# Gemini Embeddings 2: text, image, video, audio in one vector space

> Google's Gemini Embeddings 2 natively maps text, images, video, audio, and documents into one vector space, removing per-modality pipelines and conversion loss.

Published: 2026-04-17
URL: https://daniliants.com/insights/google-s-new-embeddings-2-model-is-really-impressive/
Tags: embeddings, multimodal, rag, vector-search, gemini, google-ai, retrieval

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## Summary

Google released Gemini Embeddings 2, the first model that natively maps text, images, video, audio, and documents into a single vector space without transcription or conversion. This eliminates the need for separate pipelines per modality and reduces information loss, latency, and retrieval errors compared to existing multimodal approaches.

## Key Insight

- **Native multimodal understanding, not conversion:** Unlike other "multimodal" embedding models that secretly transcribe audio to text or describe video frames, Gemini Embeddings 2 processes each modality directly. This preserves tone, visual detail, and context that gets lost in conversion.
- **Benchmark leader:** Outperforms Amazon Nova, Voyage multimodal models, and Google's own previous embedding models across all data types.
- **Single vector space:** Text, images, video, audio, and documents all map to the same embedding space. A text query can retrieve a relevant video clip or audio segment directly, no stitching of five different retrieval pipelines.
- **Practical impact for RAG:** Teams with heterogeneous knowledge bases (meeting recordings, product images, support call audio, documents) can build a single retrieval pipeline instead of maintaining separate transcription, image description, and embedding workflows.
- **Available now:** Public preview via the Gemini API.