rerank-3

This model turns written text into compact number lists that a search system can compare, so it is built for finding the right passage in a large pile of documents rather than for writing prose. It reads up to about 32 pages in one go and can handle text in many languages, which most text models of this kind do not. It gives back no words at all, so it cannot draft, summarise or answer questions on its own; that work needs a model that writes text.

This model turns written text into compact number lists that a search system can compare, so it is built for finding the right passage in a large pile of documents rather than for writing prose. It reads up to about 32 pages in one go and gives back no words at all, so it cannot draft, summarise or answer questions on its own; that work needs a model that writes text. It is the lightest of its family, tuned to answer quickly and cheaply, which makes it a fit when you have a great many passages to sort through and speed matters more than the finest match.

This model turns written text into long lists of numbers that a search system can compare, so it is built for finding the right passage in a large pile of documents rather than for writing prose. It reads up to about 32 pages in one go and handles text in many languages, which most models of this kind do not. Its number lists can be shortened to save storage space without losing much of the match quality, a trick few of its kind offer. It gives back no words at all, so it cannot draft, summarise or answer questions on its own; that work needs a model that writes text.

voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048t voyage-code-4 here: [mongodb.com/products/platform/ai-search-and-retrieval/models](https://www.mongodb.com/products/platform/ai-search-and-retrieval/models?utm_source=OpenDunes&utm_medium=referral)