Gemini 4 Argon vs Claude Opus 5.5 vs GPT-6 Astra

Gemini 4 Argon is not the best at everything. Looking at the tables in Google's announcement and the independent write-ups, the picture is mixed: the model leads on long software tasks and legal-financial work, but trails Claude Opus 5.5 and GPT-6 Astra on terminal-based and science-type tasks. "Which is best" has no single answer; "which fits my work" does.

This post puts the models side by side. If you are not familiar with Gemini 4 Argon itself, first take a look at What Is Gemini 4 Pro (Argon)?.

This post is a compilation; I have not tried all three models on the same tasks myself. The numbers come from Google's announcement and DataCamp's write-up, and there was no independent reproduction on launch day. Sources are at the end.

Side-by-side table

Benchmark	Gemini 4 Argon	Claude Opus 5.5	GPT-6 Astra
DeepSWE v1.1 (long software tasks)	77.9%	74.2%	74.1%
Vals Index (general business tasks)	68.9%	67.0%	63.1%
Terminal-Bench 4.0	57.4%	66.4%	n/a
FrontierSWE v2	55.0%	n/a	65.5%
CWE-bench v1 (vulnerability repair)	68%	n/a	68%
GraphWalks (256K - 1M tokens)	84.2%	n/a	71.8%

"n/a" means the write-up did not report a value for that model, not that the model was not measured.

What each benchmark means

DeepSWE and Vals. Argon leads on both. The gap is about three points on DeepSWE and about a point and a half over Claude Opus 5.5 on Vals. That gap is small; measurement noise can erase it. But the direction is consistent.

GraphWalks. The clearest advantage is here: in long context between 256 thousand and 1 million tokens, Argon scores 84.2% and GPT-6 Astra 71.8%. If you work on very long documents or codebases, this is the most meaningful row.

Terminal-Bench. Argon is clearly behind here: 57.4% against Claude Opus 5.5's 66.4%. In workflows where an agent runs commands in the terminal, reads the error and fixes it, which is what agents like Claude Code or Hermes do every day, this gap can matter.

FrontierSWE. On hard mixed science-and-coding tasks, GPT-6 Astra is ten points ahead of Argon, at 65.5% versus 55.0%.

Harvey legal agent. Argon 19.6%, Fable 5.1 6.7%, GPT-6 Astra 5.4%. The numbers are low because the benchmark is hard, but the relative gap is large. If you do legal work, this is the most meaningful comparison.

Pricing

Gemini 4 Argon's intro price is $2 per million input tokens and $10 for output, and $4 and $20 after the intro period. Cached input gets a 95% discount. According to one of the write-ups, on the same task set Argon's intro-period cost per task is reported as about $1.99, versus $3.26 for GPT-6 Astra. Read that as a value reported by a single source.

I am not giving Claude Opus 5.5 and GPT-6 Astra prices in this post: my sources have no reliable figure to compare against, and rather than make one up I suggest checking the official pricing pages.

Which to pick, and when

This section is my interpretation; general conclusions I draw from the numbers in the table.

• Very long documents, legal or financial text, reading a large codebase in one pass: these are Argon's strengths.
• Agent loops that run in the terminal, execute commands and debug: By the current numbers, Claude Opus 5.5 leads.
• Hard scientific and mixed coding tasks: GPT-6 Astra.
• Security vulnerability repair: Argon and Astra are level.

But there is one fact on the table: most people cannot use Argon right now. I covered who has access and when it opens in a separate post. If you are choosing today, your options are Claude Opus 5.5 and GPT-6 Astra. For how to compare tools on your own work, see the vibe coding tools comparison.

How to read benchmarks

Watch for three things:

1. Launch-day numbers are usually the benchmarks the maker chose. Google's announcement highlights the benchmarks where Argon leads; Terminal-Bench and FrontierSWE, where it trails, show up in the write-ups.
2. The agent framework changes the result. The same model scores differently in a different harness. I explained this in What Is an AI Harness? The DeepSeek Example.
3. Measure your own task. According to The Next Web, some Google employees have voiced doubts that the model is as good in real work as it is on benchmarks. Five tasks of your own are more instructive than fifty of someone else's.

Frequently Asked Questions

Is Gemini 4 Argon better than Claude Opus 5.5?

By the announced numbers, slightly ahead on DeepSWE and Vals and clearly behind on Terminal-Bench. "Better" depends on the type of work.

Which is better for writing code?

Argon appears to lead on long software tasks and Claude Opus 5.5 on agent workflows that run in the terminal. But if you cannot access Argon, the question is theoretical for now.

Can I use Gemini 4 Argon together with Claude Opus 5.5?

Argon has no general access. Once general access opens, you could pick both in agent tools that call different models through a single framework; that cannot be tested yet.

Which is cheaper?

Argon's intro price is $2 / $10. Check the other models' prices on the official pages; this post gives no figures for them.

Sources

• Google, Gemini 4 Argon: our next era of frontier intelligence (September 30, 2026): Argon's own benchmark results and pricing.
• DataCamp, Gemini 4 Argon: Benchmarks, Pricing, and Access: Claude Opus 5.5, GPT-6 Astra and Fable 5.1 values.
• Fello AI, Gemini 4 Argon: Benchmarks, Price and Who Gets It: cost-per-task comparison.
• The Next Web, Gemini 4 Argon: Google's new flagship reaches cyber defenders first: employee doubts.