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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThere is no evidence-based overall winner. In Google’s comparison, Gemini 4 Argon leads several knowledge-work, coding, long-context, video and cybersecurity benchmarks; GPT-6 Astra and Claude Opus 5.5 lead other specific tests. The best fit depends on the work you need done, whether you can access the exact model, and what your prompts, tools and token usage will cost. As of 3 October 2026, Argon’s rollout had begun with trusted cyber defenders, while broader access was announced as a plan rather than established as generally available.
Which model fits each kind of work?
The names matter: this comparison concerns Google’s Gemini 4 Argon, OpenAI’s GPT-6 Astra, and two Anthropic models—Claude Fable 5.1 and Claude Opus 5.5. “Claude” is not one model, and results for one version should not be applied to another. The figures below are those published by Google DeepMind as of 3 October 2026. They are useful for finding models to test, not proof that one will perform best on your particular prompts.
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| Task or benchmark | Reported result | What it suggests |
|---|---|---|
| Knowledge work: Vals Index | Argon 68.9% | Argon leads the listed comparison. |
| Finance: Vals Finance Agent v2 | Argon 65.4% | Argon leads the listed comparison. |
| Legal: Harvey’s Legal Agent Benchmark | Argon 19.6% | Argon leads the listed comparison; the score is specific to this benchmark. |
| Automation: AutomationBench | Argon 51.3% | Argon leads the listed comparison. |
| Software engineering: DeepSWE v1.1 | Argon 77.9% | Argon leads the listed comparison. |
| Software engineering: FrontierSWE v2 | Astra 65.5% | Astra leads the listed comparison. |
| Terminal-based coding: Terminal-bench 4.0 | Opus 5.5 66.4% | Opus 5.5 leads the listed comparison. |
| Code generation: Vibe Code Bench | Argon 91.9% | Argon leads the listed comparison. |
| ML engineering: PostTrainBench | Opus 5.5 49.3%; Argon 45.3% | Opus 5.5 leads this test. |
| Science: Terminal-Bench Science 0.1 | Astra 68.1% | Astra leads the listed comparison. |
| Science: LABBench 2 | Argon 88.8% | Argon leads the listed comparison. |
| Math: RiemannBench | Argon 76.0% | Argon leads the listed comparison. |
| Long-context retrieval: GraphWalks through 128k | Argon 99.7% | Argon leads the listed comparison for this tested range. |
| Long-context retrieval: GraphWalks, 256k–1M subset | Argon 84.2% | A result for the benchmark’s longer-context subset, not a guarantee for every long-document task. |
| Video: LVBench | Argon 91.7% | Argon leads the listed comparison. |
| Computer use: OSWorld-2.0 offline partial score | Astra 72.6%; Argon 69.2% | Astra leads this reported score. |
| Computer use: Agent’s Last Exam | Argon 39.5%; Astra 34.2% | Argon leads among the reported scores; Anthropic results are unavailable for this row. |
| Defensive cybersecurity: CWE-bench v1 | Argon 68.0%; Astra 68.0%; Opus 5.5 67.0%; Fable 5.1 58.0% | Argon and Astra tie in the listed results. |
These are different tests of different capabilities, not interchangeable measures of general intelligence. A lead on a finance-agent benchmark does not establish that a model is best for legal drafting, and strong video results do not establish quality on every video workflow. For knowledge work, selected coding tasks, long-context retrieval and video, Google’s published results make Argon a candidate worth evaluating. If your workload resembles FrontierSWE v2, Terminal-bench 4.0, PostTrainBench, Terminal-Bench Science 0.1 or OSWorld-2.0, the corresponding Astra or Opus result is a reason to include that model in your trial.
How much confidence should you put in these scores?
Google’s comparison is vendor-presented, and its methodology is mixed rather than a single, independently administered head-to-head evaluation. Google says Argon results are generally pass@1, use the highest Gemini API thinking settings, and average multiple trials on smaller benchmarks. For non-Gemini models, it generally relies on provider-reported results unless noted. Its comparison also draws on public leaderboards, provider system cards, internal calculations and different benchmark harnesses and limits. Google says, for example, that it calculated Argon’s DeepSWE result and Terminal-bench 4.0 result, computed GraphWalks across the compared models, and that LVBench frame counts differ because of API limitations.
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Those differences do not make the scores useless, but they limit how confidently you can interpret small gaps. A benchmark is a screening tool: it can identify a plausible candidate, not predict the result on your data, instructions, integrations or safety requirements. Run a small evaluation using representative tasks and the tools you plan to deploy. Compare accuracy and failure modes as well as latency, consistency and the amount of human correction required.
