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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsGemini 4 Argon is a cybersecurity-focused model from Google, but its published results do not establish it as the best choice for every security team. On Google DeepMind’s CWE-bench v1 comparison, Argon scores 68.0%—tied with GPT-6 Astra, one point above Claude Opus 5.5, and ten points above Claude Fable 5.1. Google also reports separate vulnerability-discovery results for Argon. Access to its cybersecurity capabilities is controlled through the Fairwind program, so availability may matter as much as benchmark performance.
What Argon is intended to do for cybersecurity
Google announced Gemini 4 Argon on September 30, 2026, positioning it for complex software engineering, enterprise work and cybersecurity defense. Google says Argon can autonomously find, validate and patch critical software vulnerabilities. That is a vendor capability claim, not a guarantee that the model will find every flaw or safely produce a usable fix in a particular environment. Google’s announcement also lists an output limit of 1 million tokens, compared with the prior 64,000-token limit.
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For security teams, the central distinction is between vulnerability discovery and vulnerability remediation. Finding a potential weakness, validating that it is real, and generating a patch are related but different tasks. A score from one kind of evaluation should not be treated as a result for all three.
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Google DeepMind’s model comparison page lists these results on CWE-bench v1, which Google’s announcement describes as a benchmark for security-vulnerability remediation. The page does not display a publication date alongside the table; these are the figures currently published by Google DeepMind.
#1 Best Overall
| Model | CWE-bench v1 result |
|---|---|
| Gemini 4 Argon | 68.0% |
| GPT-6 Astra | 68.0% |
| Claude Opus 5.5 | 67.0% |
| Claude Fable 5.1 | 58.0% |
On this particular published scorecard, Argon ties GPT-6 Astra, edges Claude Opus 5.5 by one percentage point, and leads Claude Fable 5.1 by ten points. Those gaps are benchmark results, not proof of statistically meaningful differences or of better outcomes on a team’s own codebase. Google publishes the figures, and the available pages do not establish independent, controlled, same-task head-to-head testing of all four models.
Google’s wider comparison also cautions against turning this security result into a universal ranking. On coding evaluations, Google lists Argon at 55.0% on FrontierSWE v2 while GPT-6 Astra scores 65.5%; on Terminal-bench 4.0, Argon is listed at 57.4% and Claude Opus 5.5 at 66.4%. These are coding benchmarks, not cybersecurity outcomes, but they show that model standings vary by task. See Google DeepMind’s Gemini comparison.
Argon’s other published security evaluations
The Fairwind program page displays two additional Google DeepMind evaluation figures for Argon. They concern discovery and penetration testing, not the same remediation result measured by CWE-bench v1.
- Real-world Vulnerability Discovery: 85.8%. A separate discovery evaluation reported on the Fairwind page.
- Wiz Penetration Test Benchmark: 70.9%. A separate penetration-testing evaluation reported on the same page.
The page does not display publication dates alongside these figures, nor do the available sources provide enough protocol detail to interpret their comparability or statistical significance. Treat them as vendor-published results for their named evaluations, not as interchangeable percentages or a direct ranking against the models in the CWE-bench table. The Fairwind program page also displays a 0.7% indirect prompt-injection attack success rate for Argon at k=15 on a Gray Swan comparison chart; its description says lower is better. That is a distinct safety evaluation, not a measure of vulnerability-finding accuracy.
Rank #3
Who can use Argon’s cybersecurity capabilities
Argon’s cybersecurity capabilities are rolling out through Fairwind to trusted defenders under access controls and vetting. Google prioritizes governments, critical-infrastructure operators and core technology platforms, and also welcomes academic labs focused on defensive benchmarking. Its page reports more than 650 Fairwind partners globally; that is the program-wide partner count, not the number of partners with Argon access.
Approved partners may use Argon for authorized threat simulation, reverse engineering and malware analysis for defensive or academic research. Malicious tasks, including malware creation, are prohibited. Google says access is restricted to internal cybersecurity, incident-response or penetration-testing teams, requires user-level authentication and phishing-resistant multifactor authentication, and may require other applicable access controls. Access cannot be resold or shared.
Google says broader availability is planned for developers, enterprises and consumers, beginning with paid API customers and Google AI Ultra subscribers. Its September 30, 2026 announcement gives no firm general-availability date. Teams that are not eligible for Fairwind can consider CodeMender with publicly available models and other Google AI Threat Defense products; Google describes CodeMender as a specialized code-security agent that helps automate software fixes.
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What Argon costs, according to Google’s announcement
Google announced introductory API pricing of $2 per million input tokens and $10 per million output tokens, followed by prices of $4 and $20 per million tokens respectively after the introductory period. Cached input tokens are listed at a 95% discount. The announcement does not state when the introductory period ends, so teams should confirm current rates and eligibility before budgeting. These are announced token prices, not a full estimate of deployment cost, which also depends on usage and the team’s workflow.
Best Value
How to choose a model for your security workflow
A leaderboard is a starting point, not a deployment decision. Compare candidates against the actual work the team needs done, and keep discovery, validation and remediation distinct in both evaluation and reporting.
- Define the task. Specify whether the model should identify suspected vulnerabilities, validate findings, propose patches, assist with authorized penetration testing, or support analysis such as reverse engineering. Do not use a discovery score as a stand-in for patch quality.
- Check access and deployment eligibility. Determine whether the team can use Fairwind, or whether the available product and API options fit the intended use. Confirm current terms rather than assuming a planned broader release has occurred.
- Evaluate authorization and safeguards. Establish who can invoke the model, which environments it can reach, what actions require human approval, and how activity will be authenticated and audited. Google describes safeguards including refusal of harmful requests, resistance to indirect prompt injection, and monitoring of model reasoning and actions; it also says these mitigations remain under development before broad availability. They should complement, not replace, the organization’s own security controls.
- Test on representative work. Use the team’s own stack and controlled, authorized tasks. Review whether findings are valid, patches are correct and safe, and the system behaves appropriately when inputs are misleading or malicious. The published results do not establish production performance for every organization.
- Model the cost at expected usage. Estimate input and output volume separately, account for cached inputs where applicable, and verify which pricing tier is in force when the team deploys.
What the public evidence does—and does not—show
The available figures and program terms come from Google, the model’s developer. The published pages do not establish independent reproductions, controlled security testing across all named models, or outcomes representative of every organization’s production systems. Google’s scores can help identify which tasks to evaluate, but the team’s own authorized testing, operational controls and access requirements determine whether a model is a fit.
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