Gemini 4 Argon vs Opus 5.5: Which Is Victorious?

2026-10-29

Key Takeaways

Neither model wins outright. Gemini 4 Argon leads Claude Opus 5.5 on 14 of the 18 benchmarks where Google’s model card reports scores for both, while Opus 5.5 scores higher on the Artificial Analysis Intelligence Index (58 at max effort versus 53).
Argon is strongest at long-context reasoning, video understanding, legal and finance agent tasks, and chart reading.
Opus 5.5 is stronger at terminal-based coding: 66.4% on Terminal-Bench 4.0 against Argon’s 57.4%.
In a hands-on DevOps test, Argon’s Kubernetes manifests failed to deploy (0 of 12), while Opus 5.5 succeeded on all three attempts.
Both list at $4 per million input tokens and $20 per million output tokens, but Argon costs half that until November 30, 2026.
Argon can produce up to one million output tokens through long decode; Opus 5.5 tops out at 128,000 (300,000 in batch).

Two frontier AI models facing off – artistic impression. Image source: Alius Noreika / AI

Gemini 4 Argon and Claude Opus 5.5 split the victory between them. Argon wins more benchmarks in Google’s published comparison and pulls far ahead on long documents, long videos and professional agent tasks in law and finance. Opus 5.5 wins on agentic coding in the terminal, on independent composite rankings, and on reliability in real deployment tests. For teams choosing one model, the right pick depends on whether the work is mostly reading and analysis or mostly building and shipping code.

Price no longer decides it either. Both models list at $4 per million input tokens and $20 per million output tokens from December 1, 2026. Until then, Argon’s introductory pricing makes it half as expensive per token, although it tends to burn more tokens per task.

Gemini 4 Argon vs Claude Opus 5.5 at a Glance

Feature	Gemini 4 Argon	Claude Opus 5.5
Developer	Google DeepMind	Anthropic
Release	September 30, 2026 (general availability October 13)	September 22, 2026
API price (input / output per 1M tokens)	$2 / $10 until Nov 30, then $4 / $20	$4 / $20
Cached input	$0.10, then $0.20	$0.20 (cache writes $5)
Context window	1,048,576 tokens	1 million tokens
Maximum output	65,536 per request; up to 1 million with long decode	128,000 (300,000 in batch)
Reasoning control	Low, medium (default), high	Effort levels, default medium; thinking cannot be switched off
Knowledge cutoff	March 2026	June 2026
Artificial Analysis Intelligence Index	53	58 (max effort), 54 (high)

Benchmark Results: Where Each Model Leads

Google’s model card lists 19 tests for Argon and reports Opus 5.5 scores on 18 of them. Argon leads on 14 and trails on four. The full set is below, though several caveats follow that matter as much as the numbers.

Benchmark	What it tests	Gemini 4 Argon	Claude Opus 5.5
Vals Index	Mixed professional tasks	68.9	67.0
AutomationBench	Business workflow automation	51.3	42.5
Vals Finance Agent v2	Financial analysis agent	65.4	58.6
Harvey LAB	Legal agent work	19.6	3.8
DeepSWE v1.1	Software engineering	77.9	74.2
FrontierSWE v2	Hard software engineering	55.0	62.3
Vibe Code Bench	App building from prompts	91.9	90.3
Terminal-Bench 4.0	Command-line agent tasks	57.4	66.4
PostTrainBench	ML post-training tasks	45.3	49.3
Terminal-Bench-Science	Scientific computing in terminal	57.6	63.3
LABBench 2	Biology research tasks	88.8	73.1
RiemannBench	Advanced mathematics	76.0	69.6
GraphWalks BFS (up to 128K)	Long-context reasoning	99.7	90.6
GraphWalks (256K to 1M)	Very long-context reasoning	84.2	66.8
Agent’s Last Exam	Hard agent tasks	39.5	38.2
Chartography	Chart understanding	71.6	66.3
LVBench	Long video understanding	91.7	83.7
CWE-bench v1	Security weakness detection	68.0	67.0

The table shows 18 rows because Google did not report an Opus 5.5 score for OSWorld-2.0 offline, where Argon scored 69.2. Opus 5.5’s wins cluster in coding and terminal work: FrontierSWE, Terminal-Bench 4.0, Terminal-Bench-Science and PostTrainBench. Argon’s biggest margins appear in legal work, biology, workflow automation and anything involving very long inputs.

Caveats behind Google’s numbers

Vendor tables deserve a careful read, and this one has several quirks. Anthropic’s own figures for Opus 5.5 differ from Google’s: Anthropic reports 40.0% on AutomationBench (using Zapier’s setup without fallbacks) and 58.7% on Terminal-Bench-Science, against Google’s 42.5% and 63.3%. Google ran Terminal-Bench-Science itself with a verifier timeout six times longer than standard. On LVBench, Gemini received video at one frame per second, while rival models were capped at 300 to 800 frames. The table also leaves out widely used tests such as SWE-bench Verified, GPQA and Humanity’s Last Exam, and it omits GPT-6.1 Sol and Claude Sonnet 5.5, both strong competitors.

