Do different AI models “think” in different ways, or is it just training data?

I’ve been comparing responses from multiple models lately, and something interesting keeps happening:
Some AIs jump to conclusions fast, some question the premise, and some explain their reasoning step-by-step.
It almost feels like different thinking styles, even though it’s all math.
Curious how others interpret things, real difference in reasoning or just dataset variance?