Comparison
Embedding vs Token
Embedding and Token are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Embedding
Embedding comes up when the question is fundamentally about architecture.
OpenAI's text-embedding-3-large produces 3,072-dim vectors.
When you would reach for Token
Always think in tokens, not characters, when planning prompts, budgets, and context windows.
"Hello, world!" tokenizes to roughly 4 GPT-4o tokens.
Frequently asked
What is the difference between Embedding and Token?
Embedding: An embedding is a list of numbers (a vector) that represents a piece of input — a word, a sentence, an image — in a space where similar things end up close together. Token: A token is the basic unit an LLM reads and writes — usually a word piece (3-4 characters). LLMs are priced and sized by tokens, not words.
When should I use Embedding vs Token?
Embedding is the right concept when you are focused on architecture. Always think in tokens, not characters, when planning prompts, budgets, and context windows.
Are Embedding and Token the same thing?
No. Embedding is architecture; Token is inference. They are related but address different parts of the AI stack.