Access is a practical part of the comparison
Model quality only matters if you can use the model in your intended product or application. The release and access details below reflect announcements available as of 3 October 2026; rollout, regional availability and plan eligibility can change.
- Gemini 4 Argon: Google announced it on 30 September 2026, initially rolling it out to trusted cyber defenders through its Fairwind program. Google said broader access for developers, enterprises and consumers would follow, beginning with paid API customers and Google AI Ultra subscribers. The announcement does not establish that every customer or region had access by 3 October.
- GPT-6 Astra: OpenAI said Astra was rolling out through paid ChatGPT plans and its API, with availability through Azure and AWS Bedrock also announced. Check the relevant service for the model’s current access in your region.
- Claude Fable 5.1: Anthropic described Fable 5.1 as generally available for coding and knowledge work, with API access. Its announcement also described access through Claude and developer or cloud platforms.
- Claude Opus 5.5: Anthropic described Opus 5.5 as available through paid Claude plans and developer or cloud platforms for coding and professional work.
Cybersecurity access deserves particular care: Google described Argon’s initial release as intended for trusted defenders, not as an unrestricted public tool. If your work involves vulnerability discovery or other sensitive security tasks, confirm the provider’s current approval, usage policy and access route before choosing a model.
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API prices below are vendor-published rates per million tokens, not a direct estimate of the total cost of running an application. They do not make workloads directly comparable unless you account for input and output volume, caching, reasoning settings and any application-level charges. Prices and introductory terms are volatile; verify them with the provider before committing.
| Model | Published API token rates in the cited announcement or documentation | Important qualification |
|---|---|---|
| Gemini 4 Argon | Introductory: $2 per million input tokens and $10 per million output tokens. Listed after the introductory period: $4 input and $20 output. Cached input: 95% off the input price. | Google’s 30 September 2026 launch announcement described the first rates as introductory; it did not make them a permanent price. |
| GPT-6 Astra | $10 per million input tokens and $50 per million output tokens. | OpenAI lists higher rates for prompts exceeding 272k input tokens. |
| Claude Fable 5.1 | $10 per million input tokens and $50 per million output tokens; cache reads $0.25 per million tokens. | Anthropic estimated typical workload costs about 25% below Fable 5, and up to about 45% lower for complex coding or highly agentic workloads. These are vendor estimates, not a guarantee for an individual deployment. |
| Claude Opus 5.5 | $4 per million input tokens and $20 per million output tokens. | Anthropic said it costs about 40% less to run than Opus 5 for typical token-billed workloads. This is a vendor comparison, not a quote for every workload. |
For a cost-sensitive choice, estimate a representative month of usage rather than comparing headline input rates alone. Include output tokens, cache hit rates and long prompts, then run the same workload through the models you can actually access. A model with a higher rate may still be less expensive for your application if it needs fewer retries or less human rework; that depends on results you measure, not the rate card by itself.
Context windows and output limits can change the fit
OpenAI’s GPT-6 Astra API documentation lists a 1,050,000-token context window and a maximum output of 128,000 tokens. Its listed knowledge cutoff is 30 April 2026. Those specifications may matter for applications that need to pass a large amount of material in one request, but a large context limit does not ensure that every relevant detail will be used accurately.
The figures available for this comparison do not establish matching context-window and maximum-output specifications for Argon, Fable 5.1 and Opus 5.5. Do not infer those limits from a benchmark score or from a different model in the same product family. Check the specific model’s current API documentation and test the longest request your workflow will require.
A practical way to choose
- Define the task. Separate the work into what the model must do: for example, edit code in a terminal, summarize long documents, operate a desktop interface, analyze video or assist with defensive security.
- Shortlist from relevant evidence. Use the benchmark rows that resemble your task, but treat Google’s comparison as a starting point rather than an independent overall ranking. Include the specific model variants you can access.
- Check product fit before testing. Confirm the current rollout, plan or API access, region, context and output limits, and any safety restrictions relevant to your use case.
- Test with representative inputs. Use the same prompts, data and available tools for each candidate. Score correctness, completeness, tool use, failures and the amount of correction needed.
- Calculate the workload cost. Apply current rates to your measured input, output and cache usage, and include retries and human review where they materially affect the total.
This process is more useful than picking the model with the largest headline score: it ties the choice to your own task and to the version, access route and cost you will actually use.
Quick Recap
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