What is the difference between a token and an embedding?

A token is a chunk of text with an id number, a symbol. An embedding is the list of numbers the model learned to attach to that id: a position in a space of meaning. The token says which word, the embedding carries what the model knows about it.

How many numbers are in one embedding?

Hundreds to a few thousand. Small retrieval models use 384 or 768 dimensions, large ones several thousand. More dimensions can hold finer distinctions and cost more to store and compare, which is why retrieval systems often pick a smaller model on purpose.

Can I compare embeddings from two different models?

No. Each model learns its own space, so the same sentence lands somewhere unrelated in another model's coordinates. If you switch embedding models, everything you had stored has to be embedded again.

Plain-language explainer

Embeddings, explained visually

What is an embedding, and how does it capture meaning?

An embedding turns a piece of text into a list of numbers, a vector, positioned so that similar meanings sit close together. Meaning becomes geometry. Words and sentences that are used in similar ways end up near each other, even when they share no exact words. That is what lets a search box find the right help article from a different phrasing, and what lets retrieval pull the relevant document for an AI answer. The model learns these positions from how language is actually used.

Last reviewed July 25, 2026

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What people get wrong

Embeddings store the definition of a word. They store a position learned from usage, not a dictionary entry.
Matching means sharing keywords. Embeddings match on meaning, so 'car' and 'automobile' land close without overlapping letters.
One embedding is the true meaning of a word. Meaning shifts with context, which is what attention later adjusts.

Where you see it in real products

Semantic search and site search rank by vector closeness.
RAG uses embeddings to fetch the documents most related to a question.
Recommendations and dedup group items that sit near each other in vector space.

Common questions

What is the difference between a token and an embedding?: A token is a chunk of text with an id number, a symbol. An embedding is the list of numbers the model learned to attach to that id: a position in a space of meaning. The token says which word, the embedding carries what the model knows about it.
How many numbers are in one embedding?: Hundreds to a few thousand. Small retrieval models use 384 or 768 dimensions, large ones several thousand. More dimensions can hold finer distinctions and cost more to store and compare, which is why retrieval systems often pick a smaller model on purpose.
Can I compare embeddings from two different models?: No. Each model learns its own space, so the same sentence lands somewhere unrelated in another model's coordinates. If you switch embedding models, everything you had stored has to be embedded again.

Related explainers

How LLMs work RAG Attention Vector databases

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