What is Gemini Embedding?
The first text model of the Gemini Embedding, referred to as gemini-embedding-001, has officially launched and is accessible through both the Gemini API and Vertex AI, having consistently held its top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its initial trial in March, thanks to its exceptional performance in retrieval, classification, and multiple embedding tasks, outperforming both legacy Google models and those from other external developers. Notably, this versatile model supports over 100 languages and features a maximum input limit of 2,048 tokens, employing the cutting-edge Matryoshka Representation Learning (MRL) technique, which enables developers to choose from output dimensions of 3072, 1536, or 768 for optimal quality, efficiency, and performance. Users can easily access this model through the well-known embed_content endpoint in the Gemini API, and while older experimental versions are scheduled to be retired by 2025, there is no need for developers to re-embed previously stored assets when switching to the new model. This transition process is designed for a smooth user experience, minimizing any impact on existing workflows and ensuring continuity in operations. The launch of this model represents a significant step forward in the field of text embeddings, paving the way for even more advancements in multilingual applications.
Pricing
Price Starts At:
$0.15 per 1M input tokens
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Screenshots and Video
Company Facts
Company Name:
Google
Date Founded:
1998
Company Location:
United States
Company Website:
developers.googleblog.com/en/gemini-embedding-available-gemini-api/
Product Details
Deployment
SaaS
Training Options
Documentation Hub
On-Site Training
Video Library
Support
Standard Support
Web-Based Support
Product Details
Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English
