Gemini 4 Argon Explained: Benchmarks, Pricing, and Access

Google’s Gemini 4 Argon, announced on September 30, 2026, is a frontier AI model designed for long-horizon software engineering, enterprise knowledge work, and cybersecurity defense, with an unusually large maximum output of one million tokens per response compared with the previous 64,000-token limit. Initially available only to trusted cyber defenders through the Fairwind Program, it is expected to expand later to paid API customers, Google AI Ultra subscribers, developers, enterprises, and consumers, with introductory API pricing of $2 per million input tokens and $10 per million output tokens. Google reports that Argon leads competing models on several benchmarks involving legal work, business automation, finance, long-context reasoning, and some coding tasks, while trailing rivals on terminal-focused software engineering, computer use, and certain science and machine-learning evaluations. Google cites internal uses including quantum optimization, data-center memory savings, and large C/C++-to-Rust migrations, and positions the model as capable of finding and patching vulnerabilities for authorized defenders. Its extended output capacity may enable longer agent workflows but also requires stronger timeout, streaming, cost-control, and full-result evaluation practices. Google describes safeguards against cyber and CBRN misuse, prompt injection, misalignment, and unsafe sandbox activity, though the source emphasizes that organizations should independently test agents, validate produced code and tool actions, and assess security behavior before deploying Argon-powered systems.

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