How to choose an AI model without getting lost in benchmarks

Leaderboards change every week. A simple test with your own tasks tells you more than any chart.

Every launch brings a new chart showing that one model beat the rest. Those benchmarks have value, but they measure standardized tasks. What matters to you is how a model performs on the work you actually do.

Start with the task

List the three tasks that take up most of your time. That might be summarizing reports, answering customers, or reviewing code. Each one calls for something different: accuracy, tone of voice, or the ability to handle long documents.

Set up a 30-minute test

• Pick five real examples of each task.
• Send the same prompt to two or three models.
• Grade each result before you look at which model produced it.
• Note how much you had to fix in each response.

Time spent fixing is often the best metric. A brilliant answer that takes ten minutes to fact-check can be worth less than a simple one that is ready to use.

Consider the cost per task

If you use models through an API, compare the cost of finishing the task, not just the price per token. A cheaper model that needs three tries can end up costing more than a better model that gets it right the first time.

The best model is the one that gets your task done with the least revision.

Educational content only. Nothing here is investment advice.