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.
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 for AI and automation?
The vector length of an embedding model's output. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ continue exploring
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
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →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
Pick a niche, validate demand, build the site, and publish ranking content using AI end-to-end.
- →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
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →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?