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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.

What is GPT-6.1 Sol?

GPT-6.1 Sol is OpenAI's upgraded Sol model for developers and professionals who need advanced reasoning and agentic capabilities without the higher cost of GPT-6 Astra. It is designed for coding, professional knowledge work, computer use, scientific research, factual question answering, and multi-step business workflows. OpenAI describes GPT-6.1 Sol as approaching GPT-6 Astra's intelligence across several important workloads while charging one-fifth of Astra's standard input and output token prices. On DeepSWE v1.1, which evaluates long-horizon software engineering in real codebases, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and exceeds GPT-6 Sol's best score by 6.4 percentage points. Its professional-work capabilities include understanding complex PDFs containing tables, charts, diagrams, and fine-print details across fields such as finance, healthcare, and legal work. On AutomationBench, GPT-6.1 Sol improves on GPT-6 Sol by 4.8 percentage points at the same reasoning setting and scores 2.2 points above Opus 5.5 at medium reasoning effort. Computer-use performance also advances significantly, with GPT-6.1 Sol outperforming GPT-6 Sol by seven percentage points on the OSWorld 2.0 offline set at maximum reasoning effort and coming within 2.1 points of GPT-6 Astra. For scientific research, the model can work with code and terminal tools on workflows involving data analysis, simulations, model fitting, and theorem proving, more than doubling GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort. OpenAI also reports improved factual accuracy, including a reduction in the factual-error rate from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol at low reasoning effort on its deliberately difficult factuality evaluation.

Media

Media

Integrations Supported

.NET
C++
CSS
Cursor
Devin Desktop
Gemini Enterprise Agent Platform
Go
Java
JavaScript
JetBrains Junie
Kotlin
Kubernetes
Objective-C
PHP
Python
Ruby
Scala
Swift
Vercel AI Gateway
YAML

Integrations Supported

.NET
C++
CSS
Cursor
Devin Desktop
Gemini Enterprise Agent Platform
Go
Java
JavaScript
JetBrains Junie
Kotlin
Kubernetes
Objective-C
PHP
Python
Ruby
Scala
Swift
Vercel AI Gateway
YAML

API Availability

Has API

API Availability

Has API

Pricing Information

$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.

Pricing Information

$2 per 1M tokens (input)
Input: $2 per 1 million tokens
Output: $10 per 1 million tokens
Cached Input: $0.10 per 1 million cached input tokens

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

gemini.com

Company Facts

Organization Name

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

AI Vision Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

AI Vision Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

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