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What is Gemini Embedding 2?

The Gemini Embedding models, particularly the sophisticated Gemini Embedding 2, are a vital component of Google's Gemini AI framework, designed to convert text, phrases, sentences, and code into numerical vectors that capture their semantic essence. Unlike generative models that produce new content, these embedding models transform inputs into dense vectors that represent meaning mathematically, allowing for the analysis and comparison of information through conceptual relationships rather than just specific wording. This unique capability enables a wide range of applications, such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation processes. Furthermore, the model supports over 100 languages and can process inputs of up to 2048 tokens, which allows it to efficiently embed longer texts or code while maintaining a strong contextual understanding. As a result, the Gemini Embedding models significantly contribute to the effectiveness of AI-driven tasks in various industries, making them indispensable tools for modern applications. Their adaptability and robust performance highlight the importance of advanced embedding techniques in the evolving landscape of artificial intelligence.

What is Gemini 2.5 Computer Use?

Introducing the Gemini 2.5 Computer Use model, an innovative agent designed to leverage the visual reasoning capabilities of Gemini 2.5 Pro, specifically created for seamless engagement with user interfaces (UIs). This model can be accessed via a newly created computer-use tool within the Gemini API, which accepts inputs such as user requests, screenshots of the UI environment, and logs of recent user actions. It skillfully generates relevant function calls for UI tasks, including actions like clicking, typing, or selecting, while also having the ability to request user confirmation for tasks that carry a higher risk. After each action is executed, the model receives updated feedback through a new screenshot and URL, ensuring a continuous workflow until the task is fully completed or halted. While it is primarily optimized for navigating web browsers, the model also shows promise for mobile UI engagements, although it does not yet support management at the desktop operating system level. In various assessments of web and mobile control tasks, the Gemini 2.5 Computer Use model outperforms leading competitors, achieving exceptional accuracy with minimized latency, thus setting the stage for future advancements in user interface interactions. As technology evolves, the potential applications of this model could expand significantly, making it a vital tool in the realm of digital interaction.

Media

Media

Integrations Supported

Gemini
Gemini Enterprise
Google AI Studio
Vertex AI
Gemini 2.5 Pro
Gemini 3 Deep Think
Python

Integrations Supported

Gemini
Gemini Enterprise
Google AI Studio
Vertex AI
Gemini 2.5 Pro
Gemini 3 Deep Think
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
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

blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

blog.google/technology/google-deepmind/gemini-computer-use-model/

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