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There is no evidence here for one overall winner. Choose based on the work you need done, the tools and input types your workflow requires, and the cost of its typical prompts and responses. Astra is positioned by OpenAI for demanding work; Sol is a lower-priced option to evaluate; Google’s published comparisons make Argon worth testing for several agent-style tasks; and Fable 5.1 offers a million-token context window, with slower latency noted by Anthropic. For a consequential decision, test your finalists on the same representative tasks rather than treating a vendor’s benchmark table as a universal ranking.
What each model is documented to offer
The official pages establish some useful differences in context limits, output limits, pricing, and vendor positioning. They do not provide a neutral, matched evaluation of all four models, and the specifications below do not by themselves establish which will perform best on your workload.
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| Model | Context and maximum output | API pricing shown | Documented modality or operating note |
|---|---|---|---|
| GPT-6 Astra | 1,050,000-token context window; maximum output of 128,000 tokens. OpenAI API documentation | $10 per million input tokens and $50 per million output tokens at standard short-context rates. Requests above 272,000 input tokens are charged at higher rates for the full request. OpenAI API documentation | Text and image input; audio and video are listed as unsupported. OpenAI describes Astra as its most capable model for demanding work. OpenAI API documentation |
| GPT-6.1 Sol | Not stated in the cited model-comparison and pricing pages. OpenAI model comparison; OpenAI pricing | $2 per million input tokens and $10 per million output tokens at standard short-context rates; long-context rates are higher. OpenAI pricing | OpenAI positions Sol as near-Astra performance for complex work at lower cost. That positioning is not proof that Sol matches Astra on every task. OpenAI model comparison |
| Gemini 4 Argon | Not stated on the cited Gemini models page. Google DeepMind Gemini models | Not stated on the cited Gemini models page. Google DeepMind Gemini models | The cited page provides Google’s comparisons on selected knowledge-work and agent benchmarks; it does not establish a universal ranking. Google DeepMind Gemini models |
| Claude Fable 5.1 | 1,000,000-token context window; maximum output of 128,000 tokens. Anthropic Fable 5.1 documentation | $10 per million input tokens and $50 per million output tokens. Anthropic Fable 5.1 documentation | Anthropic describes its latency as slower and adaptive thinking as always on. Anthropic Fable 5.1 documentation |
“Not stated” means the cited page does not establish that specification; it should not be read as a claim that the model lacks the capability. Specifications, availability, and prices can change, so check the linked provider pages before committing to an implementation.
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Which model fits which job?
Demanding, varied work: shortlist Astra
OpenAI positions GPT-6 Astra for complex reasoning, coding, computer use, research, and document creation. That makes it a sensible candidate when a workflow spans several of those tasks or when its documented input and output limits matter. Product positioning is not a guarantee of success on a particular task: evaluate the actual workflow, including the tools it uses and the format of its inputs and outputs.
#1 Best Overall
Cost-sensitive complex work: test Sol against Astra
At the standard short-context rates shown on OpenAI’s pricing page, Sol costs one-fifth as much per input token and one-fifth as much per output token as Astra. OpenAI describes Sol as near Astra on complex work, but that claim does not show that the models are interchangeable for your use case. Compare answer quality and error rates on your own examples, then weigh any quality difference against the savings.
Agent-style finance, legal, or automation work: include Argon in a trial
Google’s model page reports Argon ahead of Astra on its listed Vals Finance Agent v2, Harvey’s Legal Agent Benchmark, and AutomationBench results. Those figures are Google-published comparisons, not independent confirmation that Argon will work better in your organization. They are a reason to include Argon in a matched trial if these tasks are relevant, not a reason to skip that trial.
Rank #2
Long documents: compare context limits with task quality
Astra’s documented context window is 1,050,000 tokens, while Fable 5.1’s is 1,000,000. Both list a maximum output of 128,000 tokens. A large context window may help when a task requires substantial source material in one request, but the limit alone does not show how accurately a model finds, reconciles, or cites details across a long document. Sol’s and Argon’s context limits are not stated in the cited pages, so confirm them with the provider if they are decisive for your workflow.
