Gemini Embedding 001
Awaiting activationConnect multilingual text with search, matching and information organization.
What it does
Gemini Embedding 001 is Google’s text embedding model for semantic retrieval, classification and clustering. It helps relate a question to relevant text even when the words differ, and supports multilingual collections.
Google documents task-specific behavior and flexible vector sizes. Its default output has 3,072 dimensions, with shorter representations available. embeddings.app uses its query and document modes for retrieval, so both sides of a search are represented for their intended role.
Where it helps
International knowledge
Find relevant text in a collection used by teams across languages.
Support search
Match a customer’s question to a helpful knowledge-base passage.
Related content
Identify articles or descriptions that cover similar subjects.
When to choose it
Consider it if you want Google’s text-specific embedding model and your content fits its shorter source window. Compare it with Gemini Embedding 2 when longer source passages are important.
This model remains awaiting activation here while the existing Google project’s quota issue is unresolved. This service accepts text and applies its own smaller byte limit.
Official sources
Paraphrased from the provider’s documentation and original model information. Selection advice is our editorial guidance.
Google · Gemini embeddings ↗Reviewed October 3, 2026