I probably don't understand the modern, complex models. But doesn't it basically predict the next token given the context and the better models use more training data and can consider a larger context, and have more parameters to better retain information from the training data etc.

But the fundamental way they operate is the same - predicting the next token given previous tokens. Where/how does reasoning happen here?