Coding: compare like with like, then run your own tasks
OpenAI reports Astra’s result on Terminal-Bench 4.0, while Google reports Argon’s result on DeepSWE v1.1. These are different benchmarks; their scores cannot be compared as though they were measurements on a shared scale. If coding is your priority, test the same repository tasks, tool setup, and success criteria on your shortlisted models.
What the published benchmark scores do—and do not—show
The figures below are useful as evidence of what each company reports on named evaluations. A score applies to that benchmark and its evaluation setup; it is not a general measure of model quality.
OpenAI’s published Astra and Fable results
| Benchmark | GPT-6 Astra | Claude Fable 5.1 | Publisher and qualification |
|---|---|---|---|
| Terminal-Bench 4.0 | 57.9% | 55.8% | OpenAI, in its 2026 announcement. OpenAI GPT-6 Astra announcement |
| GPQA Diamond | 96.0% | 93.7% | OpenAI, in its 2026 announcement. OpenAI GPT-6 Astra announcement |
OpenAI says its evaluations ran in its research environment or through its API, and notes that results may differ from production ChatGPT because system prompts and tools can differ. The two rows also measure different things: a coding benchmark result is not interchangeable with a science-question benchmark result.
Google’s published Argon and Astra comparisons
| Benchmark | Gemini 4 Argon | GPT-6 Astra | Publisher |
|---|---|---|---|
| Vals Index Knowledge Work | 68.9% | 63.1% | Google DeepMind. Gemini models page |
| AutomationBench | 51.3% | 41.4% | Google DeepMind. Gemini models page |
| Vals Finance Agent v2 | 65.4% | 53.5% | Google DeepMind. Gemini models page |
| Harvey’s Legal Agent Benchmark | 19.6% | 5.4% | Google DeepMind. Gemini models page |
| DeepSWE v1.1 | 77.9% | 74.1% | Google DeepMind. Gemini models page |
These are results published by Google, and the cited Gemini page does not state its publication date. Treat them as vendor-reported evidence for those listed evaluations, not as an independent verdict or a guarantee on a reader’s workload. They should not be blended with OpenAI’s figures above to create a single cross-company leaderboard.
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How to estimate API cost for your workload
For a simple estimate, multiply the input tokens in a typical request by the per-token input rate, multiply the expected output tokens by the per-token output rate, and add the two amounts. At the standard short-context rates shown on OpenAI’s pages, a request with 100,000 input tokens and 10,000 output tokens would cost $1.50 with Astra or $0.30 with Sol, before any other charges or pricing conditions. This is arithmetic using the listed rates, not a measured workload cost.
That illustration does not apply to every request: Astra charges higher rates for the full request when input exceeds 272,000 tokens, and Sol has separate, higher long-context rates. The cited materials do not give the Sol long-context rates here. For Fable 5.1 and Argon, use the linked provider information to verify the applicable API prices and any conditions before estimating spend.
Quick Recap
A practical way to choose
- Define the task. Specify whether you need coding, long-document analysis, computer or browser use, finance or legal workflows, research, or general reasoning. Write down what a successful result must contain.
- Check hard requirements. Confirm that the model accepts your input type, can fit the required source material within its context limit, and can return an output of the needed size. For Astra, the cited API page explicitly lists text and image input, but not audio or video.
- Shortlist on relevant evidence. Compare results only when they come from the same named benchmark and note which company published them. Consider provider positioning as a reason to test a model, not as proof of task-level performance.
- Estimate cost at realistic volumes. Use representative input and output sizes, and check whether long-context pricing applies. Recheck the provider’s current rates before deployment.
- Run a matched trial. Give each finalist the same representative tasks, tools, instructions, and success criteria. Include cases where the model must say it lacks evidence, not just easy examples.
- Choose on the full result. Compare task quality, errors, latency, tool and modality fit, context needs, and the resulting cost. Keep the test set and criteria aligned with the work the model will actually do.
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.
