MTEB is the Massive Text Embedding Benchmark, a public leaderboard scoring embedding models across dozens of retrieval, clustering, and classification tasks. A higher average MTEB score generally indicates better embedding quality.

Do more dimensions always mean better quality?

No. More dimensions can capture more nuance but cost more to store and search, and some lower-dimension models outperform larger ones on MTEB. Match dimensions to your storage budget and recall needs.

Are these prices current?

Prices are list-price estimates and clearly labelled. Providers change pricing frequently, so confirm the current rate in your provider's dashboard before budgeting.

Can I shorten OpenAI embedding dimensions?

Yes. text-embedding-3 models support Matryoshka dimension truncation, so you can request fewer dimensions (e.g. 256 or 512) to cut storage cost with only a small quality loss.

Embedding Model Comparison Table

Compare embedding models at a glance

Choosing a text embedding model means balancing retrieval quality, cost, and vector size. This table puts the major options side by side — OpenAI, Cohere, Voyage, and leading open-source models — with their dimensions, maximum input length, price per million tokens, and MTEB benchmark score, so you can pick the right model for your RAG or search index.

How to read the table

Dimensions is the length of each output vector. Larger vectors cost more to store and search but can encode more meaning.
Max tokens is the longest input the model accepts in a single call; longer documents must be chunked.
Cost / 1M tokens is the embedding price — embeddings are far cheaper than generation, but at billions of tokens it adds up.
MTEB is the average benchmark score. Treat differences of a point or two as noise; large gaps are meaningful.

Filter by provider, search by model name, and click a column header to sort.

Tips for picking a model

For most production RAG, a mid-tier model like text-embedding-3-small or voyage-3-lite hits the best quality-per-dollar.
If storage dominates your bill, prefer a model that supports dimension truncation and store 256–512 dims.
Always re-embed your whole corpus when you change models — vectors from different models are not comparable.