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Description
Introducing the Gemini 2.5 Computer Use model, an advanced agent built upon the visual reasoning strengths of Gemini 2.5 Pro, specifically crafted for direct interaction with user interfaces (UIs). This model is accessible through a newly developed computer-use tool within the Gemini API, which takes inputs such as the user's request, a screenshot of the UI context, and a log of recent actions. It adeptly generates function calls relevant to UI tasks, including clicking, typing, or selecting, while also having the capability to seek user confirmation for tasks deemed higher risk. Following each performed action, the model receives updated feedback in the form of a new screenshot and URL to facilitate a continuous process until the task is either completed or stopped. Primarily fine-tuned for web browser navigation, it also shows potential for mobile UI interactions, although it currently lacks the capability for desktop OS-level management. In various benchmarks comparing web and mobile control tasks, the Gemini 2.5 Computer Use model demonstrates superior performance over leading competitors, achieving remarkable accuracy with reduced latency, and paving the way for future enhancements in interface interaction.
Description
Gemini Embedding models, which include the advanced Gemini Embedding 2, are integral to Google's Gemini AI framework and are specifically created to translate text, phrases, sentences, and code into numerical vector forms that encapsulate their semantic significance. In contrast to generative models that create new content, these embedding models convert input into dense vectors that mathematically represent meaning, facilitating the comparison and analysis of information based on conceptual relationships instead of precise wording. This functionality allows for various applications, including semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation processes. Additionally, the model accommodates input in over 100 languages and can handle requests of up to 2048 tokens, enabling it to effectively embed longer texts or code while preserving a deep contextual understanding. Ultimately, the versatility and capability of the Gemini Embedding models play a crucial role in enhancing the efficacy of AI-driven tasks across diverse fields.
API Access
Has API
API Access
Has API
Integrations
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Gemini 2.5 Pro
Gemini 3 Deep Think
Python
Integrations
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Gemini 2.5 Pro
Gemini 3 Deep Think
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
blog.google/technology/google-deepmind/gemini-computer-use-model/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/