Conditionally licensed reranking search results models
1 in the catalogue today, and the list grows as the market does. Every one with what it costs, who sells it and where it stands.
Models that take a handful of results a search already found and put them in the right order. They are the cheap second stage of retrieval: an embedding search casts a wide net fast, a reranker reads the candidates properly. Because they only ever see a few documents, they cost little per query, and they usually improve a weak search more than a better embedding model would.
Weights you can download, under a licence that asks for something in return: an acceptable-use policy, a naming requirement, a revenue ceiling above which you must ask, a restriction on training other models. The Llama and Gemma families are the familiar cases. Read the actual licence before you build on one of these — the condition is usually easy to meet and occasionally fatal, and which it is depends on your product rather than on the model.