There are now hundreds of AI models available through APIs, and a new one appears almost every day. The good news: you rarely need "the best model". You need the cheapest model that does your task well. Five questions get you there.
1. What exactly is the task?
Write down two or three real examples of what you want the model to do. "Summarise customer emails in two lines" is a very different job from "fix bugs in a large codebase". Simple, repetitive tasks (classification, extraction, short replies, translation) are handled well by small, cheap models. Complex tasks (multi-step reasoning, long code changes, research, agents that use tools) are where flagship and reasoning models earn their price.
2. How much text does it need to read?
Check the context window. A chat bot needs little; analysing a 300-page contract or a whole code repository needs a large window. Remember that the window holds everything: instructions, conversation history, documents and the answer. Our models database lets you sort by context size.
3. What is your budget per month?
Prices differ by more than 100× between the cheapest and most expensive models. Estimate your volume (requests per day, tokens per request) and put it in the cost calculator. You will often find that a mid-priced model costs a few dollars a month for your usage, and that the premium model costs a few hundred. That gap is the real decision.
4. Do you have constraints on data or hosting?
If your data must stay on your own servers, or you need to work offline, look at open-weight models you can run yourself. If you need specific features, filter for them: image input, tool use (for agents), structured output (for reliable JSON), or reasoning.
5. Test on your own examples
Benchmarks and leaderboards are a starting point, not an answer. Take your 20 or 30 real examples, run them through two or three candidate models, and compare the outputs side by side. It takes an hour and it is the most reliable way to choose. Start from the cheapest candidate and only move up if the quality is not good enough.
A simple rule of thumb
- High volume, simple task: a "mini", "flash", "lite" or small open model.
- Everyday assistant, writing, general coding: a mid-range model from a major lab.
- Hard problems, long autonomous tasks, critical code: a flagship or reasoning model.
Use the comparison tool to see any shortlist side by side, and check the change log now and then: prices fall often, and today's premium model may be tomorrow's bargain.