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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.

What is E5 Text Embeddings?

Microsoft has introduced E5 Text Embeddings, which are advanced models that convert textual content into insightful vector representations, enhancing capabilities such as semantic search and information retrieval. These models leverage weakly-supervised contrastive learning techniques and are trained on a massive dataset consisting of over one billion text pairs, enabling them to effectively understand intricate semantic relationships across multiple languages. The E5 model family includes various sizes—small, base, and large—to provide a balance between computational efficiency and the quality of the generated embeddings. Additionally, multilingual versions of these models have been carefully adjusted to support a wide variety of languages, making them ideal for use in diverse international contexts. Comprehensive evaluations show that E5 models rival the performance of leading state-of-the-art models that specialize solely in English, regardless of their size. This underscores not only the high performance of the E5 models but also their potential to democratize access to cutting-edge text embedding technologies across the globe. As a result, organizations worldwide can leverage these models to enhance their applications and improve user experiences.

Media

Media

No images available

Integrations Supported

Gemini
Google AI Studio
Python
Vertex AI

Integrations Supported

Gemini
Google AI Studio
Python
Vertex AI

API Availability

Has API

API Availability

Has API

Pricing Information

$0.15 per 1M input tokens
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

github.com/microsoft/unilm/tree/master/e5

Categories and Features

Categories and Features

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