All of these claims, based on benchmarks, don't hold up in the real world on real world tasks. Which is strongly supportive of the statistical model. It will be capable of answering patterns extensively trained on. But is quickly breaks down when you step outside that distribution.
o3 is also a significant hallucinator. I spent quite a bit of time with it last weekend and found it to be probably far worse than any of the other top models. The catch is that it its hallucinations are quite sophisticated. Unless you are using it on material for which you are extremely knowledgeable, you won't know.
LLMs are probability machines. Which means they will mostly produce content that aligns to the common distribution of data. They don't analyze what is correct, but only what is probable completions for your text by common word distributions. But when scaled to incomprehensible scales of combinatorial patterns, it does create a convincing mimic of intelligence and it does have its uses.
But importantly, it diverges from the behaviors we would see in true intelligence in ways that make it inadequate for solving many of the kinds of tasks we are hoping to apply them to. The being namely the significant unpredictable behaviors. There is just no way to know what type of query/prompt will result in operating over concepts outside the training set.