One Model or Many? What Extra AI Readers Actually Buy

Should a company build its AI systems around one "best" model, or use many? This project set out to answer that with real experiments instead of opinions, and it shares everything: the paper, the code, and all the raw results, so anyone can check the work. Being able to switch models easily is always worth it. The "best" AI model keeps changing (18 times in about 3 years), companies retire old models on short notice, and every provider has outages. If switching is cheap for you, none of that hurts much. Running several models on the same task, though, usually is not worth it. It costs 2 to 3 times more, and most of the benefit comes from simply asking one good model a few times rather than from using different brands. Paying for a mix of vendors only helps in a narrow case: when a mistake is very costly, you can tell a right answer from a wrong one, and your best model does not already get everything right. At the very top, today's strongest models got every test question correct, so extra models added nothing. The tests were built so the right answer was always known in advance, and no AI was ever used to grade another, so the scores are fair.