How Does Gemini 4 Argon Compare to Other GenAI Models?

2026-10-30

Key Takeaways

Gemini 4 Argon sits in the top group of frontier models but does not lead overall. Artificial Analysis scores it 53 on its Intelligence Index, level with GPT-6 Astra and Claude Fable 5.1, and behind Claude Opus 5.5 (58 at max effort) and Meta’s Muse Wonder Full (56).
In Google’s own four-way comparison with GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5, Argon posts the top score on 13 of 19 tests and shares the top score on one more.
Argon leads clearly on long-context reasoning, long-video understanding, legal and biology tasks, and business automation.
It trails on terminal coding, where Opus 5.5 scores 66.4 against Argon’s 57.4, and on computer use, where GPT-6 Astra leads.
Argon has the lowest hallucination rate of the group on Artificial Analysis’s knowledge test: 15%, compared with 51% for GPT-6 Astra and 54% for GPT-6.1 Sol.
It uses a lot of tokens: about 62,000 output tokens per benchmark task against roughly 27,000 for GPT-6 Astra, which raises its cost per task.

A lineup of generative AI models – artistic impression. Image source: Alius Noreika / AI

Gemini 4 Argon compares well with the other leading generative AI models of late 2026, but it is a specialist more than an all-round winner. Against GPT-6 Astra, GPT-6.1 Sol, Claude Fable 5.1, Claude Opus 5.5 and Meta’s Muse Wonder, Argon is the strongest at reasoning over very long inputs, understanding long videos, and handling legal, biology and workflow automation tasks. It is also the most factually careful of the group on independent hallucination tests. It falls behind Anthropic’s models on terminal-based coding and ranks mid-pack on composite leaderboards.

Cost tells a similar mixed story. Argon’s list price is competitive, especially before its introductory discount ends on November 30, 2026, yet its habit of thinking at length means it often spends more tokens per task than rivals at the same price.

Gemini 4 Argon on Independent Leaderboards

Artificial Analysis combines many tests into a single Intelligence Index, which makes it one of the more neutral ways to rank models from different labs. Argon scores 53, tied with GPT-6 Astra and Claude Fable 5.1.

Model	Developer	Artificial Analysis Intelligence Index
Claude Opus 5.5 (max effort)	Anthropic	58
Muse Wonder Full	Meta	56
Claude Opus 5.5 (high effort)	Anthropic	54
Gemini 4 Argon	Google	53
GPT-6 Astra	OpenAI	53
Claude Fable 5.1	Anthropic	53
GPT-6.1 Sol	OpenAI	52
Muse Wonder	Meta	52
GPT-6.1 Terra (max effort)	OpenAI	48

Other rankings place Argon higher. On the Vals Index, which focuses on professional tasks, Argon holds first place with Claude Sonnet 5.5 about two points behind. China’s 36Kr, summarizing Artificial Analysis results, noted that Muse Wonder Full reached roughly the level of Claude Sonnet 5.5, ahead of both GPT-6 Astra and Argon. The spread between rankings is a reminder that no single score captures every kind of work, a point our review of AI benchmark performance explores in more depth.

Head-to-Head Benchmarks: Argon, Astra, Fable 5.1 and Opus 5.5

Google’s model card puts Argon next to three rivals. The selection below covers the main categories. All figures are Google’s, and some differ from what the other labs report for their own models.

Benchmark	Gemini 4 Argon	GPT-6 Astra	Claude Fable 5.1	Claude Opus 5.5
Vals Index	68.9	63.1	65.8	67.0
AutomationBench	51.3	41.4	31.4	42.5
Harvey LAB (legal)	19.6	5.4	6.7	3.8
Vals Finance Agent v2	65.4	53.5	58.9	58.6
DeepSWE v1.1	77.9	74.1	67.4	74.2
FrontierSWE v2	55.0	65.5	56.3	62.3
Terminal-Bench 4.0	57.4	58.2	57.9	66.4
Terminal-Bench-Science	57.6	68.1	52.6	63.3
LABBench 2 (biology)	88.8	85.4	68.6	73.1
RiemannBench (math)	76.0	72.0	65.6	69.6
GraphWalks (256K to 1M tokens)	84.2	71.8	65.0	66.8
OSWorld-2.0 offline (computer use)	69.2	72.6	n/a	n/a
Chartography	71.6	71.0	46.2	66.3
LVBench (long video)	91.7	87.5	79.7	83.7
CWE-bench v1 (security)	68.0	68.0	58.0	67.0

Some testing choices favor Google. It ran Terminal-Bench-Science itself with a verifier timeout six times the usual length, and on LVBench it fed Gemini one frame per second while rivals were capped at 300 to 800 frames. The model card also skips popular tests such as SWE-bench Verified, GPQA and Humanity’s Last Exam, and leaves out GPT-6.1 Sol and Claude Sonnet 5.5.

Where Gemini 4 Argon Leads

Long context and long output

Argon’s widest margin over every rival appears on GraphWalks at 256,000 to one million tokens, where it scores 84.2 against 71.8 for GPT-6 Astra and under 67 for both Claude models. It also offers something none of the others match: a long decode mode that keeps writing in 64,000-token steps up to one million output tokens. Claude Opus 5.5 and Fable 5.1 cap output at 128,000 tokens.

