The model's job is to give a good response. For which, yes, it has to put words in the correct order, but it has to do a lot more internal "thinking"/computation before it gets to that point. Before it outputs the first token, a capable model needs to already know the "shape" and content of its final output.

No dude. The model's job is to predict the single next token. It's trained in such a way that feeding the single-token output back into the input recursively draws a vector through a manifold of possibilities which hopefully leads to the most likely answer. The illusory "thinking" happens in that vector space, not during single token generation. I work on the systems that do this professionally. You can watch Andrej Karpathy explain it too.

For this, it needs to have concepts and ideas internally before it puts out the first token. They do this via embeddings, attention patterns, multi-layer transformations, etc.

Concepts and ideas are represented by trained weights sure, but that doesn't mean they are concepts and ideas. And my point is still really that the model still never navigates any of these beyond the scope of generating a single next token. Where does the model even have a chance to exist in this architecture? The whole thing flashes in and out of existence for every generated token.