There is no overall winner in the published comparison. Google’s Gemini 4 Argon leads several reported knowledge-work, coding, long-context, video, and cybersecurity benchmark rows; OpenAI’s GPT-6 Astra and Anthropic’s Claude Opus 5.5 lead other specific tests. Pick by the task you need to do, the version and access route you can actually use, and the cost of your own workload—not by treating unlike benchmark scores as a single ranking.
Where does each model look strongest?
The table below summarizes results published by Google DeepMind as of 3 October 2026. The scores are task-specific: a percentage on one benchmark cannot be compared directly with a percentage on another.
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| Task | Reported result | What it suggests |
|---|---|---|
| Knowledge work | Argon: 68.9% on Vals Index, 65.4% on Vals Finance Agent v2, 19.6% on Harvey’s Legal Agent Benchmark, and 51.3% on AutomationBench. | Argon leads the listed rows in Google’s table. Treat these as signals for those evaluation tasks, not a guarantee of quality in your documents or business process. |
| Agentic coding | Argon: 77.9% on DeepSWE v1.1 and 91.9% on Vibe Code Bench. Astra: 65.5% on FrontierSWE v2. Opus 5.5: 66.4% on Terminal-bench 4.0. | Argon leads two listed coding tests, while Astra and Opus 5.5 each lead a different one. Choose the test closest to your coding workflow. |
| ML engineering | Opus 5.5: 49.3% on PostTrainBench; Argon: 45.3%. | Opus 5.5 leads this listed evaluation. |
| Science and math | Astra: 68.1% on Terminal-Bench Science 0.1. Argon: 88.8% on LABBench 2 and 76.0% on RiemannBench. | Leadership changes with the task and benchmark; these scores do not establish a single best science model. |
| Long-context and video | Argon: 99.7% on GraphWalks through 128k, 84.2% on its 256k-to-1M subset, and 91.7% on LVBench. | These results make Argon worth evaluating for long-context or video work, but do not promise equal performance on every long document or video workflow. |
| Computer use | Astra: 72.6% on the listed OSWorld-2.0 offline partial score; Argon: 69.2%. Argon: 39.5% on Agent’s Last Exam; Astra: 34.2%. Anthropic results are unavailable for these rows. | Astra leads the listed OSWorld score; Argon leads Agent’s Last Exam. The missing Anthropic values prevent a three-way comparison on these rows. |
| Defensive cybersecurity | Argon and Astra: 68.0% on CWE-bench v1; Opus 5.5: 67.0%; Fable 5.1: 58.0%. | Argon and Astra tie on this reported benchmark. The score does not determine whether a model is accessible for a given security workflow. |
How much confidence should you put in the benchmark table?
Use it to shortlist models, not to declare a universal champion. Google’s evaluation-methodology document says Argon’s results are generally pass@1 at the highest Gemini API thinking settings, with multiple trials averaged for smaller benchmarks. Its comparisons combine different sources and setups: Google calculations, public leaderboards, provider system cards, internal calculations, and, for many non-Gemini results, figures reported by the providers themselves. Harnesses, limits, and benchmark conditions vary. Google also notes frame-count differences on LVBench because of API limitations.
That makes the table useful as a map of promising candidates, but not a fully controlled independent head-to-head study. A model’s result on a benchmark may not predict how it handles your prompts, tools, latency requirements, or failure conditions. Before committing to a paid workflow, test a representative set of your own tasks and evaluate the completed work, not just whether the model produced an answer.
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Which model should you try for your task?
- Business, finance, legal, or automation workflows: Start with Argon if its access route is available to you; it leads the listed Vals, Harvey’s Legal Agent, and AutomationBench rows. Validate it on your own material, including the review and error-handling steps your process requires.
- Software development: Consider Argon for DeepSWE- or Vibe Code Bench-like work, Astra for tasks resembling FrontierSWE v2, and Opus 5.5 for Terminal-bench 4.0-like work. Try your own repository, test suite, and tool setup before choosing.
