All AI companies claim their model is the best. How will you know which one suits you?

An article explaining how to read AI model benchmark results, with expert insights on test limitations, data contamination, and a recommendation to test models on personal tasks rather than relying solely on scores.

This article examines the use of benchmarks for comparing AI models and explains how to read the results published by AI companies. Omer Nevo, CTO of Irregular, explains that each benchmark measures a specific capability, but there is no perfect correlation between scores and real-world use. He warns against data contamination, where models learn answers from training materials. Nevo recommends looking for results in tests relevant to personal use and paying attention to the number of tests a company publishes. Tzof Bar-Or, CTO of Super8, compares the choice to buying a car and emphasizes the importance of price, wait time, and usage limitations. The main recommendation is to conduct a self-test on real tasks and compare results between different models.