Embed

Embed models can be used to generate embeddings from text or classify it based on various parameters. Embeddings can be used for estimating semantic similarity between two texts, choosing a sentence which is most likely to follow another sentence, or categorizing user feedback. When used with the Classify endpoint, embeddings can be used for any classification or analysis task.

English Models

Latest Model	Description	Dimensions	Max Tokens (Context Length)	Similarity Metric	Endpoints
`embed-english-v3.0`	A model that allows for text to be classified or turned into embeddings. English only.	1024	512	Cosine Similarity	Embed, Embed Jobs
`embed-english-light-v3.0`	A smaller, faster version of `embed-english-v3.0`. Almost as capable, but a lot faster. English only.	384	512	Cosine Similarity	Embed, Embed Jobs
`embed-english-v2.0`	Our older embeddings model that allows for text to be classified or turned into embeddings. English only	4096	512	Cosine Similarity	Classify, Embed
`embed-english-light-v2.0`	A smaller, faster version of embed-english-v2.0. Almost as capable, but a lot faster. English only.	1024	512	Cosine Similarity	Classify, Embed

Multi-Lingual Models

Latest Model	Description	Dimensions	Max Tokens (Context Length)	Similarity Metric	Endpoints
`embed-multilingual-v3.0`	Provides multilingual classification and embedding support. See supported languages here.	1024	512	Cosine Similarity	Embed, Embed Jobs
`embed-multilingual-light-v3.0`	A smaller, faster version of `embed-multilingual-v3.0`. Almost as capable, but a lot faster. Supports multiple languages.	384	512	Cosine Similarity	Embed, Embed Jobs
`embed-multilingual-v2.0`	Provides multilingual classification and embedding support. See supported languages here.	768	256	Dot Product Similarity	Classify, Embed

Frequently Asked Questions

What is the Context Length for Cohere Embeddings Models?

You can find the context length for various Cohere embeddings models in the tables above. It's in the "Max Tokens (Context Length)" column.