- ML engineering: Opus 5.5 is the listed PostTrainBench leader; Argon also scores strongly on that row. Compare them on the specific training or engineering tasks you need rather than assuming the benchmark transfers to every ML workflow.
- Science and math: Match the model to the kind of problem you have. Astra leads the listed Terminal-Bench Science 0.1 row; Argon leads the listed LABBench 2 and RiemannBench rows.
- Long documents or video: Argon’s GraphWalks and LVBench results make it a candidate to test. Check the product or API limits that apply to your access route, and test representative materials; the benchmark scores alone do not establish your usable context or output limits.
- Computer interaction: Astra leads the listed OSWorld-2.0 offline partial score, while Argon leads Agent’s Last Exam. Compare the actions and environments in your actual workflow, and account for the absence of Anthropic results in these rows.
- Defensive security: The CWE-bench v1 scores are close, with Argon and Astra tied. Availability and safety restrictions matter as much as the benchmark: Argon’s launch was initially limited to trusted cyber defenders.
Can you access the models, and what do their published API prices say?
Availability and prices below reflect announcements and documentation current on 3 October 2026. They can change, and API rates do not capture every cost, such as tool charges or application-level fees.
| Model | Access described by its provider | Published API token rates |
|---|---|---|
| Gemini 4 Argon | At its 30 September 2026 announcement, Google said it was first rolling out through its Fairwind program to trusted cyber defenders. Broader access for developers, enterprises, and consumers was planned to follow, starting with paid API customers and Google AI Ultra subscribers. The announcement does not establish that every planned route was available to every user by 3 October. | Google announced introductory rates of $2 per million input tokens and $10 per million output tokens, followed by $4 and $20 respectively after the introductory period. Cached input was listed at 95% off the input price. Confirm which rates and access routes currently apply. |
| GPT-6 Astra | OpenAI said Astra was rolling out through paid ChatGPT plans and its API, Azure, and AWS Bedrock. | OpenAI’s API documentation lists $10 per million input tokens and $50 per million output tokens. It lists higher rates for prompts above 272k input tokens; the rate amounts are not stated here. The documentation lists a 1,050,000-token context window and a 128,000-token maximum output. |
| Claude Fable 5.1 | Anthropic describes Fable 5.1 as generally available, with API access. | Anthropic lists $10 per million input tokens and $50 per million output tokens, and $0.25 per million tokens for cache reads. Anthropic estimates typical workloads cost about 25% less than Fable 5, and complex coding or highly agentic workloads up to about 45% less; these are provider estimates. |
| Claude Opus 5.5 | Anthropic describes Opus 5.5 as available through paid Claude plans and developer and cloud platforms. | Anthropic’s announcement lists $4 per million input tokens and $20 per million output tokens. It says typical token-billed workloads cost about 40% less to run than Opus 5; this is a provider estimate. |
GPT-6 Astra’s published API documentation also gives a 30 April 2026 knowledge cutoff. The listed context and output limits apply to Astra; the Argon benchmark results above are not a statement of Argon’s product context window.
How should you compare real workload cost?
Per-token rates are only one input. Estimate the input and output tokens for your workload, then account for caching, reasoning settings, retries, and any tools or platform fees. The Argon introductory rate is time-limited, and Anthropic’s cost-reduction percentages compare specific models and typical workloads; neither should be treated as a universal bill estimate. For a fair comparison, run the same representative tasks through the access routes you expect to use and compare both completed-work quality and total cost.
Quick Recap
A practical way to make the choice
- Define the job. Specify what a successful result looks like, what tools the model may use, how much context it needs, and which mistakes would be costly.
- Shortlist from relevant evidence. Use the benchmark rows above only when they resemble your task, and keep each benchmark attached to its own model result.
- Check that you can use the exact model. Confirm current availability, plan or API access, regional terms, limits, and any restrictions that affect your workflow.
- Run a small, consistent evaluation. Use the same prompts, files, tools, and success criteria for each candidate. Include difficult cases and failures, not only easy examples.
- Compare the full trade-off. Consider output quality, latency, reliability, usable context, safety controls, and cost for your actual token and cache pattern.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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