Gemini Embedding 2: Google Unifies Text, Images, Video, and Audio Into One AI Model

Essential Points

• Gemini Embedding 2, launched March 10, 2026, maps text, images, video, audio, and PDFs into a single unified embedding space
• The model supports over 100 languages and processes up to 8,192 input tokens for text-based tasks
• Flexible output dimensions scale from 3,072 down to 768 using Matryoshka Representation Learning, with MTEB scores between 67.99 and 68.17 across those tiers
• Available now in public preview as gemini-embedding-2-preview via the Gemini API and Vertex AI, with integrations for LangChain, LlamaIndex, Haystack, Weaviate, QDrant, ChromaDB, Pinecone, and Vector Search

Google just closed the gap between how humans perceive the world and how AI understands it. Gemini Embedding 2, released on March 10, 2026, is the first fully multimodal embedding model built on the Gemini architecture, and it processes five different media types inside a single, unified semantic space. This article breaks down exactly what that means, who benefits, and why it matters for developers, enterprises, and AI builders in 2026.

What Gemini Embedding 2 Actually Does

An embedding model converts raw content into numerical vectors that capture semantic meaning. When two pieces of content share similar meaning, their vectors sit close together in mathematical space, regardless of the specific words or pixels used.

Previous Google embedding models handled text exclusively. Gemini Embedding 2 breaks that single-modality constraint entirely. It places text, images, video, audio, and PDFs into the same dimensional space, which means a search query written in English can retrieve a relevant video clip, an image, or a spoken audio segment through one model.

This matters most for real-world data, which is almost never text-only.

Also Read Claude Marketplace: Anthropic’s Enterprise AI Platform Now Reshapes How Teams Work

Five Modalities, One Embedding Space

Gemini Embedding 2 handles each input type with specific, confirmed parameters from Google’s API documentation.

• Text: Up to 8,192 input tokens per request, covering long documents, code, and multilingual content across 100+ languages
• Images: Maximum of 6 images per request in PNG or JPEG format
• Video: Maximum of 128 seconds per request in MP4 or MOV format, supporting H264, H265, AV1, and VP9 codecs
• Audio: Maximum of 80 seconds per request in MP3 or WAV format, natively ingested without intermediate text transcription
• Documents (PDF): Maximum of 6 pages per request; the model processes both visual and text content of each page

The overall maximum input token limit across all modalities is 8,192 tokens per request. The model also natively understands interleaved input, meaning you can pass multiple modalities such as an image combined with a text query inside a single request to capture relationships between different media types.

Matryoshka Dimensions: Balancing Quality and Storage Cost

Gemini Embedding 2 uses Matryoshka Representation Learning (MRL), a technique that nests information so that truncating a 3,072-dimension vector to a smaller size still yields an accurate, usable representation. By default, the model outputs 3,072-dimensional embeddings, which are normalized for accurate cosine similarity.

For dimensions other than 3,072, including 768 and 1,536, Google’s documentation specifies that developers should normalize the embeddings manually to maintain semantic accuracy. The recommended output sizes are 3,072, 1,536, and 768 dimensions.

MTEB benchmark scores across MRL dimension tiers confirm that smaller dimensions retain strong performance:

The data shows a notable finding: 1,536 dimensions actually scores marginally higher than 2,048 dimensions. For most production deployments, 768 dimensions delivers near-peak quality at roughly one-quarter the storage footprint of 3,072 dimensions.

Gemini Embedding 2 vs gemini-embedding-001

gemini-embedding-001 remains available for text-only use cases. Gemini Embedding 2 expands on that text-only foundation with multimodal support but introduces one important constraint: the embedding spaces between the two models are incompatible.

Teams upgrading from gemini-embedding-001 to gemini-embedding-2-preview must re-embed all existing data before switching. Direct comparison of embeddings generated by one model with embeddings generated by the other will produce inaccurate results.

Real-World Use Cases for Developers and Enterprises

Google’s official documentation identifies the following as primary use cases for Gemini Embedding 2:

Retrieval-Augmented Generation (RAG): Embeddings enhance the quality of generated text by retrieving and incorporating relevant information into model context. With multimodal support, a RAG pipeline can now retrieve images, audio, or video alongside text using a single unified index.

Semantic search and information retrieval: Cross-modal search allows a text query to surface relevant video, image, or audio results from the same vector index, eliminating the need for separate retrieval systems per media type.

Classification and clustering: All modalities map to the same space, making cross-modal sentiment analysis, anomaly detection, and data organization viable with one model rather than a stack of specialized models.

Document intelligence: PDFs are embedded directly. The model processes both visual layout and text content on each page, preserving information that text-extraction pipelines often lose.

Everlaw, an early access partner, confirmed measurable improvements in precision and recall across millions of records in legal discovery workflows, adding image and video search capabilities on top of existing text search.

Access, Integration, and Batch Pricing

Gemini Embedding 2 is available now in public preview under the model ID gemini-embedding-2-preview through two primary routes.

1. Gemini API: Accessible via Google AI for Developers, with interactive Colab notebooks for quick onboarding
2. Vertex AI: Enterprise-grade access with Google Cloud’s security, scaling, and compliance infrastructure

Framework and vector database integrations confirmed at launch include LangChain, LlamaIndex, Haystack, Weaviate, QDrant, ChromaDB, Pinecone, and Google’s own Vector Search. Google Cloud managed storage options compatible with the model include BigQuery, AlloyDB, and Cloud SQL.

For high-throughput, latency-tolerant workloads, Google’s Batch API supports Gemini Embedding models at 50% of the standard embedding price.

Limitations and Considerations

Gemini Embedding 2 is in public preview, meaning API capacity may be limited and the model specification can change before general availability. Audio input is capped at 80 seconds per request and limited to MP3 and WAV formats, excluding formats like AAC or FLAC. Video input is limited to 128 seconds, requiring chunking for longer content. PDF embedding is capped at 6 pages per request, which limits direct ingestion of long-form documents such as contracts or research papers. Teams migrating from gemini-embedding-001 face a mandatory full re-embedding of existing vector stores due to incompatible embedding spaces.

Also Read GitHub Security Lab’s AI Framework Found 80+ Real Vulnerabilities in Open Source Code

Frequently Asked Questions (FAQs)