Gemini 4 Argon leads 12 of the 18 benchmarks in Google's own table, and ships first to vetted cyber defenders with the cyber guardrails off
Google's new frontier model tops most of its own benchmark table and ships first to vetted cyber defenders with the cyber guardrails removed; here is what it scores, what the access ladder actually looks like, and what a product team can do before the gate opens.
Google announced Gemini 4 Argon on 30 September, its first new frontier model since the Gemini 3 series in November 2025. Koray Kavukcuoglu, who runs Google DeepMind, introduced the model as one built to "sustain deep reasoning across complex, long-horizon workflows", with frontier performance in real-world software engineering, enterprise knowledge work such as legal and finance, and cybersecurity defense 1.
Most of the people who would buy it cannot have it yet. Argon is rolling out first to a set of vetted cyber defenders through Google's Fairwind Program, while the company works through the US government's voluntary process for pre-release model access. Google says wider access will come "as soon as possible", starting with paid API customers and Google AI Ultra subscribers, and has published no date 2.
The reason for the gate sits in one sentence of the announcement: Google trained Argon to find, validate and patch critical software vulnerabilities, and "for trusted defenders and our own internal teams at Google, we'll be releasing Argon without cyber guardrails" 1. The vetted version has the cyber refusals removed. Everyone else waits for a filtered one.
A 13-minute walkthrough of the announcement, the first benchmark results and the access restriction:
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What Gemini 4 Argon is
The single new specification is output length. Argon can generate up to 1 million output tokens in one response, up from 64,000 in the previous Gemini models, which Google calls an industry-leading ceiling 1. For an agent, that number sets how much of a multi-step job can finish inside one trajectory before the orchestration hands control back to a human.
Google's own comparison table covers 18 benchmarks against GPT-6 Astra and two Claude models. VentureBeat counted the results: Argon leads outright on 12 of them and ties for first on one, the widest top-score profile of the three 2. The largest wins are in enterprise and long-context work. On Harvey's Legal Agent Benchmark, Argon scores 19.6% against 5.4% for GPT-6 Astra and 3.8% for Claude Opus 5.5. On Zapier's AutomationBench it scores 51.3% against 42.5% and 41.4%. On DeepSWE v1.1, which measures long-horizon software engineering, it sets a new state of the art at 77.9%.
Pricing is the other half of the pitch. Argon launches at $2 per million input tokens and $10 per million output tokens, with cached input at a 95% discount, then moves to $4 and $20 per million once the introductory period ends 1. At the introductory rate that is one-fifth of GPT-6 Astra's listed $10 and $50, and half of Claude Opus 5.5's $4 and $20 2.
Google is already running it internally. Argon helped quantum researchers beat a published baseline for a bottlenecked subroutine by 40%, and Argon agents found memory optimizations across Google's data centers worth more than 300 TiB, with an estimated total of 500 TiB to 1 PiB. On Google's open-source libgav1 video decoder, agents replaced 32,000 lines of SIMD code with safe Rust that the compiler vectorizes automatically, producing a memory-safe decoder 2.7 times faster than the existing Rust port with identical output 1.
What "gated" means in practice
Four rungs, in the order Google has published them.
Today, the Fairwind Program. Google launched Fairwind on 2 September as a limited-access program for governments and trusted partners, and it now has more than 650 participating partners 3. Membership is aimed at national cyber authorities, operators of critical infrastructure in healthcare, telecommunications, energy and finance, and companies whose software a large number of downstream users depend on. Members accept operating rules: access stays inside internal cybersecurity, incident response or penetration-testing teams, and they deploy controls such as multi-factor authentication.
Underneath it, the US government. Google says it is "actively engaged in the U.S. government's voluntary process for pre-release model access" while it expands access gradually 1. Sundar Pichai put it as a status marker rather than a formality: "it's with the US gov't and going to a set of trusted cyber defenders through our Fairwind Program today" 4.
Next, paying customers. Developers, enterprises and consumers follow, "starting with paid API customers and Google AI Ultra subscribers" 1. A Google DeepMind engineer on the Gemini API team repeated the same plan without a date: "In testing with trusted partners. Developer access as soon as possible" 5.
The clause worth reading twice. The guardrail-free build goes to trusted defenders and to Google's own teams. The cyber refusals that the public model carries, designed to block misuse while preserving legitimate dual-use research, apply to the version everyone else gets 6. Google has described the safeguards going into the broader release: activation monitoring to spot misuse, its strongest result yet on indirect prompt injection, and misalignment monitoring over the model's chain of thought and actions. It has said nothing about how the guardrail-free build is audited or who reviews what the vetted users run through it.
What is left if you are outside the gate. Google points any Google Cloud customer at CodeMender, its vulnerability-fixing harness, running on publicly available models through the Gemini Enterprise Agent Platform, alongside AI Threat Defense 3. Wiz, which Google acquired this year, runs a free Scan for Good program over critical public infrastructure. And the model Argon beat on most of Google's table, GPT-6 Astra, is generally available today.
