Embedding Dimensions
The vector length of an embedding model's output.
/ quick answer
Common dimensions: 384 (small), 768 (base), 1536 (OpenAI text-embedding-3-small), 3072 (large). Higher dimensions capture more nuance at the cost of storage and query speed. The vector length of an embedding model's output.
/ frequently asked
What is Embedding Dimensions?
Common dimensions: 384 (small), 768 (base), 1536 (OpenAI text-embedding-3-small), 3072 (large). Higher dimensions capture more nuance at the cost of storage and query speed.
What is an example of Embedding Dimensions?
OpenAI's text-embedding-3-large returns 3072-dim vectors but supports truncation to 1536 or 512.
Why does Embedding Dimensions matter?
The vector length of an embedding model's output.
What should I learn after Embedding Dimensions?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
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Related concepts
The vocabulary this page depends on.
- →Embedding
A numerical vector representation of text, image or audio that captures meaning for similarity search.
- →Retrieval
Selecting the most relevant chunks for a query before generation.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
Related workflows
Turn this into a repeatable process.
- →How to Create a Website with AI
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- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →How to Start a Niche Website with AI
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- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →ChatGPT vs Claude
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- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.
- →Lovable vs Cursor
Prompt-to-app builder vs AI-assisted code editor — which one should you reach for?