LANDMARK · 7 MIN · BUILD
Choosing a model
After this landmark, you can choose a model against your actual constraints (task fit, latency, cost, and data handling), instead of by general reputation.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
Model choice gets treated as a single ranking, ‘which model is smartest’, when it’s really a multi-constraint decision: does it handle this task type (long documents, structured output, tool use, a specific language), does it fit your latency and cost budget at your expected volume, and does its data-handling terms fit what you’re allowed to send it. A frontier model that’s excellent and slow and expensive can be the wrong choice for a feature that needs to run in under a second at high volume; a smaller model tuned for the task can beat it on the metric that matters.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
You don’t choose a model directly, but you do choose a tool; a fast, free assistant is fine for brainstorming; a task that needs care is worth using a stronger, slower one for.
MAKE A DECISION
A feature needs to classify support tickets into five categories, running on every incoming ticket, thousands per day. How should the model be chosen?
CARRY THISFor one AI feature you’re building or maintaining, write down its latency budget and cost-per-request ceiling. Check whether the current model choice actually fits them.