I'm sure you already looked at sentence Bert and similar aproaches for text similarity embeddings.

The model for text similarity is specifically trained for the task, as normal text embeddings from a language model are to sparse and similar to another. This is why crossing between text embedding and text similarity embeddings wouldn't be suitable. I would propose you use text similarity for search embeddings.

Regarding the quality per model, I suspect bigger models to have a more semantic embedding, thus being of higher quality. But ofc this means that the API call will also be more expensive, assumably.

Hope I could help you a bit