Gemini 4 Argon: The reasons I love Gemini are all written in Argon this time

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Yesterday, Google officially announced Gemini 4 Argon。

Strictly speaking, this is not a regular release that "everyone can use it now". Argon is being handed over to a group of trusted cybersecurity defenders through the Fairwind Program, and Google is also participating in the US government's pre-release model access process. Paid API users and Google AI Ultra users will be next in line, but Google has not given a specific date.

However, if we only understand it as a cautious grayscale launch, we would underestimate this update. From Google's announced design goals and internal use cases to the third-party testing that has already emerged, Argon is trying to answer a question more important than "Is chatting smarter?":

Can a cutting-edge model work continuously for tens of minutes, hours, or even longer, identifying problems, calling tools, reflecting, modifying, and actually getting things done?

This is exactly what I've always loved about Gemini. Rather than simply pursuing a sense of amazement in conversation, I prefer Google's direction of integrating multimodality, long context, tool calls, and real workflows into a complete system. Argon may not be number one in every test yet, but it is currently the next-generation Gemini that most closely resembles the final form of this approach.

Let's look at the most dramatic change first: the output limit of 1 million tokens.

The most eye-catching parameter of the Gemini 4 Argon is Google's maximum...outputIt increased from the previous 64K tokens to... 1M token。

The word "output" must be emphasized here. It doesn't simply mean that the model can read a million tokens, but rather that the reasoning, tool calls, and generation trajectories of a long task can have much more space than in the past. Google's explanation is also very straightforward: when a model can continuously think and generate hundreds of thousands of tokens in a single trajectory, some complex tasks that used to have to be broken down into many rounds and continued manually can theoretically be completed in one go.

This is certainly not necessary for regular chat, but it is important for code migration, in-depth research, financial due diligence, long video analytics, and Agent workflows.

The prices are also very aggressive. Google Announces Argon's Initial API Price $2 / 1 million Input token, $10 / 1 million Output tokenGet an additional 95% discount on cached input; It will resume after the initial offer ends. $4 / $20。 According to Artificial Analysis CalculationAt the current discounted price, Argon costs an average of about $1.99 to complete an Intelligence Index task, which is about 60% of GPT-6 Astra.

However, there is a noteworthy detail about this price advantage: Argon does not save money by "thinking less". Artificial Analysis observed that, on average, each task generated approximately... 62K Output tokenThe Astra is approximately 27K. In other words, Argon is more like a model that is willing to spend a long time reasoning, except that Google has temporarily kept the token price low enough.

Just how strong is it? The answer is: very strong, but not a "sweep of the entire list".

Google's official website provides a very nice comparison table:

Gemini 4 Argon Official benchmark Comparison

Official evaluation methodThere is a caveat that must be clarified first: most model scores other than Gemini come from publicly available or self-reported results, while some Argon scores are run by Google itself. Therefore, this is not a unified blind test of all models under the exact same harness.

Even so, these sets of data are still very representative.

test	Gemini 4 Argon	Main competitors	What do I think?
DeepSWE v1.1	77.9%	Opus 5.5 74.2%，Astra 74.1%	Long-term software engineering is very strong.
Zapier AutomationBench	51.29%	Sonnet 5.5 44.75%，Opus 5.5 42.47%，Astra 41.4%	One of the most eye-catching real-world business Agent tests to date
Artificial Analysis Intelligence Index	53	Astra 53，Opus 5.5 58	Comprehensive intelligence has entered the first tier, but not the first.
Terminal-Bench 4.0	Approximately 57%	Astra 59%，Opus 5.5 60%，Sonnet 5.5 64%	Terminal-based coding agents still lagging behind
Vals Finance Agent v2	65.40%	Current ranking #1	Multi-step research in finance is prominent.
LVBench	91.7%	Google calls it SOTA	Long video understanding continues Gemini's traditional strengths.

What I value most is Zapier AutomationBench。 Instead of asking the model a few math problems, it gives the Agent a simulated company: CRM, emails, forms, work orders, exchange rates, duplicate customer names, and rules hidden in old emails are all mixed together, and finally it directly checks whether the model has corrected the real state. Argon High 51.29% This is significantly higher than Claude Opus 5.5's 42.47% and GPT-6 Astra's 41.4%.

These types of tests are closer to the AI I want than single-round questions: not "answering as if you understand," but actually getting things done.

But on the other side,Artificial Analysis The overall Intelligence Index for Argon is 53This is roughly on par with GPT-6 Astra, but still lower than Claude Opus 5.5's 58. On Terminal-Bench 4, it also did not surpass the top models of Anthropic and OpenAI.

Therefore, the most accurate description this time is not "Gemini wins the championship overall," but rather:Google has finally regained a flagship model that has no significant generational differences and has its own advantage range in long tasks and Agent workflows.

My favorite part isn't actually the benchmark, but rather what Google has done with it.

Google's internal case study this time is more convincing than traditional benchmark scores.

The first is quantum algorithm optimization. Google claims that Argon helps quantum computing teams optimize the spatiotemporal resources of subroutines that limit the performance of critical applications, that is, qubits × gatesOne of the cases improved performance within minutes. 40%, exceeding published benchmarks.

The second is data center memory. An Argon Agent team analyzed profiling telemetry across the entire Google cluster, automatically finding and applying memory optimizations. Google expects these changes to be released first after they are fully rolled out. 300 TiB Above memoryThe total savings may reach 500 TiB to 1 PiB。

Third, and what I think is the most "Google-like" aspect, is the large-scale C/C++ → Rust migration.

