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There is no single benchmark winner in Google’s comparison of Gemini 4 Argon, GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5. Argon leads on some knowledge-work, coding, long-context, and multimodal measures; GPT-6 Astra and Claude Opus 5.5 lead on other coding, science, computer-use, and model-engineering tests. Choose based on the specific work, whether you can access the model, its total cost, and results from a small trial using your own tasks.
What Gemini 4 Argon is—and whether you can use it
Google announced Gemini 4 Argon on September 30, 2026, positioning it for complex software engineering, enterprise knowledge work such as legal and finance work, and cybersecurity defense. At announcement, access was initially rolling out to a set of trusted cyber defenders through the Fairwind Program. Google said it planned to expand access in phases, beginning with paid API customers and Google AI Ultra subscribers, then to developers, enterprises, and consumers. The announcement gave no firm date for those broader releases, so check current availability for your region and account before choosing a workflow.
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Google described the phased release as a way to gather early-tester feedback and strengthen safeguards. That matters especially for security tasks: Argon is intended to be capable in cyber defense, and access was limited while Google continued work on its guardrails.
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The table below reproduces selected results from Google’s published comparison. They are vendor-reported scores, not independently reproduced head-to-head results. Google says Argon’s results are generally pass@1 runs through the Gemini API at its highest thinking settings; values for other models generally come from their providers and use maximum thinking or reasoning settings where available. The underlying results draw on different sources, evaluation harnesses, and setups.
#1 Best Overall
| Model | Google-reported result | What the result indicates |
|---|---|---|
| Gemini 4 Argon | 68.9% on Vals Index | A result in the comparison’s knowledge-work evaluations. |
| Gemini 4 Argon | 77.9% on DeepSWE v1.1 | A result in the comparison’s agentic coding evaluations. |
| Gemini 4 Argon | 84.2% on GraphWalks for the 256K-to-1M context subset | A result on a long-context evaluation; it applies to that stated subset. |
| Gemini 4 Argon | 91.7% on LVBench | A result in the comparison’s video-understanding evaluations. |
| GPT-6 Astra | 65.5% on FrontierSWE v2; 68.1% on Terminal-Bench Science 0.1; 72.6% on OSWorld-2.0 | GPT-6 Astra leads the listed models on these respective coding, science, and computer-use tests. |
| Claude Opus 5.5 | 66.4% on Terminal-bench 4.0; 49.3% on PostTrainBench | Claude Opus 5.5 leads the listed models on these respective terminal-task and post-training evaluations. |
These scores are useful for identifying which tasks to test, not for declaring one model best overall. The evaluations measure different capabilities, and in some tests models do not share the same data or setup. Google’s table also includes missing values where results were unavailable. Keep each score attached to its benchmark and treat the comparison as Google’s account of results.
Choose by the work you need done
Long-horizon software engineering
Argon’s 77.9% DeepSWE v1.1 result gives it a strong showing on one agentic-coding measure, while GPT-6 Astra leads Google’s table on FrontierSWE v2 at 65.5%. Claude Opus 5.5 leads on Terminal-bench 4.0 at 66.4%. Because those tests are not interchangeable, shortlist models that you can access and try them on representative repository tasks: for example, a multi-file change, a failing test investigation, or a migration with explicit acceptance criteria. Measure whether the model completes the task correctly, how much review it requires, and whether it introduces regressions.
Research, drafting, and enterprise knowledge work
Google positions Argon for enterprise work, including legal and finance tasks, and its comparison reports 68.9% on Vals Index. That is evidence of a result on that evaluation, not a guarantee of accuracy in a particular organization’s documents or policies. Test with representative, permission-appropriate material and check factual claims, citations, calculations, and compliance with your internal rules. For consequential legal or financial decisions, use qualified human review rather than treating generated text as final advice.
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Google reports that Argon supports an output limit of 1 million tokens, up from 64K. An output limit describes how much the model can generate; it is not by itself a statement of how much input context the model can accept. The comparison separately reports 84.2% on GraphWalks for its 256K-to-1M context subset. If your work involves very long material, verify the current input and output limits for the exact product or API route you will use, then test whether the model can retrieve details reliably across the full document rather than merely produce a long response.
Video and multimodal work
Argon’s 91.7% LVBench result is a reason to include it in a trial for video-understanding tasks, but the score does not establish performance on every visual workload. Test with the kind of footage, image quality, duration, and questions you expect to use. Check whether answers identify relevant evidence and handle uncertainty instead of inferring details that are not visible.
Science, computer use, and model engineering
In Google’s table, GPT-6 Astra leads on Terminal-Bench Science 0.1 and OSWorld-2.0, while Claude Opus 5.5 leads on PostTrainBench. These are distinct evaluations: a science benchmark does not settle which model is best for all scientific work, and a computer-use score does not establish how well a model will operate your specific application. For tasks involving tools or software interfaces, include recovery from mistakes and correct completion—not just a successful first action—in your own evaluation.
Defensive cybersecurity
Google identifies cyber defense as one of Argon’s intended uses and began rollout with trusted cyber defenders. Access restrictions and safety controls are part of the product’s release context, not a reason to assume that any user can obtain it or use it for unrestricted security work. If you are evaluating it for a security team, confirm eligibility and permitted use, and keep testing within an authorized environment with appropriate human oversight.
Compare access and cost before committing
Google announced introductory Gemini API rates of $2 per million input tokens and $10 per million output tokens, with cached input tokens discounted 95% from the input rate. It said the rates would later be $4 per million input tokens and $20 per million output tokens. The announcement did not say when the introductory period ends. Treat those figures as announced rates, not a guarantee of the price currently available to your account; verify current pricing before budgeting.
For a fair cost comparison, estimate both input and output usage for the same representative workload, account for how much input can be served from cache, and include the cost of retries and human review. The current pricing, access terms, and specifications for OpenAI and Anthropic alternatives are not established here, so check their official documentation rather than inferring parity from benchmark results.
Run a small, repeatable evaluation
- Choose real tasks. Select a small set of representative prompts or jobs, including ordinary cases and the edge cases that create meaningful risk.
- Define success before testing. Record expected outputs, required evidence, acceptable error rates, review effort, and any failure that should disqualify a result.
- Use the same inputs and conditions. Where product settings allow, keep instructions and task materials consistent across models. Note model version, access route, settings, and date so results can be reproduced.
- Score the work, not the polish. Check correctness, completeness, tool use, citation or source quality where relevant, and how much editing or intervention was needed.
- Compare operational fit. Record availability for your account, latency, token use, total cost, and the consequences of errors in that task.
- Keep human review where it matters. For code, security, legal, finance, and other consequential work, set a review process appropriate to the risk rather than relying on a benchmark score.
Make the choice
Shortlist Argon if its reported strengths match your workload and you can access it; include GPT-6 Astra or Claude Opus 5.5 when their stronger results align with the particular coding, science, computer-use, or model-engineering task you need. Claude Fable 5.1 is also part of Google’s comparison, but the selected results above do not establish a lead for it on a named test. In every case, the practical winner is the model that is available to you and performs reliably on your own tasks at an acceptable total cost.
Quick Recap
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