AI model by Google
Gemini Embedding 2 for business
Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
- Reviewed on September 24, 2026
Capability tiersRelative, not benchmarks
Key facts about Gemini Embedding 2
- Model ID at review
- gemini-embedding-2
- Provider
- 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
-
Search across text and images together.
-
Embedding PDFs, audio and video.
-
Flexible vector sizes from 128 to 3,072 dimensions.
-
Retrieval for multimodal knowledge assistants.
-
Grouping similar media and documents.
Not the right choice for
-
Inputs longer than 8,192 tokens per item.
-
Generating answers.
-
Teams that want to self-host embeddings.
-
Projects that cannot re-embed if they switch later.
Use cases
Business use cases we would use it for
Visual search
PDF retrieval
Media libraries
Our notes
When we would choose it
Gemini Embedding 2 is Google's current embedding model. Unlike most embedding models, it is multimodal: it accepts text, images, video, audio and PDFs and places them in the same vector space. Inputs can be up to 8,192 tokens, and you can choose vector sizes from 128 to 3,072 dimensions.
That opens up search that crosses media types. A user can type a description and find a matching photo, a slide in a PDF or a moment in a recorded session. For businesses with large image or media libraries, this can remove a separate captioning step.
The usual embedding rules still apply. Changing models later means re-embedding your whole collection, so test before you commit. Measure retrieval on real queries, check storage costs at your chosen dimension and consider adding a rerank step such as Cohere Rerank.
If you use the older gemini-embedding-001, Google lists its shutdown for May 14, 2028, so there is time to plan. For text-only search, compare with OpenAI embeddings and Voyage. See RAG knowledge assistants.
A simple way to start is to take one collection, such as product photos with their descriptions, embed both and check whether a text search returns the right photos. If it does, the same index can later serve a knowledge assistant, a similar-items feature and duplicate detection. One embedding model can power several features, which is part of why the choice deserves a careful test at the start rather than a quick pick.
Before you commit
Things to check before you commit
-
Input size
The input limit is 8,192 tokens per item. Plan chunking.
-
Vector size
Pick a dimension that balances quality and storage.
-
Older models
gemini-embedding-001 shuts down in May 2028 and text-embedding-004 is already shut down.
-
Retrieval test
Test with real queries across your media types.
Alternatives
Models to compare it with
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.
text-embedding-3-large
OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
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.
- Computer vision Software that reads photos and scans: damage checks, stock counts, document capture and quality control.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- Document automation Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.
- 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.
- Receipt capture and matching Snap a receipt, and the details are read, categorized and matched to the card transaction and the right project.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- Invoice processing Read supplier invoices, match them to orders and push approved ones into accounting, with exceptions flagged for review.
- AI document processing Read forms, applications, IDs and statements, extract the fields you need and route each document for the right review.
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.
- Insurance Claims intake, document and photo processing, policy knowledge assistants and customer portals for brokers and insurers.
- Manufacturing Quality inspection, production dashboards, quoting tools, knowledge assistants and legacy system modernization for manufacturers.
- Construction and facilities Job tracking, maintenance requests, inspections, quotes and proof-of-work photos for builders and facility 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 articlesMCP servers
View all mcp servers- 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.
- Readwise Readwise's official hosted MCP server lets AI search and manage your reading highlights and Reader documents.
- Knowledge and memory Servers that give AI long-term memory or access to knowledge bases, wikis and personal notes.
- Box Box's official MCP server lets AI search and read files, ask questions across documents with Box AI, extract data and upload files.
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.
- Multimodal model A multimodal model is an AI model that can take in more than one kind of input, such as text with images, audio or video.
- 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".
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- 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.
Yes. It accepts text, images, video, audio and PDFs.
From 128 to 3,072 dimensions, according to the Gemini embeddings documentation.
Yes, with a listed shutdown date of May 14, 2028.