Cohere Embed is Cohere's family of embedding models, offered through an API, that turn text and images into vectors for search, classification and RAG.
In plain words
An embedding is a list of numbers that captures meaning, so similar content ends up with similar numbers. Cohere Embed produces these vectors for you. Older v3 versions come in English-only and multilingual forms; newer ones such as embed-v4.0 and embed-v5.0 also take mixed documents like PDFs, and accept up to 128,000 tokens. The multilingual model supports over 100 languages, and you can pick shorter vectors to save storage.
A quick example
Imagine a company with thousands of scanned PDF reports. It could send each one to Cohere Embed, store the vectors in a vector database, and let staff search by meaning, such as "reports about supplier delays".
Where you'll meet it
- Semantic search and RAG over company documents.
- Classifying text, such as sorting customer feedback.
Related
Sources
- DocsCohere's Embed models, Cohere Docs · read 29 Sept 2026
- DocsCommand A, Cohere Docs · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.