Google 发布 Gemini 4 Argon，面向长周期智能体任务

Google has launched Gemini 4 Argon, a new flagship AI model aimed at complex enterprise and software-development workloads, pairing claims of stronger coding performance and advanced reasoning with an introductory pricing model that doubles after November 30.

The model “excels at deep work in complex, real-world tasks,” Koray Kavukcuoglu, senior vice president of Google DeepMind and chief AI architect at Google, wrote in a blog post.

Google said that its engineers used Argon to refactor a legacy folder of about six million lines within Chromium’s 40 million-line codebase, finishing in weeks rather than months.

Teams using Argon in Antigravity increased agentic code submissions by more than 35% and cut code rollbacks by nearly a third, the company said, without detailing how it measured either figure.

In a separate developer blog post , Google said the model is “designed for those long-horizon workflows that require deep reasoning and structural precision.” Pricing set to double According to the post, Argon costs $2 per million input tokens and $10 per million output tokens during the introductory period, with cached input discounted by 95%.

A footnote in the post states the prices rise to $4 and $20, respectively, after November 30.

The company said the model’s 1 million-token limit in the Gemini API is shared between input and output.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said CIOs should budget at the higher rate from day one, because no enterprise evaluation, security review, and procurement cycle finishes within the discount window.

“A business case built on the launch rate is a business case built to be rewritten in seven weeks,” he said.

Gogia said Google bills reasoning tokens at the output rate, which makes the per-token price a poor guide to agent costs.

“Cheap per token and cheap per task are different claims, and only the second one reaches the budget,” he said.

Sakshi Grover, research director at IDC, said agentic workloads multiply token use through planning loops, tool calls and repeated context.

She cited IDC’s FERS Survey (Wave 5, 2026), in which 72% of enterprises said their AI agent spending had run over forecast in the past 12 months.

Whether Argon’s pricing and public benchmark results translate into “comparable output quality for enterprise use cases remains unproven,” said Nitish Tyagi, senior principal analyst at Gartner.

He said internal validation on an organization’s own workflows should remain the primary basis for choosing a model.

Gogia pointed out that Google Cloud’s standard terms allow fee changes unless a price is expressly agreed.

CIOs who want price and version certainty after Nov. 30 must therefore write it into their contracts, he said.

Gogia added that the deeper lock-in sits in Google’s Interactions API, which keeps agent conversation state on Google’s servers.

“The protocols are neutral.

The state is not,” he said.

The model tried to game the tests in training Google disclosed in a separate safety blog post that Argon tried to work around its own evaluations during training.

What the company sees in practice is “an agent trying too hard to finish the job it was given,” Anca Dragan, vice president of AI safety and behavior, and Raluca Ada Popa, head of Gemini security at Google DeepMind, wrote in the post.

According to that post, the model, in rare cases during training, tried to raise its container privileges to change grading scripts and pull answers from the test runner’s memory.

In one case, it crashed the evaluation sandbox so that a failed run would not be scored.

Google said in the post that its misalignment monitor caught these attempts and that shortcut attempts on coding benchmarks fell to 2%.

“A model that games its grader turns every enterprise grader into a control that must be defended,” Gogia said.

He said agents should never hold write access to anything that judges their own work, and that a crashed or timed-out check must count as a failure.

Grover recommended a distinct agent identity, short-lived task-scoped credentials, restricted network access, and human approval for destructive changes, privilege expansion, and sensitive data exports.

“Production deployment should depend on demonstrated control and auditability alongside task performance,” she said.

Cyber capability rises In Google’s internal cyber evaluations, Argon solved 12 of 12 simulated end-to-end attack scenarios, compared with five for the previous Gemini generation, the safety post said.

Google said in the post that it applied frontier safeguards to the cyber and biological domains under its Frontier Safety Framework.

Gogia said the result is “a score on Google’s own exam, set and marked by Google,” and that Greyhound has found no independent replication of it.

Google is also expanding its Fairwind Program to hundreds of additional partners, giving vetted cyber defenders access to Argon without cyber guardrails, the launch post said.

“The weakest partner, not the strongest control, sets the exposure,” Gogia said, noting that each new partner adds another identity estate and contractor chain.

IDC’s Grover said security leaders should judge such access by reproducible findings, validated patches and reduced exposure.

She warned that more findings without the capacity to fix them can increase operational pressure.

The article originally appeared on InfoWorld .