Gemini Embedding 2 is Google's first multimodal embedding model. It currently supports mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity, you can embed a text query and retrieve the most relevant images, or vice versa, making it well-suited for multimodal search, recommendation, and document understanding pipelines.
$0.10
Share of requests served without a server error. Client errors (4xx) do not count.
Works with the OpenAI SDK pointed at our base URL. Every parameter is in the .
Tokens embedded per day on NagaAI.
Gemini Embedding 2 is Google's first multimodal embedding model. It currently supports mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG).
On NagaAI, Gemini Embedding 2 costs $0.10 per 1M tokens. You pay only for what you use, with no subscription.
It takes text, images, audio and files as input and returns embeddings.
Create an API key, copy the model ID from the top of this page and send requests with the OpenAI SDK pointed at NagaAI. The API section on this page has a ready example.
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