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features
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support

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Description

An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.

Description

Gemini 4 Argon is a frontier AI model from Google DeepMind built to sustain deep reasoning across complex, long-running professional workflows. Google designed the model for demanding work spanning software engineering, finance, legal tasks, enterprise knowledge work, cybersecurity defense, and creative writing. Argon supports coding, reasoning, multimodality, and multi-step task execution, allowing it to work across workflows that require information gathering, analysis, tool use, and extended problem solving. Its output token limit has been increased from 64,000 to 1 million tokens, giving the model additional capacity for lengthy reasoning and generation within a single trajectory. On DeepSWE v1.1, Google reports a score of 77.9% for real-world long-horizon software engineering, while its AutomationBench score of 51.3% measures performance on end-to-end business workflows. Google also reports strong results on evaluations covering finance, legal work, visual analysis, and long-video understanding, including a 91.7% score on LVBench. For cybersecurity teams, Argon can autonomously discover, validate, and patch software vulnerabilities and achieved a reported 68% score on CWE-bench v1. Google is initially providing the model to selected cyber defenders through its Fairwind Program while strengthening safeguards before expanding access to developers, enterprises, and consumers. Argon is planned to launch at an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input tokens receiving a 95% discount from the standard input price.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Agent Search on Gemini Enterprise Agent Platform No 
Android Studio No 
C No 
C# No 
Devin Desktop No 
Gemini 3.8 Live No 
Gemini Enterprise Agent Platform No 
Gemini Managed Agents No 
Go No 
Google AI Ultra No 
Google Antigravity No 
HTML No 
JetBrains Junie No 
Kubernetes No 
Lua No 
R No 
Replit No 
Solidity No 
Swift No 
TypeScript No 

Integrations

Agent Search on Gemini Enterprise Agent Platform Yes 
Android Studio Yes 
C Yes 
C# Yes 
Devin Desktop Yes 
Gemini 3.8 Live Yes 
Gemini Enterprise Agent Platform Yes 
Gemini Managed Agents Yes 
Go Yes 
Google AI Ultra Yes 
Google Antigravity Yes 
HTML Yes 
JetBrains Junie Yes 
Kubernetes Yes 
Lua Yes 
R Yes 
Replit Yes 
Solidity Yes 
Swift Yes 
TypeScript Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Pricing Details

$2 per 1M tokens (input)
$2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Apple

Founded

1976

Country

United States

Website

github.com/apple/ml-ferret

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

gemini.com

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