Gemini Embedding 2

Gemini Embedding 2 is a current AI model verified from first-party Google sources.

Gemini Embedding 2 verified specifications

Gemini Embedding 2 is Google’s first stable multimodal embedding model, mapping text, images, video, audio and PDFs into one vector space for search, RAG and recommendation systems. The verified context or usage limit is 8192 input tokens, with 128-3072 embedding dimensions maximum output.

Gemini Embedding 2 pricing and access

Paid standard pricing is $0.20/M text input tokens, $0.45/M image tokens, $6.50/M audio tokens and $12/M video tokens. A free tier is available.

Gemini Embedding 2 best uses

Multimodal RAG, Semantic search, Cross-modal retrieval, Recommendations, Classification, Clustering.

Gemini Embedding 2 limitations

Does not generate answers, Vector quality still requires retrieval evaluation, Large media collections can create indexing cost.

Gemini Embedding 2 capabilities and use cases

In addition, its main capabilities include Multimodal embeddings, 8192 input tokens, 128-3072 dimensions, 100+ languages and Cross-modal retrieval. For example, common use cases include RAG, Vector search, Recommendations, Classification, Clustering and Multimodal retrieval.

Who should consider Gemini Embedding 2?

In practice, this model may suit Multimodal RAG, Semantic search, Cross-modal retrieval, Recommendations, Classification and Clustering. Also, notable strengths include Stable current Google embedding model, Multimodal, Flexible dimensions and Free tier. However, review trade-offs such as Does not generate answers, Vector quality still requires retrieval evaluation and Large media collections can create indexing cost before adopting it.

Gemini Embedding 2 pricing and access

Meanwhile, Paid standard pricing is $0.20/M text input tokens, $0.45/M image tokens, $6.50/M audio tokens and $12/M video tokens. A free tier is available. Pricing depends on input modality; batch processing offers discounted rates for production indexing.

Official resources and verification

Use the official model website, official documentation, pricing or release source and additional primary source to confirm current availability, limits and pricing. Product details can change after publication, so rely on primary documentation for final decisions.

Compare with other AI models

Next, continue your research in the AI models directory, Google models and Embedding Model models. Compare providers, pricing, modalities and practical limitations side by side to choose the right model for your workflow.