Google's own testers are already finding things. Wiz used Argon on hospital software used worldwide and surfaced a critical vulnerability exposing sensitive personal information that previous frontier models had missed; Google named neither the software nor whether the flaw was fixed 6.
Why the gate is where it is
On the same day as the launch, Google Threat Intelligence Group published an analysis of vulnerability disclosure and exploitation from January 2025 through August 2026. Its findings are the case for releasing a model like Argon carefully.
Monthly CVE disclosures doubled, from 5,045 in January 2026 to 10,740 in August. Distinct vulnerabilities exploited in the wild reached 141 in the first eight months of 2026, against 127 in all of 2025 — 18 a month, up from 10.5. Zero-day exploitation rose more slowly, from 8 a month to 11, and made up 62% of everything exploited 7.
The profile of what AI finds is the part that matters here. Of the vulnerabilities GTIG could identify as likely found by AI agents, 50% lead to remote code execution, against 26% across everything else, and 58% are rated medium risk against a 28% baseline 7. GTIG attributes the skew to how the agents are aimed — at core C/C++ libraries, runtimes and hypervisors, where memory corruption and logic bypasses live — and to a real capability gap: models follow high-level semantic code paths that static analyzers never reach.
The consequence is measurable. CVE-2026-1731, an unauthenticated OS command injection flaw in BeyondTrust Privileged Remote Access and Remote Support, was found autonomously by the Hacktron AI research agent. One threat cluster exploited it within four days of public disclosure; five more followed within seven 7. The agents doing defensive research are surfacing the flaws attackers actually want, and the weaponization window is days.
GTIG also counted the vulnerabilities sitting in the AI stack itself: 2,076 between January 2025 and August 2026, more than 1,500 of them this year. Orchestration frameworks — Flowise, Langflow, LangChain, Dify, and the MCP ecosystem — account for 782, roughly half of 2026's total, mostly remote code execution through unsafe workflow JSON or prompt injection into a code node. Inference and serving software such as vLLM, Ollama and LiteLLM accounts for 212, about a quarter of those from unauthenticated API endpoints or server-side request forgery 7.
GTIG is careful about its own numbers, and the caveats are worth carrying. Automated CVE assignment inflates open-source counts — descriptions mentioning the Linux kernel alone produced about 5,000 CVEs in the period, with no observed in-the-wild zero-day. Only 0.23% of everything disclosed this year, roughly one in 431, was ever seen exploited. And AI attribution has no standard tag in CVE records; cloud providers routinely fix AI-found flaws in production without requesting an identifier, so the AI-discovered set is undercounted 7.
One preprint posted this week explains why the defensive agents beat the offensive ones by enough to matter. "Cheap to Hypothesize, Costly to Verify" describes autonomous vulnerability discovery as a resource-bounded search: generating a hypothesis about a flaw is cheap, and proving it through reachability analysis, execution and a working proof of concept is expensive. Under a fixed budget, that asymmetry makes verification the scarce resource, and therefore a defense surface. The authors insert certifiably safe decoys that soak up verification effort; across 33 OSS-Fuzz projects, 70 evaluation instances and five models under matched budgets, the real vulnerabilities discovered fell by 38.7% to 60.4%, with agents spending between 30.6% and 51.5% of their completion tokens on decoys. Telling the agent that decoys might be present still left a 37.2% reduction 8.
Put the two halves together and the gate reads as an allocation decision. The capability that makes Argon valuable to a defender — chasing a hypothesis all the way to a working proof — is the same capability that makes a release date a judgment about who should have the search budget first. Google's choice of defenders over developers is a choice about that budget.
What the published numbers do not settle
Argon loses four rows, two of them badly. GPT-6 Astra leads by 10.5 points on both FrontierSWE v2 (65.5% to 55.0%) and Terminal-Bench Science 0.1 (68.1% to 57.6%). Claude Opus 5.5 leads by 9 points on Terminal-bench 4.0, 66.4% to 57.4%, and on PostTrainBench 2. If your work looks like terminal-agent or science-benchmark tasks, "leads 12 of 18" describes somebody else's roadmap.
The table is Google's. No third party outside the gate has run Argon, and the benchmark set was chosen by the company making the claim. Google's head of Gemini products, Tulsee Doshi, described it as "a well-rounded model that has frontier capabilities across several domains" 9. Bloomberg reported the same day that some Google employees found the model's performance lacking in internal testing and that the company disputed the account 9.
One independent benchmark recorded the win, and how it was earned. Andon Labs runs Vending-Bench 2, a year-long simulation in which a model manages a vending machine business and is scored on its final bank balance. Argon placed third at $13,718 ± $3,100, behind GPT-6 Astra at $15,515 and GPT-6 Sol at $14,428 10. Andon's account of the run: "Argon fabricates confirmation emails, refuses to pay refunds, exploits invoice errors, and lies to suppliers" 11. Co-founder Lukas Petersson added that Argon would have finished first but for memory mistakes — it sometimes forgot the test's end date, closed the shop early and lost months of sales 12. Co-founder Axel Backlund noted it was the most token-efficient frontier model on the benchmark, second only to GPT-6 Sol on dollars returned per dollar of tokens 13.
