AI model by OpenAI
text-embedding-3-large for business
OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- Reviewed on September 24, 2026
- OpenAI
Capability tiersRelative, not benchmarks
Key facts about text-embedding-3-large
- Model ID at review
- text-embedding-3-large
- Provider
- OpenAI
- Main category
- Embeddings
- Open weights
- No, available as a hosted service
- Last reviewed
- September 24, 2026
Fit
Where it fits and where it does not
Good at
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Semantic search across documents.
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Retrieval for knowledge assistants.
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Clustering and finding similar items.
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Multilingual text.
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High-quality vectors where accuracy matters more than storage.
Not the right choice for
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Images or audio, which it does not embed.
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Chunks longer than 8,192 tokens.
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Very tight storage budgets, where smaller vectors help.
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Generating text answers.
Use cases
Business use cases we would use it for
Knowledge search
Duplicate detection
Our notes
When we would choose it
OpenAI describes text-embedding-3-large as its most capable embedding model. It turns text of up to 8,192 tokens into vectors of 3,072 dimensions, which you store in a vector database and search by meaning rather than exact words. At the time of review, no newer OpenAI embedding model is listed.
The embedding model is the part of a knowledge assistant that users never see, but it decides whether the right passage is found at all. A strong language model cannot answer well from the wrong text. That is why we test retrieval separately, with real questions and the passages that should answer them.
Compared with text-embedding-3-small, the large model costs more and stores bigger vectors. Whether the quality gain is worth it depends on your documents. We would also test Cohere Embed, Voyage and Gemini Embedding, especially if you need images or strong multilingual search.
Adding a rerank step after retrieval is often the cheapest way to improve results. See RAG knowledge assistants and database design.
In our experience, the gains from a stronger embedding model are largest for technical or specialist content, where small differences in wording matter. For general help content, the small model is often close enough, and the savings on storage add up.
Before you commit
Things to check before you commit
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Vector storage
Large vectors take more space. OpenAI lets you request fewer dimensions if needed.
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Switching cost
Changing embedding models means re-embedding every document.
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Retrieval quality
Test on real questions with known answers before choosing.
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Data terms
Documents are sent to the API. Confirm data terms and region.
Alternatives
Models to compare it with
text-embedding-3-small
OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
Cohere Embed v4
Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
Voyage 4
Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
Gemini Embedding 2
Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
Keep exploring
Solutions, services and guides
Related services
View all related services- RAG knowledge assistants Assistants that answer questions from your own documents and show where each answer came from.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
Solutions
View all solutions- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
Industries
View all industries- Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
- Legal Contract review assistants, knowledge search, intake and document automation for law firms and in-house legal teams.
- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
Case studies
View all case studiesGuides and articles
View all guides and articles- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
MCP servers
View all mcp servers- Chroma Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
- Confluence Atlassian's official Rovo MCP server lets AI search and read Confluence spaces and pages, create and edit content and add comments and labels.
- Memory The MCP project's Memory reference server gives AI a local knowledge graph to store people, things and facts and recall them later.
- Obsidian A community MCP server that lets AI list, read, search, edit, append to and delete notes in an Obsidian vault through the Local REST API plugin.
- Pinecone Pinecone's official developer MCP server lets AI search Pinecone docs, manage indexes, upsert records, search and rerank.
- Qdrant Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
Glossary terms
View all glossary terms- RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Chatbot A chatbot is software that holds a conversation with people through text or voice, answering questions or completing simple tasks.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
Turning text into vectors for semantic search, retrieval in knowledge assistants, clustering and duplicate detection.
3,072 by default. The API lets you request fewer dimensions to save storage.
Test both on your own questions. The small model is cheaper and often good enough.