Gemini 4 Argon vs Claude Opus 5.5: Which AI Model Should You Use?

Two of the strongest AI models in the world arrived within weeks of each other. Anthropic released Claude Opus 5.5 on 22 September 2026. Google announced Gemini 4 Argon on 30 September and opened it to paying developers on 13 October.

Both are built for the same kind of work: writing and fixing code, long research tasks, and "agents" that carry out many steps on their own. So which one should you use? This guide compares them on what they cost, what they can do, how they score on benchmarks, and, most importantly, how they did when independent testers gave them real work.

At a glance

A "token" is a small piece of text, roughly three-quarters of a word. A one-million-token context window means the model can read the equivalent of several long books in one go.

What each company is aiming for

Google describes Argon as a model for "real-world coding, enterprise knowledge work, and cyber defense". It is strongest on paper at understanding very long documents and long videos, and at legal and financial research. Access is being widened in stages: trusted cybersecurity teams first, then paid API customers and Google AI Ultra subscribers.

Anthropic says Opus 5.5 sets a new high in coding and knowledge work, beats its own larger Fable model on many tests, and costs less to run than Opus 5. Its output price fell from $25 to $20 per million tokens. It also writes in a plainer style, with less jargon and the important points first.

What the benchmarks say

Benchmarks are standard tests that let you compare models on the same tasks. Google's model card for Argon includes a head-to-head table. These are figures reported by Google, not independent checks:

On Google's own table, Argon scores higher on most rows. Opus 5.5 is ahead on four: FrontierSWE v2, Terminal-Bench 4.0, PostTrainBench and Terminal-Bench Science.

Independent rankings are split. Artificial Analysis, which runs its own set of ten evaluations, puts Opus 5.5 at about 58 on its Intelligence Index and Argon at about 53. It also found Argon used more tokens than most models to finish its tests, which matters for cost (more on that below). Vals AI, on the other hand, ranks Argon first on its Vals Index, narrowly ahead of Opus 5.5 (68.9% against 67.0%).

What happened in real tests

Benchmarks only go so far, so several writers gave both models the same real tasks. Their results do not all point the same way, which is useful in itself.

The New Stack ran three tests.

• Finding bugs: a tie. Both found all 14 hidden bugs in a 3,500-line Python library and fixed them without breaking anything.
• Building a billing system to a spec: Opus passed 80 of 80 hidden tests. Argon passed only 11, because of a single wrong line. With that line fixed by hand it would have passed, but the official score stood.
• Very long documents: at 500,000 tokens, Opus answered 33 of 33 questions correctly and Argon 20 of 33. That is the opposite of what Google's long-context benchmark suggests.
• Cost and speed: Argon was cheaper ($4.98 against $8.75 for all tests) and faster, but the author said they "wouldn't unquestioningly trust Argon on any task".

A developer blog (paddo.dev) replayed six real code changes from a production codebase, 20 runs per model.

• Accuracy: Argon was the most accurate. It passed every test in 16 of 20 runs, against 13 for Opus.
• Cost: Argon wrote 2.8 times as much output, so its total bill was higher even at the introductory half price: $57.68 against $45.81. At full price from 1 December, the same work would cost about 2.5 times as much as Opus.
• Speed and vision: Argon was slower, and weaker at turning screenshots into web pages.

DataCamp asked both to animate a robot arm that avoids obstacles. Opus kept to the rules for the whole run and scored 4.7 out of 5. Argon clipped through obstacles and scored 3.7.

Each of these is a small test by one person, so treat them as signals, not verdicts. Together they suggest that Argon can be brilliant but less consistent, while Opus 5.5 is steadier at following instructions all the way through.

Price: per token versus per job

Argon looks half the price, and until 30 November it is. But two things narrow the gap:

1. It tends to write more. Independent testers saw Argon use noticeably more tokens than Opus for the same work. You pay for every token, so a cheaper rate can still mean a bigger bill.
2. The discount is temporary. From 1 December 2026, Argon's price rises to $4 / $20 per million tokens, the same as Opus 5.5.

If you are cost-sensitive, measure the cost per finished task, not the price per token. Both models also offer discounts for repeated text (caching), and Anthropic offers 50% off for batch jobs that don't need an instant answer.

Which should you choose?

Choose Gemini 4 Argon if you:

• work with long videos or very large documents, where Google's tests show its biggest lead (check its answers, given the mixed independent results);
• build legal or financial research tools;
• want to experiment cheaply before the introductory price ends on 30 November;
• already work inside Google's tools, such as AI Studio, Android Studio or Antigravity.

Choose Claude Opus 5.5 if you:

• run coding agents in the terminal or on long, multi-step tasks;
• need a model that sticks to your instructions for the whole job;
• need long answers without extra setup;
• want to use it through AWS, Microsoft or Google Cloud, or simply in the Claude apps.

For most people, the best answer is to try both on your own work. Take three or four real tasks you do every week, run them through each model, and compare the quality of the result, the time taken and the final cost. Many teams now keep both and send each task to whichever model handles it best.

The bottom line

Gemini 4 Argon leads most of Google's own benchmarks and is the stronger choice for long video and very long documents, at a lower price for now. Claude Opus 5.5 leads the Artificial Analysis index and was the more dependable model in most hands-on tests, especially when a task has to follow strict rules from start to finish.

Neither is simply "better". The right choice depends on your task, so test with your own work before you commit.

Figures are as of 19 October 2026. Benchmark scores are reported by the model makers unless stated otherwise. Prices are in US dollars per million tokens for the standard API.

Sources

• Google: Gemini 4 Argon, our next era of frontier intelligence
• Google: Gemini 4 Argon is now available for developers
• Google DeepMind: Gemini 4 Argon model card
• Gemini API: Gemini 4 Argon model details
• TechCrunch: Anthropic releases Opus 5.5
• KDnuggets: Everything Claude Opus 5.5 actually ships with
• The New Stack: Gemini 4 Argon vs Claude Opus 5.5
• paddo.dev: Gemini 4 Argon vs Opus 5.5 on real code
• DataCamp: Gemini 4 Argon vs Claude Opus 5.5
• NeuralTrust: Gemini 4 Argon benchmarks and pricing

Cover photo by Growtika on Unsplash.