Gemini 4 Argon: Built to Solve Your Hardest Problems

By the ChatGBot Team · Published September 30, 2026

Google DeepMind has unveiled Gemini 4 Argon, its new top-tier model built for the hardest, longest problems rather than quick chat replies. It is aimed squarely at deep, multi-step work: real software engineering, cybersecurity defense, and enterprise research in law and finance. Here is what it does and why it matters.

What is Gemini 4 Argon?

Argon is the first and, so far, only model in Google's Gemini 4 family, announced on September 30, 2026. It is positioned as a frontier model for long, complex, multi-step tasks, the kind of work that takes a human expert hours, rather than one-line answers. Google says it leads on software engineering, enterprise knowledge work, and defensive cybersecurity.

A 1 million token output limit

The headline capability is room to work. Argon's output limit jumped to 1 million tokens, up from 64,000. That means it can reason and produce very long results, such as a large codebase change or a full report, in a single run without being cut off partway through.

State-of-the-art coding

On DeepSWE v1.1, a benchmark for real-world, long-horizon software engineering, Google reports a new state-of-the-art score of 77.9%. It also claims first place on Zapier's AutomationBench at 51.3% and 91.7% on LVBench for long-video understanding. These are strong numbers for agent-style work that chains many steps together, the heart of modern AI agents.

Built for cybersecurity defense

Argon is designed to autonomously find, validate and patch critical software vulnerabilities. Google reports it ties for first place at 68% on CWE-bench v1, a vulnerability-remediation test. That security focus is also why access is limited at first, as we explain below.

How it compares

Google pitches Argon directly against the other frontier models of this cycle, GPT-6 Astra and Claude Opus 5.5, and some reporting says it wins a majority of head-to-head tests. As always with vendor benchmarks, independent results will tell the fuller story, and a few observers have questioned the scores, which Google has publicly denied. For the broader lineup, see our September 2026 AI model releases roundup.

Pricing and access

Introductory API pricing is $2 per million input tokens and $10 per million output tokens, with cached input tokens discounted by 95%. Access is restricted at launch: only trusted cyber defenders in Google's Fairwind Program can use it first, with paid API customers and Google AI Ultra subscribers next in line. Google has not given a date for wider availability, and thousands of its own employees are already using it internally.

One quick note

Gemini is Google's own product and is not part of ChatGBot. ChatGBot focuses on the chat side of AI and bundles GPT, Claude, DeepSeek, Qwen and GLM under one subscription. For how Google's chat models compare, see Gemini vs ChatGPT.

Frequently asked questions

What is Gemini 4 Argon? Google DeepMind's new top-tier Gemini 4 model, announced September 30, 2026, built for long, complex, multi-step tasks in software engineering, cybersecurity and enterprise research.

What is special about it? A 1 million token output limit, a state-of-the-art 77.9% on the DeepSWE v1.1 coding benchmark, and the ability to autonomously find, validate and patch software vulnerabilities.

How much does Gemini 4 Argon cost and can I use it? Introductory API pricing is $2 per million input tokens and $10 per million output tokens. Access is limited at first to trusted cyber defenders, then paid API and Google AI Ultra users, with no public date yet.

Is Gemini part of ChatGBot? No. Gemini is a Google product. ChatGBot gives you GPT, Claude, DeepSeek, Qwen and GLM in one subscription so you can switch between those chat models in one place.

Still deciding which model to use? Our guide to the best AI in 2026 breaks it down by task.

Compare the top chat models in one app

For the conversation side of AI, ChatGBot puts GPT, Claude, DeepSeek, Qwen and GLM under one subscription, so you can test answers side by side and switch models whenever the task changes.