GetPack

Posted on Oct 4 Originally published at getpack.fr

Gemini 4 Argon: why you can’t use it (yet)

#ai #gemini #security #students

First published in GetPack Magazine, sourced guides to use AI well as a student.

On September 30, 2026, Google announced its most capable model to date, Gemini 4 Argon. First on 12 of 18 benchmarks, the best hallucination rate on the market according to an independent firm: on paper, that is a clear step forward. Except you can’t use it. Not in the Gemini app, not in AI Studio, not through the API: Google is reserving it first for a circle of « trusted cyber defenders », with no date set for a public rollout.

In short: Gemini 4 Argon, announced on September 30, 2026 by Koray Kavukcuoglu (SVP, Google DeepMind), posts record scores on 12 of 18 benchmarks and extends output to one million tokens. It is first deployed to vetted cyber defenders through the Fairwind program and the US government (CAISI), with a guardrail-free version reserved for that same circle. A public rollout will follow « in phases », with no firm date. Clubic points out that Google’s comparison leaves out two rivals at the same price. This is not an isolated case: Anthropic did the same in April 2026 with Claude Mythos Preview.

What Google announced on September 30, 2026

In a post signed by Koray Kavukcuoglu, SVP of Google DeepMind and Chief AI Architect, Google describes Gemini 4 Argon as a « frontier model for real-world coding, enterprise knowledge work, and cyber defense », able to « sustain deep reasoning across complex, long-horizon workflows ». It is the first model of the Gemini 4 generation.

Two technical changes come with the announcement: the output limit rises to one million tokens per response, up from 64,000 for the previous generation, and the introductory price is $2 per million input tokens and $10 per million output tokens ($4 and $20 afterward), with a 95% discount on cached input tokens.

One million output tokens: what that actually means

A token is a small word fragment the model processes one at a time. 64,000 output tokens was already enough for a long report. One million tokens is roughly the equivalent of an entire novel generated in a single response, or a large codebase rewritten in one pass rather than file by file.

In practice, for student use, this limit changes almost nothing: you never ask an AI for a million tokens at once to revise a course or debug a script. It is a capability built for enterprise use (massive code refactoring, analysis of very long documents), not for a student’s daily workflow.

The benchmarks, and what Clubic and Artificial Analysis say about them

Of the 18 benchmarks Google published, Argon ranks first on 12 and ties on one. Example cited by Google: 77.9% on DeepSWE v1.1, versus 74.1% for GPT-6 Astra and 74.2% for Claude Opus 5.5.

The independent firm Artificial Analysis, which evaluates models with its own methodology, places Argon level with GPT-6 Astra on its Intelligence Index (53 points, one point above GPT-6.1 Sol), with a 15% hallucination rate, versus 51% for Astra and 54% for Sol. This rate measures the share of wrong answers among responses where the model didn’t actually know the answer: a low score means Argon prefers to say « I don’t know » rather than make something up. The flip side, noted by Artificial Analysis: its raw accuracy (50%) still trails GPT-6 Astra (63%) and even Gemini 3.1 Pro Preview.

Clubic, in its October 1 article, tempers the enthusiasm: Google’s published comparison excludes its two rivals at the same input and output price, Claude Sonnet 5.5 and GPT-6.1 Sol. In other words, Google is comparing itself to more expensive or older models, not to the ones that actually compete in its price bracket. And as long as access stays closed, no independent test on a regular account can confirm these figures.

Fairwind, CAISI, a guardrail-free version: why the model stays locked

Here is the core of the paradox. Google is not making Argon available to its paying users first: it is deploying it first to a « set of trusted cyber defenders » through its Fairwind program, while also taking part in the US government’s voluntary pre-deployment access process, CAISI (Center for AI Standards and Innovation, within NIST). Google joined this government program in May 2026 alongside Microsoft and xAI (OpenAI and Anthropic have taken part since September 2025), giving federal evaluators access to versions of the model with safety guardrails stripped back, to probe risks that surface-level testing would not reveal.

Google also plans to release a version of Argon without cyber guardrails, reserved for vetted defenders and its internal teams, while it develops further protections against malicious misuse and indirect prompt injections.

The rollout will then proceed « in phases »: paying API customers and Google AI Ultra subscribers first, then the general public « as soon as possible », with no date announced.

Mythos at Anthropic, Argon at Google: the new norm of phased releases

This pattern is not new. In April 2026, Anthropic announced Claude Mythos Preview, a model whose software vulnerability-finding capabilities proved so effective that the company chose not to release it publicly. Access was limited to around 50 organizations through a defensive coalition called Project Glasswing, later expanded to roughly 150 organizations across more than 15 countries.

The common thread: labs now judge some models capable enough in cybersecurity, offensive and defensive alike, to justify giving defenders access before the general public. It’s a shift worth watching: the raw power of a coding model can also make it a vulnerability-discovery tool, which changes how labs manage its release.

What you can actually use today with a student account

As of this article, Gemini 4 Argon is not available to any student, free or paid. What you can use today is still the previous Gemini generation, through the Google AI Plus offer: eligible higher-education students can activate twelve free months (verified via a university email address, with a payment card on file), an offer to claim before December 31, 2026. One caveat: the subscription does not stop automatically after a year — it switches to paid unless you cancel it yourself.

Nothing so far suggests Gemini 4 Argon will join this student offer before its public rollout.

If you code: what Argon’s cyber capabilities mean for your own projects

Even without access to Argon, the argument Google uses to justify it, that a sufficiently capable coding model can also spot security flaws, applies to any project you build with a mainstream AI. A script you generate quickly for a student project can contain the same kinds of flaws a « frontier » model is trained to catch: exposed secrets, missing access rules, missing validation. That’s exactly what a structured security audit against the OWASP Top 10 covers, worth doing before putting any app online.

Our take

Gemini 4 Argon illustrates a shift in how major labs operate: the most capable model is no longer necessarily the one you get first. The raw numbers Google announced are impressive, but they remain figures supplied by Google itself, on a comparison that excludes its direct price competitors, pending a full independent check. For a student, the real question isn’t « which model is the most powerful right now », since you don’t have access to it anyway: it’s making good use of the tools actually available today, and keeping the habit of securing whatever you build, regardless of the model behind it.

FAQ

Is Gemini 4 Argon available in France today?

No. As of this article, it isn’t available in the Gemini app, AI Studio, or the API, anywhere in the world: only vetted cyber defenders and US government evaluators have access.

Why doesn’t Google compare Argon to all its direct competitors?

Clubic notes that Google’s published comparison omits Claude Sonnet 5.5 and GPT-6.1 Sol, two models at the same price as Argon. The scores remain Google’s own, pending full independent testing.

What is CAISI?

The Center for AI Standards and Innovation is the US government body, within NIST, that tests frontier models from major labs ahead of release, with access to versions stripped of safety guardrails.

Is this the first time a lab has held back a model for cyber defenders?

No. Anthropic did the same in April 2026 with Claude Mythos Preview, limited to a defensive coalition before any wider rollout.

What can I use instead as a student?

The current generation of Gemini, available through the Google AI Plus offer with twelve free months for eligible students until December 31, 2026, or any other mainstream model already available.

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