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Google has announced Gemini 4 Argon, but it is not generally available. As of October 3, 2026, initial access is limited to trusted cyber defenders in the Fairwind Program. Google says developers, enterprises, and consumers will get access later, beginning with paid API customers and Google AI Ultra subscribers, but it has not given a date.

What is Gemini 4 Argon?

Gemini 4 Argon is a new frontier model that Google says is designed for complex software engineering, enterprise knowledge work, and cybersecurity defense. Google DeepMind SVP and Google Chief AI Architect Koray Kavukcuoglu announced it on September 30, 2026, saying it was “rolling out to a set of trusted cyber defenders through our Fairwind Program.”

Google says Argon can handle legal and financial knowledge work as well as software engineering and cybersecurity tasks. Its stated output limit is 1 million tokens, compared with 64,000 tokens in previous Gemini models. That is an output limit; Google’s announcement does not establish a 1-million-token context window.

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Who can use Gemini 4 Argon now?

Access is initially restricted to trusted cyber defenders participating in Google’s Fairwind Program. Google says it is also engaged in a U.S. government voluntary process for pre-release model access as it gradually expands availability and gathers feedback from early testers. The announcement does not provide a public signup route, waitlist, or date for broader access.

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Google’s earlier description of Fairwind says participating organizations are expected to limit use to internal cybersecurity, incident-response, or penetration-testing teams and deploy protections such as multifactor authentication. The program is described as serving Google Cloud customers, government agencies, and cybersecurity partners. See Google’s Fairwind program description.

Who is expected to get access next?

Google says it plans to expand access to developers, enterprises, and consumers, starting with paid API customers and Google AI Ultra subscribers. It has not said when that expansion will begin, so neither a subscription nor API payment should be treated as a guarantee of immediate Argon access.

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What does Google say Argon can do?

Google presents Argon as a model for long, complex work, including software development, enterprise tasks, and vulnerability discovery and remediation. It reports the following benchmark results:

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Evaluation Google’s reported result What Google says it measures
DeepSWE v1.1 77.9% Long-horizon software engineering
AutomationBench 51.3%; Google says Argon ranked first Automation tasks
LVBench 91.7%; Google describes it as state of the art Long-video understanding
CWE-bench v1 68%; Google says Argon tied for first Vulnerability remediation

These are results reported by Google, not independent demonstrations of performance on every organization’s work. InfoWorld’s comparison reports that Argon scores higher than Claude Opus 5.5 on Vals Index (68.9% versus 67.0%) and DeepSWE v1.1 (77.9% versus 74.2%), but lower on PostTrainBench for ML engineering (45.3% versus 49.3%). The comparison is reported by InfoWorld; benchmark results depend on the task and evaluation method and should not be treated as a universal ranking.

What is known about Argon’s cybersecurity use and safeguards?

Google says Argon can autonomously find, validate, and patch critical software vulnerabilities. It also says Wiz is using the model for cybersecurity defense through its Scan for Good initiative, and describes an early demonstration in which Argon uncovered a critical vulnerability in healthcare software. These are Google’s accounts of the model’s capabilities and early use, not independently audited findings.

Google says trusted defenders and its internal teams receive Argon without cyber guardrails. That access condition matters: the initial release is restricted to trusted users, not an unrestricted public cybersecurity tool.

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Google describes safeguards covering misuse reduction, defense against indirect prompt injection, monitoring for misalignment, and hardening sandboxed test environments. It says internal and external red teams tested the safeguards, but the announcement does not include a complete test report or an independent certification. Google characterizes the rollout as phased, stating: “Safely releasing frontier capabilities at this level requires a phased approach.”

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For related security work, Google’s Fairwind description also discusses CodeMender paired with Gemini 3.8 Flash Cyber for vulnerability discovery and fixes. It says Google Cloud customers can use CodeMender with publicly available models hosted on Gemini Enterprise Agent Platform alongside AI Threat Defense. Those services provide context for Google’s broader security offering; they are not the same as access to Gemini 4 Argon.

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How much does Gemini 4 Argon cost?

Google lists introductory rates of $2 per million input tokens and $10 per million output tokens. Cached input tokens are priced 95% below the input-token rate. Google says the rates will later become $4 per million input tokens and $20 per million output tokens, but it has not specified when the introductory period ends. These listed prices do not change the fact that access is currently restricted.

How should an enterprise evaluate Argon?

Benchmark scores and token prices alone cannot show whether a model is a good fit for a company’s work. InfoWorld quotes Pareekh Jain, CEO of EIIRTrend and Pareekh Consulting, advising buyers that “the key metric is not cost per token, but the cost per successful business outcome.” Treat that as buying advice, not a measured result.

For a meaningful comparison, run representative tasks and assess:

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  • Task success, correctness, and hallucination rate.
  • Reliability across long workflows, including how often agents fail or need human intervention.
  • Latency and total token costs for the work actually performed.
  • Privacy, security, and governance controls for your data and environment.
  • Integration effort, migration costs, and the cost of switching from existing tools.

InfoWorld’s reporting supports testing a model against an organization’s own workload and accounting for integration and migration—not assuming that a strong score on a published benchmark establishes business value.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.