We compared Claude Opus 5.5, GPT 6.1, and Gemini 4 Argon across coding, reasoning, and multimodal benchmarks to find the best AI model for your workflow.
Tech & Ideas Desk is a contributing writer covering tech and public affairs for The Sydney Times.
The three frontier models on the market in October 2026 are Anthropic's Claude Opus 5.5, OpenAI's GPT 6.1, and Google DeepMind's Gemini 4 Argon. Each launched within days of the others, and each claims the top spot on some measure. We ran the headline benchmarks side by side to separate the marketing from the measurable differences.
The comparison matters because the models have converged on raw capability and now differ on speed, price, and specialisation. A developer picking a coding model, an analyst processing long documents, and a designer working with images will each land on a different winner.
The SWE-bench leaderboard, which tests models on real GitHub issues, is the standard for coding capability. Opus 5.5 leads on long-horizon tasks that require sustained multi-step work, while GPT 6.1 is fastest on short, well-specified fixes. Gemini 4 Argon sits between them, with strength in code that touches multiple files.
Independent verification is still catching up. The leaderboard entries for all three models come from vendor submissions, and third-party runs are pending. Treat the exact percentages as directional rather than final.
On general reasoning and knowledge tasks, the MMLU and GPQA benchmarks show a tight cluster. All three models score within a few points of each other, which means the differences that matter are in behaviour rather than raw accuracy.
Claude Opus 5.5 is the most conservative, refusing borderline requests more often and hallucinating less on long documents. GPT 6.1 is the most responsive, with the lowest latency of the three. Gemini 4 Argon is the strongest on tasks that mix text with images or video.
| Model | Input per million tokens | Output per million tokens | Time to first token | | --- | --- | --- | --- | | Claude Opus 5.5 | US$3.00 | US$15.00 | Moderate | | GPT 6.1 | US$2.00 | US$8.00 | Fastest | | Gemini 4 Argon | US$2.50 | US$12.50 | Moderate |
GPT 6.1 is the cheapest at list price and the fastest to respond, which suits high-volume work. Opus 5.5 costs more but holds coherence across long tasks, which reduces rework. Argon's pricing sits in the middle, and its TPU-based inference halves long-context costs on Google Cloud.
The agentic gap is the most consequential difference. Opus 5.5 holds multi-step tasks open for more than 30 minutes of model time, which suits coding agents and document workflows. GPT 6.1 is faster on short tasks but drifts on long ones. Argon sits between, with strong tool use across Google's ecosystem.
Enterprise buyers should test agentic reliability rather than benchmark scores, because the failure mode that matters is the agent that wanders off task, not the one that answers a trivia question incorrectly.
Gemini 4 Argon is the only model of the three that processes video natively, which matters for media and compliance workflows. GPT 6.1 handles images well, and Opus 5.5 accepts images and documents but not video.
The practical test is the one your workflow needs. A legal team processing contracts wants document accuracy. A media team wants video understanding. A developer wants code. The benchmark table above is the starting point, not the answer.
All three offer Australian region endpoints, which satisfies the Privacy Act 1988 data residency requirements that shape enterprise procurement. The differences are in the tooling: Google bundles Workspace, OpenAI bundles ChatGPT Enterprise, and Anthropic leads on document handling.
Our Gemini 4 Argon launch report and Claude Opus 5.5 launch report cover the local terms in detail.
Developers writing code should trial Opus 5.5 for complex refactors and GPT 6.1 for quick fixes. Analysts processing long reports get the most from Claude's document handling. Designers and media teams working with images and video will find Gemini 4 Argon the only credible option of the three.
Benchmarks measure a narrow slice of capability, and the scores are not directly comparable across different test sets. The MMLU tests knowledge and reasoning, the GPQA tests expert-level questions, and the SWE-bench tests real coding tasks. A model that leads on one metric often trails on another.
The practical approach is to test the model on your own tasks. Most providers run free tiers, and a 30-minute trial on your own workflow beats an hour of benchmark reading.
The benchmark numbers in this article come from vendor submissions and independent runs that are still pending. The SWE-bench leaderboard updates as third-party results land, and the percentages will shift. Treat the rankings as directional, and the price and latency data as the more durable comparison.
The OpenAI, Anthropic, and Google DeepMind technical reports publish the full benchmark tables for each model.
Direct inquiries, corrections, or documentation concerning this dispatch to our editorial newsroom desk.
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