Gemini 4 Argon 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.

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Pricing

Pricing Starts At:
$2 per 1M tokens (input)
Pricing Information:
$2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.

Integrations

API:
Yes, Gemini 4 Argon has an API
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Reviews - 1 Verified Review

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Company Details

Company:
Google
Year Founded:
1998
Headquarters:
United States
Website:
gemini.com

Media

Gemini 4 Argon Screenshot 1
Gemini 4 Argon Screenshot 1 Gemini 4 Argon Screenshot 2 Gemini 4 Argon Screenshot 3
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support

Gemini 4 Argon Features and Options

Gemini 4 Argon User Reviews

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  • Name: Anonymous (Verified)
    Job Title: Developer
    Length of product use: Less than 6 months
    Used How Often?: Daily
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Google's finally at the frontier of AI

    Date: Oct 13 2026

    Summary: Gemini 4 Argon is one of the strongest models I have used for serious coding, research, and long-running agent work. It feels especially good when the task is complicated enough that weaker models start losing context or need constant correction.

    Positive: I can give it a large coding problem, a long research task, or something involving multiple tools and it does a much better job keeping the goal in view instead of slowly wandering off course. Coding is especially strong. Google reports 77.9% on DeepSWE v1.1 and 91.9% on Vibe Code Bench, and it feels very comfortable with repo-level work, multi-file changes, debugging, and longer agent loops. The 1M-token context window is also genuinely useful. I can keep a huge amount of code, documentation, logs, PDFs, and prior work in the same session without constantly trimming context. I also like the tool support. Computer use, code execution, search grounding, file search, function calling, and URL context make Argon feel much more like an agent model than a normal chatbot.

    Negative: The main downside is that it is overkill for simple tasks. I would still use Flash for quick coding help, lightweight automation, or anything where speed and cost matter more than maximum reasoning depth.

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