Gemini Embedding 2
Natively multimodal embeddings for search, retrieval, and agent-ready knowledge systems
Gemini Embedding 2 is the advanced choice for search engineers and AI platform teams who need to build one multimodal retrieval layer across text, images, audio, video, and documents. It stands out by collapsing several embedding pipelines into one managed model. The tradeoff is migration overhead and a still-preview product posture.
Why we love it
- Unifies text, image, audio, video, and PDF embeddings in one model
- Cuts orchestration work in multimodal RAG and search pipelines
- 8192-token text support helps longer retrieval chunks
- 3072-dimensional vectors fit high-recall enterprise search use cases
- Managed access through Gemini API and Vertex AI speeds deployment
- Strong fit for agent memory and cross-media retrieval workflows
Things to know
- Preview status may deter strict production governance teams
- Older Google embedding indexes require re-embedding work
- Media-heavy workloads can get costly beyond text-only search
- Less attractive for simple text-only pipelines on tight budgets
About
Executive Summary: Gemini Embedding 2 is Google's natively multimodal embedding model for teams building search, RAG, analytics, and cross-media retrieval systems. It is best for developers who need one embedding space for text, images, audio, video, and documents instead of stitching together multiple models and pipelines.
What it is
Large Language Models usually generate text, but Gemini Embedding 2 solves a different systems problem: turning content into vectors that power semantic search, recommendation, clustering, and retrieval. The big shift is that Google now offers one native embedding model across text, images, audio, video, and PDFs, so modern AI stacks can unify indexing instead of managing separate encoders.
Why it matters for automation
This model reduces orchestration overhead in production AI systems. Instead of chaining a text embedding model, an image encoder, an audio pipeline, and separate document preprocessing logic, teams can standardize on one API through Vertex AI or the Gemini API and simplify retrieval infrastructure for multimodal agents.
Technical specifics
Google says Gemini Embedding 2 supports up to 8192 input tokens for text, up to 6 images per request, up to 120 seconds of video, and PDFs up to 6 pages. On Vertex AI, it generates 3072-dimensional vectors in a unified semantic space, which makes text-to-image and cross-media retrieval practical without building separate embedding stores.
Pricing and value
Gemini Embedding 2 offers a Freemium plan, with paid tiers starting at $0.20 per 1M text tokens. It is less expensive than average for this category if you need one multimodal embedding layer instead of combining separate text, image, video, and audio models. Vertex AI pricing also lists $0.00012 per image, $0.00079 per video frame, and $0.00016 per audio second, so cost control depends on media mix more than pure text volume.
Best fit
Gemini Embedding 2 is strongest for enterprise search, multimodal RAG, e-commerce discovery, media archives, and agent memory systems that must retrieve across formats. Its biggest limitation is compatibility: teams upgrading from older Google embedding stacks should expect re-indexing work rather than a drop-in swap.
Key Features
- ✓Embed text, images, audio, video, and PDFs in one unified semantic space
- ✓Reduce pipeline complexity by replacing multiple modality-specific encoders
- ✓Process up to 8192 text tokens for longer retrieval chunks
- ✓Handle up to 6 images per request for multimodal search workflows
- ✓Index up to 120 seconds of video for cross-media retrieval
- ✓Embed audio natively without forcing speech-to-text preprocessing
- ✓Generate 3072-dimensional vectors for high-recall similarity search
- ✓Deploy through Gemini API or Vertex AI for managed production access
- ✓Support multimodal RAG, recommendation, clustering, and analytics systems
- ✓Simplify enterprise search stacks that span documents, media, and structured content
Product Comparison
| Dimension | Gemini Embedding 2 | OpenAI text-embedding-3-small | Cohere Embed 4 |
|---|---|---|---|
| Core use case | Multimodal retrieval across text, image, audio, video, and PDF in one vector space | Low-cost text embeddings for classic RAG, search, and classification pipelines | Enterprise semantic retrieval with strong text search and production NLP positioning |
| Differentiated killer feature | Native multimodal embedding without stitching together separate encoders | Very low text-only cost for teams that do not need media retrieval | Enterprise search focus with strong relevance tooling and business adoption |
| Performance and limits | 8192 text tokens, 6 images/request, 120s video, 3072-dim vectors | Text-first workflow, cheaper but not built as one unified multimodal space | Strong enterprise retrieval, but less compelling than Gemini for unified media search |
| Integration and learning curve | Best with Gemini API and Vertex AI; easiest inside Google Cloud AI stacks | Best with OpenAI-based stacks and simple vector search pipelines | Best for teams already standardizing on Cohere and enterprise NLP workflows |
| ROI for AI systems | Highest ROI when one model replaces separate text, image, audio, and video pipelines | Highest ROI for budget-conscious text-only search and RAG deployments | High ROI for enterprises that prioritize retrieval quality and vendor support |
| Main limitation | Re-embedding required for older Google indexes; preview status adds caution | Not ideal for cross-media retrieval because modality coverage is narrower | Less differentiated if your workload needs native video and audio embeddings |
Frequently Asked Questions
The core difference is modality coverage. While OpenAI text-embedding-3-small is cheaper for text-only pipelines, Gemini Embedding 2 has the absolute advantage for multimodal RAG because it embeds text, images, audio, video, and PDFs in one space with 3072-dimensional vectors and 8192-token text input.
The biggest concerns are preview maturity and migration cost. Teams report that older Gemini embedding indexes are not compatible, so moving to Gemini Embedding 2 means re-embedding datasets, and text-only teams may question whether multimodal capability justifies higher cost than cheaper text embedding models.
Yes. Prices start at free testing access, then paid usage starts at $0.20 per 1M text tokens on Vertex AI. Image input is $0.00012 per image, video is $0.00079 per frame, and audio is $0.00016 per second, so media-heavy retrieval can cost more than text-only indexing.
It fits as the embedding layer in Gemini API or Vertex AI-based pipelines. You generate vectors, store them in a vector database such as Qdrant or Pinecone, and use them for multimodal search, agent memory, recommendation, and RAG across text, images, audio, video, and PDFs.
Yes for many enterprise cases, especially through Vertex AI. The safer pattern is to run it inside Google Cloud governance, keep documents in controlled storage, and separate embedding generation from downstream retrieval policy so private corpora are not mixed with public data pipelines.
Yes. That is one of its strongest use cases because it maps text, images, audio, video, and documents into one semantic space. Google says it supports up to 6 images per request, 120 seconds of video, and direct PDF embedding, which makes cross-media retrieval far easier to implement.