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Google launches Gemini 4 Argon with 1M token output, claims lead on DeepSWE and cybersecurity benchmarks

Google has introduced Gemini 4 Argon, a new frontier AI model designed for complex, long-horizon tasks in software engineering, enterprise knowledge work, and cybersecurity defense. The model features an industry-leading 1 million token output limit, up from 64,000 tokens in the previous generation. Google is rolling out Argon initially to trusted cyber defenders through its Fairwind Program and…

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Key points

  • Gemini 4 Argon launches with 1M token output limit and introductory pricing of $2/$10 per million input/output tokens.
  • Google claims Argon leads on DeepSWE v1.1 (77.9%), Vals Index (68.9%), and cybersecurity benchmarks; trails on FrontierSWE v2 and OSWorld-2.0.
  • Model available first to Fairwind Program cyber defenders; thousands of Googlers use it internally for coding, research, and infrastructure optimization.

The sources agree on core specifications and internal use cases but differ on benchmark comparisons and competitive positioning. The official Google blog and Digit.in emphasize Argon outperforming GPT-6 Astra on several benchmarks, while Marktechpost provides a detailed breakdown showing Argon leads on 12 of 18 benchmarks—including DeepSWE v1.1 (77.9%), Vals Index (68.9%), and AutomationBench (51.3%)—but trails GPT-6 Astra on FrontierSWE v2 (55.0% vs 65.5%) and OSWorld-2.0 (69.2% vs 72.6%), and trails Claude Opus 5.5 on Terminal-Bench 4.0. Artificial Analysis independently scores Argon 53 on its Intelligence Index, noting it generates more tokens per task than the median. Only Marktechpost reports that pricing will rise to $4 input and $20 output after the introductory period and that Logan Kilpatrick confirmed the launch pricing.

Model pages: Gemini 4 Argon → · GPT-6-Astra → · Claude Opus 5.5 →

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Full story from digit.in · via Search: GoogleOpen source ↗

Google introduces Gemini 4 Argon AI model, claims it outperforms GPT 6 Astra on several benchmarks

digit.in · 30 September 2026

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This text was published by digit.in. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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