What is Gemini 4 Argon?

Gemini 4 Argon is Google's frontier AI model for advanced reasoning and long-horizon workflows across software engineering, enterprise knowledge work, cybersecurity defense, and creative tasks. The model combines coding, multimodal understanding, reasoning, and multi-step execution to address complex professional workloads that may require sustained work across many steps. Google expanded Argon's maximum output from 64,000 tokens to 1 million tokens, allowing it to reason and generate hundreds of thousands of tokens within a single trajectory when required. Google engineers are already using Argon internally for tasks ranging from everyday debugging and algorithm design to large-scale migrations of C and C++ codebases to Rust. On DeepSWE v1.1, which evaluates real-world long-horizon software engineering, Google reports that Gemini 4 Argon achieves a score of 77.9%. The model also scored 51.3% on AutomationBench, a Zapier benchmark measuring end-to-end execution across business functions. Its knowledge-work capabilities include financial research, legal research and drafting, professional chart analysis, document-based workflows, and long-video understanding, with a reported 91.7% score on LVBench. Google has additionally trained Argon for defensive cybersecurity, enabling it to autonomously find, validate, and patch critical software vulnerabilities. Argon tied for first with a reported 68% score on CWE-bench v1 and has been evaluated on vulnerability discovery across complex codebases covering 20 programming languages. Google is using a phased release strategy that begins with trusted cyber defenders through the Fairwind Program while additional safeguards are tested before broader availability. The company plans to expand Gemini 4 Argon to developers, enterprises, and consumers, beginning with paid API customers and Google AI Ultra subscribers.

Pricing

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

Integrations

Offers API?:
Yes, Gemini 4 Argon provides an API
All Gemini 4 Argon Integrations
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Screenshots and Video

Gemini 4 Argon Screenshot 1
Gemini 4 Argon Screenshot 1 Gemini 4 Argon Screenshot 2 Gemini 4 Argon Screenshot 3

Company Facts

Company Name:
Google
Date Founded:
1998
Company Location:
United States
Company Website:
gemini.google.com

Product Details

Deployment
SaaS
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

Gemini 4 Argon Categories and Features

Multimodal Models

Large Language Models

Foundation Models

AI Vision Models

AI Reasoning Models

AI Models

AI Coding Models

Gemini 4 Argon Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: Developer
    Has used product for: Less than 6 months
    Uses the product: Daily
    Org Size (# of Employees): 100 - 499
    Feature Set 5 out of 5 stars
    Layout 5 out of 5 stars
    Ease Of Use 5 out of 5 stars
    Cost 5 out of 5 stars
    Customer Service 5 out of 5 stars
    Would you Recommend to Others? 10 out of 10

    Google's finally at the frontier of AI

    5 out of 5 stars 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.

    Read More...
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