Two different numeric representations of text that get lumped together as "the numbers the model uses" — but only one of them has any idea what the text means.
It's easy to hear "the model turns text into numbers" and assume that's one process with one kind of output. It isn't. A token ID and an embeddingare both numeric stand-ins for a piece of text, and that's roughly where the similarity ends. One is an arbitrary lookup index with no meaning baked in at all. The other is a carefully learned coordinate in a space specifically built so that distance corresponds to meaning. Mixing them up is why people occasionally expect semantic search to work by comparing token IDs — which fails for a reason that's obvious once you see the two side by side.