Argon Agent is involved in repositories ranging from tens of thousands of lines of code like re2 and libgav1 to... Fuchsia Zircon kernel 800,000+ lines of codeThe migration. Of course, these key systems will not be launched immediately after the model is written; Google has made it clear that they will still undergo automated and manual audits, simulation tests, and reviews.

The example of libgav1 is particularly impressive: Argon, based on an existing Rust port, conducted multiple rounds of profile-guided experiments, analyzed the compiler output, and then... 3.2 million lines of SIMD codeReplace it with Rust, which is more secure and allows the compiler to vectorize automatically. The final version of the video output is completely consistent and performs faster than the original Rust port. 2.7 timesThis further approaches the highly optimized C++ version.

These examples all come from Google official releaseThis cannot currently be considered completely independent verification. But they at least illustrate one thing: Google is not treating Argon as a more chatty web model, but is actually using it to touch its own infrastructure, compiler, kernel, and research work.

This is why I naturally prefer the Gemini route.

Another interesting change: Argon seems more willing to admit "I don't know."

In Artificial Analysis's AA-Omniscience test, Argon's hallucination rate was only... 15%It is the lowest level among models with an Intelligence Index score of 45 or higher; GPT-6 Astra is 51%, and GPT-6.1 Sol is 54%.

But this does not mean that Google has "solved the illusion".

In the same test, Argon's accuracy was 50%This is actually lower than Astra's 63%. A more accurate explanation is that Argon is more inclined to stop guessing when he doesn't know, rather than making up an answer.

I actually really like this trade-off. For models that are actually intended for research, finance, law, or engineering work, "I'm not sure" is often more valuable than a smooth but incorrect answer.

The discussion following the post on Reddit is also quite representative. On one hand, there is a lot of excitement about "Gemini is back"; On the other hand, the most common complaint is not performance, but rather:Such a good model, why don't most people use it yet? r/GeminiAI Release Discussionand Discussion of r/singularityYou can see this conflicting emotion of "the benchmark scores are very appealing, but let me use them first".

This is actually Argon's biggest real-world problem right now.

Where does it fall short of the "strongest model"?

First,You basically don't need it now.

Argon is the first trusted cybersecurity partner for Fairwind, and ordinary developers, businesses, and consumers are still waiting. For a flagship model, the lack of widespread real-world use means we still lack a large number of external samples from the codebase, production environment, and long everyday conversations. Google's "rolling out soon" sounds nice, but until the API is actually opened up, it still has a bit of a product launch feel to it.

Second,Programming is not leading in every dimension.

In Google's own comparison, Argon looks great in DeepSWE, but it still loses to Astra or Opus in tests such as FrontierSWE v2, Terminal-Bench 4.0, PostTrainBench, Terminal-Bench Science, and OSWorld-2.0. In particular, Terminal-Bench, a powerful tool and strong execution coding agent scenario, is still a very stable area of advantage for Anthropic.

Third,"Longer thinking" is also a cost and a delay.

Argon spits out significantly more tokens in Artificial Analysis's comprehensive testing. The introductory discount price makes this seem like a good deal right now, but the advantage will diminish once the standard price returns to $4/$20. The true cost of a business is not "how much per million tokens," but...How much does it actually cost to complete a task, how long does it take, and whether to rerun it after failure?。

Fourth,It is not the absolute number one in the overall intelligence ranking.

A score of 53 in Artificial Analysis is enough to put Google back at the forefront, but Opus 5.5 currently still scores 58. In other words, if you just want to ask, "Who is at the top of a comprehensive intelligence list?", the answer is not Argon; If you ask, "Who deserves the most attention in complex business automation, long-trajectory tasks, and the multimodal scenarios that Google excels in," Argon becomes very competitive.

Gemini is back, but I'd rather say: Gemini has finally grown into the person she wants to be.

My preference for Gemini has never come entirely from the charts.

From native multimodal and ultra-long contexts to Google Search, Workspace, Android, Cloud, and now the increasingly complete Agent toolchain, what attracts me most about Gemini has always been that Google rarely treats the model as just a chat box; it wants to be a general-purpose assistant that can see, understand, retrieve, execute, and participate in real work for a long time.

Argon has pushed this approach to its most radical position to date.

It has a maximum output of 1 million tokens, a very strong track record in long-term software engineering and enterprise automation, and has already been used by Google to optimize quantum algorithms, data center memory, and hundreds of thousands of lines of system code. At the same time, it still loses to competitors in hardcore execution tests such as Terminal-Bench and FrontierSWE, and third-party comprehensive rankings do not place it absolutely number one.

I actually think it makes it more believable.

If every image of a model is number one when it is first released, then I would first wonder how the images were chosen. Argon now looks more like a truly "capability-shaped" model: it has particularly strong points and clear weaknesses.

For me, what I'm most excited about isn't how many SOTAs Google gets back, but that it has finally released another Gemini flagship that I'm willing to wait to use in person.

Once Argon truly opens up its API and Ultra, the first thing I most want to do is not to run the benchmark again, but to give it a real project that is long enough, dirty enough, and requires repeated trial and error, and then see if it can go from start to finish.

That's the real test of this generation of models.

Main references and further reading:

Cover image: Google official Gemini 4 Argon key art, from @Google's X post, Images are hosted by Google official CDN.

Gemini 4 Argon: The reasons I like Gemini are all written in Argon this time.