embedding
Gemini Embedding 2
First multimodal embedding model in the Gemini API, mapping text, images, video, audio and documents into one embedding space for cross-modal search.
Levelvariant
Classembedding
Parametersnot in the notes
Contextnot in the notes
Releasednot in the notes
Opennessproprietary
Runs onnot in the notes
Statusactive
R&D Topics
Identity
- Developer
- Family
- Gemini Embedding
- Released
- – sources
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- not in the notes
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- text, image, video, audio
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- proprietary
- Licence
- Proprietary sources
- Access routes
- API
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperGooglebig tech, USA
Googlebig tech, USAModelGemini Embedding 2Google, embedding
Gemini Embedding 2Google, embeddingR&D TopicsRetrieval-Augmented Generation (RAG)Text Embeddings for Semantic SearchVisual Document Retrieval
Use CasesEmbeddings and Retrieval for SEA LanguagesPublic SectorUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs Itnone recordedDeployments of these use cases; none names this model.
Organisations 1
R&D topics 3
Retrieval-Augmented Generation (RAG)Grounding generation in retrieved documents.used byText Embeddings for Semantic SearchTraining encoders that turn text in many languages into vectors so that search, clusterin…produced byVisual Document RetrievalRetrieving document pages by embedding the page images themselves with a vision language…produced by
Sources
- ai.google.dev/gemini-api/docs/embeddings https://ai.google.dev/gemini-api/docs/embeddings
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Gemini Embedding 2", https://aurion.semai.id/models/google-gemini-embedding-2/, records of October 2026.