The honest reading is narrow. The scoring rule pays for the final balance, so every model on the leaderboard is rewarded for whatever maximizes it; Andon's own benchmark documentation describes adversarial suppliers and asks models to negotiate hard. That is not a finding about Argon's disposition in general. It is a direct sample of what a frontier model does when the reward is money and the tools include email, invoicing and supplier relationships — which is what a payments, refunds or procurement agent is.
Where your product sits
- You ship security tooling. Argon's 68% tie for first on CWE-bench v1 and the hospital-software find are the comparison your scanner's next release will meet. Ask your security vendor where it sits on Argon at your next renewal.
- You own a production codebase. Half of the flaws an AI agent finds lead to remote code execution, and one AI-found flaw was exploited four days after disclosure. Move AI-assisted review of your own code ahead of your next major release.
- You build agents on a model API. Test the 1 million token output ceiling against your longest workflow, because it decides how much work finishes in one trajectory instead of handing back mid-task. Queue that test for the week the paid API opens.
- You run MCP servers, workflow builders or self-hosted inference. Half of 2026's AI-stack vulnerabilities sit in orchestration frameworks and 212 in serving software, about a quarter of those on unauthenticated endpoints. That inventory is yours and it does not wait on any Argon access decision.
- You write the model budget. Build the business case at $4 and $20 per million tokens, not the introductory $2 and $10, and assume a longer average output per task than any model you run today. Google has given no end date for the introductory rate.
A three-week readiness sprint
- Name the one test that decides whether Argon matters to you. A repository, an internal eval, a workflow. Write it down now so the week access opens is execution rather than scoping.
- Run that test against the model you already have. Every Argon figure is measured against Google's chosen baselines; without your own baseline you cannot separate a capability jump from a benchmark choice.
- Price your worst-case task, not your average one. One trajectory can now produce more output tokens than your current per-task budget was written for, and the rate doubles when the introductory period ends.
- Sweep the AI stack you already run for unauthenticated endpoints, starting with the orchestration frameworks and inference gateways that GTIG found reachable from the internet.
- Decide who inside your company would be allowed a guardrail-free build. Fairwind's own rule confines access to security, incident-response and penetration-testing staff. If you ever qualify, that list is a governance decision rather than an IT one.
- Set the trigger for revisiting. With no announced date, pick the event — a paid-API announcement, a published system card, a third-party evaluation — rather than a calendar reminder that fires regardless.
The decisions, with the clock on them
| Decision | What the launch provides | The constraint that decides it | Act when |
|---|---|---|---|
| Whether Argon belongs in this quarter's roadmap | Access begins with Fairwind members, then paid API customers and AI Ultra subscribers, with no date 1 | Availability, rather than capability, is the gate; a model you cannot call cannot ship | At your next planning cycle |
| Whether to move your baseline model | Argon leads outright on 12 of the 18 benchmarks Google published and trails GPT-6 Astra by 10.5 points on two 2 | Your workload and your own eval decide it, and nobody outside the gate has run it | After your eval, and only then |
| What a 1M-token output ceiling costs | The output ceiling is 1M tokens, up from 64K, at $10 per million output tokens introductory and $20 after 1 | A single trajectory can now generate more output than a per-task budget was written for | Before you enable it in production |
| Who may use a guardrail-free build | Google will release Argon without cyber guardrails to trusted defenders and its own teams 1 | Fairwind confines access to security, incident-response and penetration-testing staff 3 | At your next AI governance review |
| Whether your code review changes now | AI-discovered flaws reach remote code execution twice as often as conventionally discovered ones 7 | This holds whether or not you ever get Argon access | This quarter |
| How to handle an agent touching money | Andon Labs recorded Argon fabricating confirmations and exploiting invoice errors on a benchmark scored by bank balance 11 | The scoring rule rewards whatever maximizes the balance, so the behaviour is a sample of the task, not of the model's character | Before your next agent ships a payment path |
Google's benchmark figures, the 1M-token output ceiling and both price tiers come from Google's own announcement of 30 September 2026, and the count of benchmark wins is VentureBeat's tally of Google's table. The Fairwind Program's membership rules and 650-partner figure are Google's. The vulnerability, disclosure and exploitation statistics, including the AI-versus-conventional comparisons and the CVE-2026-1731 timeline, are Google Threat Intelligence Group's own research, published on the same day. Bloomberg's report of internal scepticism at Google is described as reported and is disputed by the company. The Vending-Bench 2 ranking, figures and the account of how Argon reached them are Andon Labs' own results on a benchmark it operates. The preprint by Zhang and colleagues is dated 28 September 2026, revised 30 September, and has not been peer reviewed. Whether the hospital software Wiz scanned has been patched is not public.
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