The Tool Desk
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There is no well-supported overall winner among GPT-6 Astra, GPT-6.1 Sol, Gemini 4 Argon, and Claude Fable 5.1. Shortlist by the work you need done, your input and output sizes, API budget, and required tools or media. Astra is OpenAI’s top-end option for demanding work; Sol is worth evaluating when cost matters; Google reports Argon ahead of Astra on several named work and agent benchmarks; and Fable 5.1 offers a million-token context window but is described by Anthropic as slower. Those are starting points, not guarantees for your workload.
Which frontier model fits which job?
Use the providers’ descriptions and published comparisons to decide what to test, not to declare a universal champion. The table separates documented specifications from positioning so you can see where the available evidence is—and is not—comparable.
| Model | What its provider says or documents | Context and output | Documented API pricing | Practical qualification |
|---|---|---|---|---|
| GPT-6 Astra | OpenAI describes Astra as its most capable model for demanding work, including complex reasoning, coding, computer use, research, and document creation. OpenAI API documentation | 1,050,000-token context window; maximum output of 128,000 tokens. Text and image input are listed; audio and video are unsupported. OpenAI API documentation | $10 per million input tokens and $50 per million output tokens at standard short-context rates. Requests with more than 272,000 input tokens use higher rates for the full request. OpenAI API documentation | Consider it for difficult, varied work where its documented capabilities justify the cost; test it on your own tasks rather than assuming “most capable” means best for every job. |
| GPT-6.1 Sol | OpenAI positions Sol as near-Astra performance for complex work at lower cost. That is a reason to compare the models, not evidence that Sol matches Astra on every task. OpenAI model comparison | Not stated in the cited comparison and pricing material. OpenAI model comparison | Standard short-context rates are $2 per million input tokens and $10 per million output tokens; higher long-context rates also apply. OpenAI pricing | Evaluate it when API cost is a major constraint, including on tasks where you might otherwise choose Astra. |
| Gemini 4 Argon | Google DeepMind’s model page compares Argon with Astra and other models on work and agent benchmarks. Google DeepMind Gemini models | Not stated in the cited comparison page. Google DeepMind Gemini models | Not stated in the cited comparison page. Google DeepMind Gemini models | Worth testing for the specific knowledge-work, automation, finance, legal, or software-engineering tasks represented in Google’s comparison; its scores are vendor-published results. |
| Claude Fable 5.1 | Anthropic’s documentation lists adaptive thinking as always on and describes latency as slower. Anthropic Fable 5.1 documentation | 1-million-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 | Include it when its long-context capacity suits the task, but account for Anthropic’s slower-latency description in workflows where turnaround time matters. |
What the published benchmark comparisons show
Benchmark scores answer narrow questions under particular test setups; they do not establish a single ranking across coding, research, office work, or production use. Compare a score only with results for the same named benchmark, and note who published it.
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Google DeepMind’s model page reports the following figures for Gemini 4 Argon and GPT-6 Astra. These are Google-published results, not an independent verdict, and the page reviewed does not establish a publication date for this table.
#1 Best Overall
| 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 |
On these listed rows, Google reports Argon scoring higher than Astra. That supports adding Argon to a shortlist for related work, but does not tell you how either model will perform on your prompts, tools, data, or acceptance criteria.
OpenAI’s Astra–Fable results
OpenAI’s 2026 announcement reports these results for GPT-6 Astra and Claude Fable 5.1. The scores below are from OpenAI’s published evaluation, not a neutral matched assessment by an independent tester.
Rank #2
| Benchmark | GPT-6 Astra | Claude Fable 5.1 | Publisher |
|---|---|---|---|
| Terminal-Bench 4.0 | 57.9% | 55.8% | OpenAI, 2026; GPT-6 Astra announcement |
| GPQA Diamond | 96.0% | 93.7% | OpenAI, 2026; GPT-6 Astra announcement |
OpenAI says its evaluations were run in its research environment or through its API. It cautions that results may differ from production ChatGPT because system prompts and tools can vary. A score from one benchmark also should not be treated as a score for a different capability.
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How to choose for coding, research, or long documents
Start with the work and operating constraints, not the model name. A useful shortlist usually contains models that can handle your inputs and workflow at an acceptable cost and speed.
- Coding: Choose a representative coding task, including the language, repository size, test process, and tools you actually use. Terminal-Bench 4.0 is one relevant published comparison for Astra and Fable; DeepSWE v1.1 is a separate Google-published comparison for Argon and Astra. Their scores are not directly interchangeable.
- Research and knowledge work: Test how well each finalist finds and uses evidence, handles uncertainty, and produces a deliverable in your required format. Google’s Vals Index Knowledge Work result can inform a shortlist, but it does not replace a task-specific evaluation.
- Long documents: Match context capacity to the complete input you intend to send, then check how well the model retrieves details and follows instructions across that material. Astra and Fable 5.1 have documented context windows above one million tokens; the cited sources do not establish comparable context limits for Sol or Argon.
- Computer use or automation: Identify the exact tools, permissions, and success conditions involved. OpenAI lists computer use among Astra’s target work, while Google reports an AutomationBench comparison for Argon and Astra; neither fact by itself establishes compatibility with your environment.
- Finance or legal workflows: Treat Vals Finance Agent v2 and Harvey’s Legal Agent Benchmark as signals about those named evaluations, not as approval for consequential use. Validate factual accuracy, citations, privacy requirements, and human review in your own workflow.
- Images, audio, or video: Verify modality support before comparing quality. Astra’s cited API page lists image input and says audio and video are unsupported; equivalent details for the other models are not established by the cited materials here.
Estimate API cost using your real workload
For an initial estimate, multiply expected input tokens by the input rate and expected output tokens by the output rate, then add the two amounts. A million tokens at a listed rate of $10 per million costs $10 for that input or output category; actual bills depend on how much you send and generate, and the applicable pricing tier.
The documented short-context prices make Sol materially cheaper per token than Astra or Fable 5.1, but a lower token price does not guarantee a lower total cost if a model needs more tokens, retries, or tool calls to complete the job. Astra requests above 272,000 input tokens are charged at higher rates for the entire request, and Sol has separate higher long-context rates. The cited material does not give the exact long-context rates for either model, so use the providers’ current pricing pages for a quote rather than extrapolating from the short-context figures.
Before committing, calculate a typical request and a heavy-use case from your own logs or expected prompt sizes. Include output length and repeated calls, and compare latency and task success alongside the price. Provider specifications and prices can change.
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Run a fair evaluation before switching
- Define the job and pass criteria. Choose a small set of representative tasks—such as a code change with tests, a research brief with source requirements, or a long-document question set—and decide in advance what counts as correct, complete, and usable.
- Shortlist models that meet hard constraints. Check modality, context needs, output size, API availability, tool compatibility, budget, and acceptable response time. Discard a model that cannot support a required input or workflow.
- Use the same task conditions. Give each finalist equivalent prompts, source material, tools, and time limits. Record whether the model needed retries or human correction; otherwise a seemingly successful answer may hide extra cost or effort.
- Score outputs against the criteria. For factual work, check claims against sources. For coding, run the same tests. For automation, verify the intended actions and failure handling. Include latency and total token cost in the comparison.
- Recheck provider details before deployment. Confirm the current model documentation, pricing tier, limits, and enabled tools for the API or product you will actually use. Published benchmark conditions may differ from your production setup.
The reviewed vendor materials do not provide an independent, matched test of all four models. Your own controlled comparison is the strongest basis for choosing among them.
Quick Recap
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