By Cohere
One of the key improvements in Embed v3 is its ability to evaluate how well a query matches a document’s topic and assesses the overall quality of the content. This means that it can rank the highest-quality documents at the top, which is especially helpful when dealing with noisy datasets. Additionally, we’ve implemented a special, compression-aware training method, which substantially reduces the cost of running your vector database. This allows you to efficiently handle billions of embeddings without causing a significant increase in your cloud infrastructure expenses.1
Cost of usage is $0.10/1M tokens.2