Google unveiled Gemini 4 Argon, positioning it against Anthropic and OpenAI and saying it is already using the model internally. Argon’s single-response output limit rose to up to 1,000,000 tokens from 64,000, alongside a launch price of $2 per million input tokens and $10 per million output tokens. The model is being released first to selected cybersecurity experts and then to paying API customers and Google AI Ultra subscribers, with broader public access left without a set date.
Google announced Gemini 4 Argon, an AI model it says is strongest for coding, knowledge work, and cybersecurity, but it is not available for public use yet. Google says Argon saves 300 TiB of memory across its data centers and reports 77.9 percent on the DeepSWE v1.1 benchmark. Access is limited to Google engineers for now, while internal use and Rust-migration/benchmarking efforts continue until broader availability.
Google announced Gemini 4 Argon, a new frontier model rolling out first to a set of trusted cyber defenders via its Fairwind Program. It will launch with an introductory price of $2 per million input tokens and $10 per million output tokens. The release is staged with guardrails, government pre-release access, a larger 1M-token output limit, and gradual expansion to paid API customers and Google AI Ultra subscribers.
Google announced Gemini 4 Argon as its flagship model but is limiting access while it gathers early feedback and adjusts guardrails. The model is priced at $2 per million input tokens and $10 per million output tokens during an introductory period, before rising to $4 and $20. Access will roll out first to Fairwind participants, then paid API and AI Ultra subscribers, and only later to developers, enterprises, and consumers; overall it performs strongly on knowledge-work benchmarks but is weaker on some coding and cybersecurity measures.
Google announced Gemini 4 Argon, a new AI model aimed at complex software engineering, enterprise knowledge work, and cybersecurity defense. Access is being limited initially to a set of trusted cyber defenders, while Google works through the U.S. government’s voluntary pre-release access process. Over time, Google plans to gradually expand who can use the model.
Google has tested paying publishers when their content appears in Gemini-powered AI search answers, but reports say the payments are extremely small. About 100 publishers were admitted, and payouts are reportedly around 0.1% of advertising revenue. Publishers are becoming skeptical and some larger publishers have declined to participate, hoping for better terms.
MeetTwins, an AI avatar app in beta by Aditya Shinde, is being recruited to attend Google Meet calls on a user’s behalf and relay approved information to others. It is built using AI models from Sarvam and can be immediately stopped when someone types “/stop” in the meeting chat. The result is that operators can offload parts of routine meeting participation to a bot, with any off-brief questions passed back to the human after the call.
Google developed a way to watermark proteins designed by AI tools. Nearly a year after the risk was flagged, it still wasn’t clear what could be done to address detection gaps for AI-designed proteins. The watermarking approach gives those proteins a way to be identified, closing part of the biosecurity blind spot.
AI shopping agents that can compare and buy without viewing or clicking ads threaten the traditional advertising model built on human browsing of banners and sponsored results. The article cites that Google traffic to more than 2,500 news sites fell by one third worldwide within a year and by 38% in the US, and that Reuters Institute surveyed executives expecting another 43% drop over the next three years. As a result, major platforms and publishers are shifting toward monetizing agent-driven commerce via subscription fees, transaction commissions, or pay-for-results, plus new commerce and advertising protocols that let ads plug into the agent decision process instead of the user’s screen.
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