Independent leaderboards add more nuance. The Harvey legal leaderboard hosted on Vals places Meta’s Muse Spark above Argon, which sits sixth. Artificial Analysis gives Opus 5.5 an index score of 58 at maximum effort and 54 at high effort, against 53 for Argon. On its own Terminal-Bench 4.0 run, Artificial Analysis recorded 60% for Opus 5.5 and 57% for Argon, with Claude Sonnet 5.5 ahead of both at 64%.

Real-World Coding Test: Which Model Ships Working Code?

Benchmarks measure averages. Hands-on tests show failure modes. Computing for Geeks gave Argon, Opus 5.5 and GPT-6.1 Sol the same set of DevOps tasks: writing Kubernetes manifests, Bash scripts and OpenTofu infrastructure code.

The Kubernetes results separated the models sharply. None of Argon’s 12 generated manifests deployed successfully. The cause was a single repeated mistake: Argon set the nginx container to run as user 101 without adding a writable temporary volume, so nginx crashed with a “permission denied” error when it tried to create its cache directory. Opus 5.5 and GPT-6.1 Sol each deployed all three of their attempts. All three models handled the Bash and OpenTofu tasks without trouble, and Argon fixed its manifest as soon as it saw the error message.

Measure (9-call DevOps test)	Gemini 4 Argon (default)	Gemini 4 Argon (high)	Claude Opus 5.5
Kubernetes manifests deployed	0 of 12		3 of 3
Total cost	$0.045	$0.169	$0.254
Average latency	5.5 seconds	15.3 seconds	12.1 seconds

Argon was the cheapest and fastest at its default setting, streaming between 258 and 299 visible tokens per second. Opus 5.5 cost more but produced code that worked first time. For developers who pick a model for vibe coding, that difference in first-pass reliability can outweigh a cheaper token bill. Our look at the Codex and Claude Code upgrades covers how the coding agents built on these models compare.

Long Context and Multimodal Work

Argon’s clearest advantage lies in very long inputs. On GraphWalks between 256,000 and one million tokens, it scored 84.2 against Opus 5.5’s 66.8. Both models accept around a million tokens of context, but Argon keeps its reasoning sharper toward the end of that window. Our explainer on the Gemini 1 million token context window covers why this matters for codebases, contracts and research archives.

Output length is another gap. Argon’s long decode feature continues writing in 64,000-token steps up to a total of one million tokens, enough for a full book draft or a large code migration in one run. Opus 5.5 is capped at 128,000 output tokens in normal use and 300,000 in batch mode. Argon also leads on video: its 91.7 on LVBench beats Opus 5.5’s 83.7, even accounting for the frame-rate differences in testing.

Pricing: Which Model Costs Less?

Until November 30, Argon costs $2 input and $10 output per million tokens, half of Opus 5.5’s rates. From December 1, the two match at $4 and $20. The catch is token volume. Argon writes many thinking tokens, and Artificial Analysis estimates it costs about $1.99 per index task at the introductory price and $3.98 at the standard price. Community readings of the same chart put Opus 5.5 at high effort around $1.82 per task.

Each model offers its own savings tools. Anthropic provides a 50% batch discount, explicit cache control and a fast mode priced at $8 input and $40 output per million tokens for latency-sensitive work. Google offers half-price Batch and Flex tiers and automatic caching for repeated prompt prefixes of at least 4,096 tokens, but it does not support explicit cache objects on Argon.

Safety and Behavior

Both companies emphasize safety work. Google says Argon attempted shortcuts in only 2% of test cases, was checked by a misalignment monitor during training, and led Gray Swan’s indirect prompt injection benchmark with a 0.7% attack success rate across 15 adaptive attempts. In biology red teaming, testers unlocked fewer than 1% of restricted subtasks. Anthropic reports that Opus 5.5 ties Fable 5.1 for the lowest prompt injection success rate on Gray Swan’s benchmark. The two results come from different test setups, so they cannot be compared directly.

The models also handle sensitive cyber work differently. Anthropic routes cybersecurity tasks from Opus 5.5 to the older Opus 4.8. Google gives vetted defenders in its Fairwind program a separate, more permissive Argon variant. Developers migrating to Argon should note some breaking changes: older thinking-budget settings and the minimal thinking level return errors on Vertex AI, sampling settings such as temperature are ignored, and Google recommends timeouts of at least 15 minutes for long reasoning calls.

Verdict: Which Model Should You Choose?

Choose Gemini 4 Argon for long-document analysis, research across huge archives, video understanding, legal and financial agent workflows, and any job that needs extremely long output. Its introductory price makes it especially attractive before December 1. Choose Claude Opus 5.5 for terminal-heavy coding, infrastructure work and agent tasks where code must run correctly the first time; it also ranks higher on independent composite scores. Teams with mixed workloads may get the best results by routing reading-heavy jobs to Argon and build-heavy jobs to Opus 5.5. For a wider view of how tight the generative AI model race has become, and how both stack up against cheaper options in our GPT-6 Luna vs Gemini 3.8 Flash vs Claude Opus 5.5 comparison, see our related coverage.

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

Sources: Google – Introducing Gemini 4 Argon, Google – Gemini 4 Argon, Anthropic – Claude Opus 5.5, Computing for Geeks – Gemini 4 Argon, Computing for Geeks – Claude Opus 5.5, DeepLearning.AI – The Batch

Written by Alius Noreika