasks, but you don't control them and your data leaves your boundary. Open-weight models you can run yourself give control, privacy, and fixed cost, at the price of running infrastructure and sometimes a capability gap. Many stacks end up hybrid — closed for the hardest queries, open for high-volume routine work.
Benchmark against your own tasks
Public benchmarks narrow the field; they don't make the decision. Assemble a dozen real examples from your work, run your two or three finalists against them, and judge the outputs yourself. This tiny eval takes an afternoon and tells you what no leaderboard can: which model is better at your job. Re-run it when you consider switching — "everyone says it's better" is not the same as "better for us."
Latency and limits shape the experience
Beyond raw quality, two practical factors decide whether a model is usable: how fast it responds and how much it can handle at once. A brilliant model that takes ten seconds per reply is wrong for an interactive chat; a fast one with a small context window is wrong for analyzing long documents. Check typical response time, the context-window size, and any rate limits against your real usage pattern before committing — day to day, these often matter more than a few points on a benchmark.
Don't marry one model
Models are deprecated, repriced, and leapfrogged constantly. Architect so swapping models is a config change, not a rewrite: keep prompts and tools model-agnostic where you can, and keep your eval set so you can re-test a new option quickly. The right answer this quarter will likely be the wrong one next quarter, and that's fine if switching is cheap.