Gemini 4 Argon: 1M Output Tokens at $2/$10 — AI Digest

Gemini 4 Argon: 1M Output Tokens at $2/$10 — AI Digest

Google shipped Gemini 4 Argon: first on 13 of 19 benchmarks, up to 1M output tokens and $2/$10 at the launch discount. OpenAI nears $70B in annualized revenue.

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Google ships Gemini 4 Argon: first on 13 of 19 benchmarks and 1M output tokens

Google DeepMind introduced Gemini 4 Argon, a model for coding, enterprise knowledge work and cyber defense that Google says beats GPT-6 Astra and Claude Opus 5.5 on 13 of 19 published benchmarks. The launch was announced by Google DeepMind and on the Google blog. On DeepSWE, Gemini 4 Argon scores 77.9% versus 74.2% for Opus 5.5 and 74.1% for Astra, The Rundown reports. Access starts with government users and trusted cyber defenders in the Fairwind Program; Google says developers, enterprises and consumers come next, once guardrails are refined (Google, Demis Hassabis). Standard pricing is $4/$20 per million input/output tokens; a 50% introductory discount with no announced end date brings it to $2/$10, and cached input is 95% cheaper (Philipp Schmid).

Gemini 4 Argon writes up to 1M tokens per answer, nearly 16x the old 64K cap

Google cites a 1-million-token output limit for Gemini 4 Argon, up from 64,000 (Google AI). Long Decode Continuation is an API feature that pauses a long response and resumes it across subsequent calls. That is how Artificial Analysis reached 1M output tokens, while Vals lists a 262K maximum — so the "1M" figure depends on how the model is called. In the Artificial Analysis run, Gemini 4 Argon scores 53 on the Intelligence Index, level with GPT-6 Astra and one point above GPT-6.1 Sol. A task costs $1.99 with Gemini 4 Argon versus $3.26 with Astra at discounted pricing, or $3.98 at standard pricing. The saving comes from price, not frugality: Argon averages 62,000 output tokens per task against Astra's 27,000.

Gemini 4 Argon agents freed 300 TiB of memory at Google and are moving 800K lines of C/C++ to Rust

Google says agents built on Gemini 4 Argon freed more than 300 TiB of data-center memory and are migrating more than 800,000 lines of C/C++ kernel code to Rust (summary of the report). In a video decoder, the agents replaced 32,000 lines of SIMD code with safe Rust, making the existing Rust port 2.7x faster with identical output. Google's team also says internal agent loops on Gemini 4 Argon helped complete the CK conjecture. These are Google's own claims and have not been independently verified. Ready-made agent workflows for your own tasks live in the AI SKILLS automation templates catalog.

OpenAI nears $70B in annualized revenue and talks about raising $30B at a $1.4T valuation

OpenAI is close to $70 billion in annualized revenue and in talks to raise $30 billion at a $1.4 trillion valuation, with its IPO pushed to next year, according to The New York Times, as relayed by reporter Sri Muppidi. The same week, OpenAI attributed the core of a campaign to extract its models' hidden reasoning to individuals linked to Moonshot AI: it recorded 16,000 attempts from more than 4,000 users in two days, with related activity across more than 15,000 users (summary). Outside researchers say their attacks kept working on GPT-6 Astra until this week and that patches were hard to propagate across product versions and third-party hosts (Jonas Geiping). Nathan Lambert argues the vulnerability is the API provider's responsibility.

Ethan Mollick: agents learned to work as a group on their own — the Bitter Lesson strikes again

On October 1, 2026, Ethan Mollick admitted a forecasting miss: he expected that getting AI agents to work as a group would require building something like a company, but newer models handle it themselves. In his essay The Dot and the Swarm he writes that he "fell prey to The Bitter Lesson" — the pattern in which elaborate human-designed rules keep losing to the scale of machine learning. Mollick draws the parallel with prompting: elaborate templates and prompt chains that walked a model through a task step by step lost their value once models learned to plan the steps themselves. The practical takeaway for agent builders: less rigid orchestration, more effort on stating the goal and checking the result. How to brief models today — see the AI SKILLS prompt library.

Numbers and facts

Different views: is Gemini 4 Argon a new leader or tuned to the tests?

Independent evaluations confirm that Gemini 4 Argon puts Google back among the leaders, but not alone at the top: Artificial Analysis scores it level with GPT-6 Astra (53 and 53), while Vals ranks it first on its index at 68.9%. Benchmaxxing is tuning a model to public tests so that leaderboard numbers run ahead of real-world usefulness. That is what some observers suspect of Gemini 4 Argon.

Tools and techniques

In brief