Why do different AI models give different answers to the same question?
Different AI models give different answers because they were trained on different data, with different methods, and tuned by different companies. On questions with a single well-established answer, they mostly agree. Where they diverge is usually where the question is genuinely uncertain, underspecified, or contested. That makes disagreement between models useful rather than annoying: it is a map that points straight at the part of your question that needs a human decision, instead of hiding it under one confident answer.
Same question, different machines
No two models are the same. They read different slices of the internet, were trained with different techniques, and were tuned toward different behaviors by different companies. So on anything that is not a settled fact, they will not produce identical answers. This surprises people who expect AI to be like a calculator. It is not. It is more like asking several well-read strangers.
Why that is good news
A single model hides its uncertainty. It gives you one confident answer whether the question is settled or wide open, and you cannot tell which from the answer. Several models expose the uncertainty. Where they line up, the answer is probably solid. Where they fork, you have found the real question, the part that actually needs your judgment or a real source.
The method built on this idea is to read the disagreement first: concentrate on where the models split, force those claims to prove themselves, and resolve them on evidence. That is the heart of the Council Method. It is also the answer to should I use more than one AI model: yes, precisely because the differences are informative.
The full approach is in Let the AI Be Smart.
Go deeper: this site's hub page on the Council Method is the full definition. Related questions: Should I use more than one AI model?, How do I know if an AI answer is correct?, What is the Council Method?.