You will have to re-embed everything if you change to a different model or different dimensional embedding.
One of the more intriguing uses is text-embedding-3-large at dimensions:1024. If you have an existing vector database of fixed dimension where you can segment the search spaces, you can fill the remaining 512 values with -1 or 1, which will put dot products in a completely different embedding search space.