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

One independent scoreboard, three launch tables, and the prices and access limits behind each flagship

Published 16 October 2026 Updated 16 October 2026

Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5 is a choice where every vendor's own table says the vendor wins. Google's table puts Gemini first on at least 13 of 19 tests; an independent index ties it for last. This article walks through one scoreboard none of the three labs controls, checks each lab's claims against it, and then covers what each model costs, what it needs to run, and who is allowed to use it today.

A leaderboard for every vendor

The releases came fast. OpenAI shipped GPT-6 Astra on September 3, 2026, Anthropic followed with Claude Opus 5.5 on September 22, and Google closed the month with Gemini 4 Argon on September 30. Three flagships in four weeks, and each one arrived with a benchmark table on which it looks like the winner.

Those tables are not lies. They are selections. Each lab picks the tests, the settings and the rivals it shows. So the useful move is to lean on one scoreboard none of them controls, and to read every vendor claim against it. The first thing that scoreboard shows is a leader none of the launch tables prepares you for.

Gemini 4 Argon vs GPT-6 Astra on one scoreboard nobody controls

On the Artificial Analysis Intelligence Index, Claude Opus 5.5 leads with 58 points, while Gemini 4 Argon and GPT-6 Astra tie at 53. That is an independent measurement rather than a vendor claim, and the 5-point gap is the clearest signal in the whole comparison.

Google's own table shows something different. It compares 19 benchmarks, and Argon finishes first or tied for first on 13 to 14 of them. That is a real result, but it is Google grading on a layout Google designed.

Take the coding row Google chose to show. On DeepSWE, Argon scores 77.9%, ahead of Opus 5.5 at 74.2%, with Astra a hair behind Opus at 74.1%. Read only Google's page and Argon is your coder.

Now switch to agentic coding in a terminal, the work most people would hand to an agent. On Terminal-Bench, Opus 5.5 scores 66.4%, Astra lands at 57.9% and Argon at 57%. Those numbers come from the launch pages and a third-party comparison, so treat them as a direction rather than a single controlled run. The direction is not subtle.

Anthropic's launch page also quotes GitHub chief product officer Mario Rodriguez saying Opus 5.5 solved more terminal tasks than Opus 5 in less than half the steps. That is a partner's word printed on the seller's own page: weigh it accordingly, not as an independent measurement.

Where Argon really leads

Claude leading the index does not make it better at everything. On the AA-Omniscience test from Artificial Analysis, Argon hallucinates 15% of the time, the lowest of any model scoring 45 or higher on the index, while GPT-6 Astra sits at 51%. That is more than three times the rate, on the same test, from the same independent team. If you are building anything that answers questions about facts, that gap turns into your support queue.

The community reaction overshot the result. A popular r/singularity thread declared that Argon had solved hallucinations, and the thread's own top comment pushed back, calling 15% a huge improvement but not a solution. The comment is the better reading of the number.

Argon also writes longer. It reads a context window of about 1 million tokens, and with a mode Google calls long decode it can generate up to 1 million output tokens in a single response. Astra reads about as much, but caps a single answer at 128,000 tokens.

Here is the catch, and it is the big one for Argon. At launch, the model is limited to trusted cyber defenders in Google's Fairwind Program. If you are not in it, the best hallucination score of the three is something you can read about, not something you can use.

Price per token, and price per task

None of these three is free, and none of them runs on your own hardware. All three are cloud models: you need an API key, there is nothing to buy or install beyond that, and you pay per token. The material here lists no local option and no hardware requirements, because there are none to list.

Claude Opus 5.5 costs $4 per million input tokens and $20 per million output. Anthropic says that is roughly 20% below Opus 5's list price, and about 40% cheaper on typical work once speed gains are counted. Those are Anthropic's own figures, not an outside measurement.

Argon starts lower, at an introductory $2 in and $10 out. At standard rates it rises to exactly what Opus charges, so the discount is a launch window rather than a permanent edge.

Astra is the outlier at $10 in and $50 out in standard mode, with a fast mode that doubles both. Per token, that is more than twice what Opus costs.

Price per token is not price per job. Artificial Analysis looked at what it costs each model to complete the tasks in its benchmark suite, and at introductory pricing Argon needs only about 60% of what Astra spends. A model that wastes fewer steps can win on the bill even when the sticker looks similar. Astra remains the most expensive of the three.

What Astra's premium buys

Astra's most striking result is a piece of history. It independently broke a German Army Enigma message dated July 10, 1941 (one that had resisted cryptanalysis since 2005) by writing its own Enigma and Bombe simulation software and recovering the correct key and plaintext. Researcher Carter Leffen validated the break.

That same skill has a darker side. OpenAI rates Astra Critical for cybersecurity under its Preparedness Framework, the first OpenAI model to get that rating, judged able to find and exploit previously unknown flaws in hardened systems largely without human help. Advanced offensive use is therefore restricted to a vetted-defender channel, and the rollout is staged, starting with OpenAI's Daybreak enterprise programme.

There is a quieter catch too: Astra's knowledge cutoff is April 30, 2026, so anything later has to come from you or from a search tool. Ask it about a library release from this summer and it simply will not know.

Google answered with demos of its own. Argon taught an Apptronik Apollo humanoid robot to play cornhole from a single video of a human player, building its own physics simulator and landing 7 consecutive throws after refining on the real robot. It also built a photorealistic pagoda temple complex in Blender from one prompt, with several agent copies critiquing each other. Both are Google's own demos, and neither tells you how the model will handle your codebase.

The verdict

If the vendor tables cannot settle it, two things can: the one job you will give the model most often, and whether you can get access today. A vendor's table answers neither.

If you build coding agents that live in a terminal, take Claude Opus 5.5. It leads the independent index and the terminal benchmark, it costs well under half of Astra per token, and it is shipping now: Anthropic's Python SDK supports the model from release v1.8.0, so installing the SDK and supplying an API key is the whole setup. Its catch: on Google's own coding table it trails Argon, and nothing in the material here gives its hallucination rate, so verify its facts yourself. Use it instead of Astra when the work is terminal-based agent coding; don't use it if you need a published hallucination number to show a risk reviewer.

If your product lives or dies on factual answers or very long outputs, Argon is the one to watch, but only if you are inside Fairwind. Everyone else waits for the wider rollout. And if you need deep security research and your company qualifies for OpenAI's vetted channel, Astra is the specialist, at the highest price of the three and with a hallucination rate more than three times Argon's.

A benchmark table tells you what the vendor chose to measure. The only benchmark that counts is the job you are about to hand over.

Our Short on this: mattpocock/skills: "Skills For Real Engineers".

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