How to Choose the Right AI Model
Learn a simple, repeatable way to choose a model for a real project.
You now know where models come from, how they’re offered and how they’re scored. Time to actually choose one. The temptation is to grab whichever model topped the news this week. A better habit is a short, repeatable routine.
Step 1: check the walls. Some requirements aren’t trade-offs, they’re rules. Must data stay on your own servers? Then only models you can run yourself qualify. Must it read 200-page contracts? Then the context window must be big enough. Must it work offline, answer in under a second, handle Japanese? Every “must” removes candidates before any scoring starts.
Step 2: weigh the trade-offs. Among what’s left, the familiar triangle from Track 4 applies: quality, speed and cost pull against each other. The trick is to weigh them by what this project needs. Real-time autocomplete for millions of users lives or dies on speed and cost; a slightly cleverer suggestion is worth little. A legal-contract reviewer is the opposite: an error costs far more than a slow or expensive answer.
Step 3: shortlist and test. Pick two or three candidates and run them on 20 to 50 of your own examples, as in the last lesson. Look at the failures, not just the score: some mistakes are harmless, others are unacceptable.
Step 4: start smaller than you think. Teams often prototype with the strongest model to prove an idea works, then try a cheaper model on the same tests and switch if it’s good enough. Many products route requests: a small, fast model for the easy majority, a big one for the hard few.
And keep your tests. Models improve and prices fall every few months, so rerunning them is how you know when to switch.
Key takeaways
- First apply the hard requirements (privacy, context length, offline, latency): they rule models out.
- Then weigh quality, speed and cost by what this project actually needs.
- Shortlist two or three models and test them on your own examples, looking closely at the failures.
- Prototype strong, then try smaller and cheaper; many products route easy and hard requests to different models.
Check your understanding
Go deeper (more technical)
Routing can be as simple as a rule (long or technical questions go to the big model) or a small classifier that predicts which model a request needs. A related pattern is the cascade: try the cheap model first, check the answer, and only escalate to the expensive one if the check fails.
Other practical factors: the provider’s reliability and support, data-retention terms, regional availability, rate limits, and whether the model supports the features you need, such as structured outputs, tool calling or image input.
The simulation’s models and ratings are generic.