Google Rolls Out Gemini Embedding Model for RAG and Multilingual NLP

The Gemini Embedding model officially exited experimental status on July 14. It replaces older models in Google's Vertex AI and Gemini APIs, introducing a 3,072-dimensional embedding format, support for over 100 languages, and a 2,048-token input limit. Google says the model now leads the Massive Text Embedding Benchmark (MTEB) for multilingual performance, surpassing both proprietary and open-source alternatives.

For developers building RAG systems, the improvements are concrete. Higher dimensionality offers better semantic resolution. Multilingual support reduces pipeline complexity for global applications. And with a pay-as-you-go pricing model of $0.15 per million tokens—plus a generous free tier—the model is accessible for prototyping and production use alike. Google notes that users of legacy text-embedding-gecko models will need to migrate manually, as it won’t happen automatically.

The release reflects Google's broader push to modernize its foundation model APIs, challenging incumbents like OpenAI’s text-embedding-3-small and emerging contenders like Cohere and Mistral. While the field remains competitive, Gemini Embedding’s strong benchmark performance and native integration with Vertex AI give it a foothold among enterprise users and AI teams prioritizing multilingual reach and RAG efficiency.

Uses cases are: Retrieval-Augmented Generation (RAG), Information retrieval, Search reranking, Anomaly detection, Classification, Clustering

Text embeddings are crucial for a variety of common AI use cases, such as:

• Retrieval-Augmented Generation (RAG): Embeddings enhance the quality of generated text by retrieving and incorporating relevant information into the context of a model.
• Information retrieval: Search for the most semantically similar text or documents given a piece of input text.
• Search reranking: Prioritize the most relevant items by semantically scoring initial results against the query.
• Anomaly detection: Comparing groups of embeddings can help identify hidden trends or outliers.
• Classification: Automatically categorize text based on its content, such as sentiment analysis or spam detection
• Clustering: Effectively grasp complex relationships by creating clusters and visualizations of your embeddings.