Video, charts and multimodal input

Argon accepts text, images, video, audio and PDFs in a context window of 1,048,576 tokens. It leads LVBench for long-video understanding and edges GPT-6 Astra on chart reading. Google also reports an 87% score on ERQA, a test of spatial reasoning for robots, and a demo in which Apptronik’s Apollo humanoid used Argon to play cornhole.

Professional agent work

The legal results stand out most. On Harvey LAB, Argon scores 19.6, nearly three times the next-best score in Google’s table. It also leads finance agent tasks and AutomationBench, which measures business workflow automation. These are the areas where enterprises may see the clearest gains from switching.

Where Gemini 4 Argon Falls Behind

Coding in a terminal remains Anthropic’s territory. Claude Opus 5.5 leads Terminal-Bench 4.0 by nine points, and on Artificial Analysis’s own run of the same test, Claude Sonnet 5.5 scored 64%, Opus 5.5 60% and Argon 57%. GPT-6 Astra leads FrontierSWE v2 and Terminal-Bench-Science, along with offline computer use on OSWorld-2.0.

Real-world tests expose sharper gaps. When Computing for Geeks asked several models to write Kubernetes manifests, Argon’s files failed to deploy because of a recurring permissions mistake, while Claude Opus 5.5 and GPT-6.1 Sol produced working configurations. Argon fixed the issue when shown the error, but first-pass reliability is what matters most for automated pipelines.

Accuracy and Hallucinations

Argon’s most underrated strength may be restraint. On Artificial Analysis’s AA-Omniscience test, which measures how often a model gives a confident wrong answer instead of admitting uncertainty, Argon’s hallucination rate was 15%. GPT-6 Astra came in at 51% and GPT-6.1 Sol at 54%. Google also says the model “answers questions more directly with less hedging” than its predecessors, so the lower error rate does not come at the cost of vague replies.

Google adds that Argon attempted to take shortcuts in only 2% of test cases and led Gray Swan’s indirect prompt injection benchmark, with attacks succeeding just 0.7% of the time across 15 adaptive attempts.

How Argon Compares With Meta’s Muse Wonder

Meta’s newest models arrived on October 12, after Google published its comparison table. Meta’s own figures put Muse Wonder Full at 79.0 on DeepSWE, ahead of Argon’s 77.9 and GPT-6.1 Sol’s 75.2. Muse Wonder Full also scores 56 on the Artificial Analysis index, three points above Argon. On legal work, Meta reports 13.1 for Wonder Full on a Harvey LAB variant run by Artificial Analysis, which uses a different setup from the version in Google’s table, so the two numbers cannot be compared directly. Our piece on Meta Superintelligence Labs versus the AI giants covers Meta’s wider push.

Price, Speed and Token Efficiency

Model	Input / output per 1M tokens	Approximate cost per index task
Gemini 4 Argon	$2 / $10 until Nov 30; $4 / $20 after	$1.99 (intro), $3.98 (standard)
GPT-6.1 Sol	$2 / $10	$0.72
Claude Opus 5.5	$4 / $20	About $1.82 (high effort)
Muse Wonder	$4 / $20	Not published
GPT-6 Astra	$10 / $50	$3.26
Claude Fable 5.1	$10 / $50	Not published

Argon’s per-token price is reasonable, but its long reasoning chains drive the cost per task up. Artificial Analysis found it writes roughly 62,000 output tokens per task, more than double GPT-6 Astra’s 27,000. Readers comparing bills should look at GPT-6 Astra’s token usage and cost alongside Argon’s, since Astra charges five times more per token yet uses far fewer.

On speed, Argon is quick when it does not think hard. Computing for Geeks measured average responses of 5.5 seconds at the default setting, with output streaming at 258 to 299 visible tokens per second, against 12.1 seconds for Opus 5.5 and 18.7 seconds for GPT-6.1 Sol. At the high thinking setting, Argon slowed to 15.3 seconds.

What Google Says Argon Does in Practice

Google has tested Argon heavily inside its own engineering teams. It reports that a refactor of around six million lines of Chrome code shrank from months to weeks. Argon rewrote 32,000 lines of hand-tuned SIMD code in the libgav1 video decoder, and the result ran 2.7 times faster than a Rust port. Agentic code submissions rose 35% while rollbacks fell by nearly a third. Much of this runs on Google’s own silicon, the subject of our article on Alphabet’s custom chips for Gemini.

Which Model Fits Which Job?

Gemini 4 Argon is the strongest choice for long documents, large codebases read in one pass, long videos, legal and financial agents, and tasks where a wrong confident answer is costly. Claude Opus 5.5 remains the safer bet for terminal coding and infrastructure work. GPT-6.1 Sol offers the best cost per task for general use, and GPT-6 Astra still leads several hard coding and computer-use tests at a premium price. Muse Wonder Full is the newest contender for agentic coding but is only available through Meta’s top subscription plans. For a closer look at how two of these rivals handle complex jobs, see our analysis of Fable 5.1 vs GPT-6 Astra on task execution.

If you are interested in this topic, we suggest you check our articles:

Sources: Google – Gemini 4 Argon, Google – Introducing Gemini 4 Argon, Computing for Geeks, Meta – Muse Wonder, 36Kr, eesel AI

Written by Alius Noreika