OpenAI Text Embedding 3 vs Cohere Embed

Hosted embedding models with configurable output dimensions.

Feature comparison

Feature comparison of OpenAI Text Embedding 3 and Cohere Embed
Feature	OpenAI Text Embedding 3	Cohere Embed
Dimensions	1536 by default, 256 to 3072 selectable	1024
License	Proprietary	Proprietary
Max tokens	8191	512
Open weights	No	No
Provider	OpenAI	Cohere

Shaded rows are identical for both; the differing rows are the ones that drive the recommendation below.

Which should you choose?

Generated from the differences above by structured rules, not by a model.

Choose OpenAI Text Embedding 3 if…

Nothing in the recorded attributes distinguishes it in your favour.

Choose Cohere Embed if…

Nothing in the recorded attributes distinguishes it in your favour.

OpenAI Text Embedding 3

model · Embedding Models

Text Embedding 3 is OpenAI's hosted embedding family, notable for letting the caller choose a shorter output vector than the model natively produces. It solves the storage problem directly: embedding indexes are sized by dimensions times vectors, so trimming 3072 dimensions to 512 or 256 shrinks the index substantially at a small quality cost. It exists because that trade is one the caller is better placed to make than the model author, and it removes the need to retrain to change it.

Cohere Embed

model · Embedding Models

Cohere Embed is a hosted embedding model that distinguishes the query from the document being indexed, and supports a hundred languages in one model. It solves the asymmetry problem explicitly: a question and a passage are not the same kind of text, and encoding them with the same mode is a compromise. It exists because multilingual retrieval otherwise means either a separate model per language or one model doing all of them badly.