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GPT-6 Astra posts leading results on some of OpenAI’s published reasoning and computer-use tests, but it does not win every comparison—and those scores do not establish that it is the best model for every kind of work. OpenAI’s September 3, 2026 comparison shows a mixed picture: Astra scores 96.0% on GPQA Diamond and 95.0% on ARC-AGI-2, while Claude Fable 5.1 scores higher on Humanity’s Last Exam with tools. For day-to-day selection, weigh the task’s difficulty, time and budget against model availability; an expensive, high-reasoning option is not automatically the right choice for routine work.
What the published comparisons can—and cannot—tell you
The available numbers are from OpenAI’s September 3, 2026 launch comparison and related OpenAI materials. They are vendor-published results, not a comprehensive independent ranking or a hands-on evaluation. OpenAI says its displayed benchmark scores are the maximum achieved at any reasoning effort. It also says GPT evaluations were run in a research environment or through its API, which may differ slightly from production ChatGPT because of system prompts and available tools.
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That context matters. A benchmark score describes performance on a particular test under stated conditions; it is not a direct forecast of how a model will perform on every user’s prompts, software setup, or workflow. The comparison includes GPT-5.6 Sol, Claude Fable 5.1, Claude Fable 5, Claude Opus 5, and Gemini 3.8 Flash, but not every model has a reported score for every benchmark. A blank result should be read as unreported in that table, not as a zero.
There is also a version distinction to keep in view: OpenAI’s benchmark table compares Astra with GPT-5.6 Sol, while its current model-selection guidance recommends GPT-6.1 Sol as a lower-cost alternative. Those are not interchangeable labels, and the published figures for GPT-5.6 Sol should not be treated as test results for GPT-6.1 Sol.
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Reasoning performance depends on the test
OpenAI’s results put Astra near the top of several listed evaluations, but different tests reward different capabilities. The graduate-level science, tool-assisted exam, and abstract reasoning scores do not point to one model that leads in every category.
| Evaluation | GPT-6 Astra | Other results reported by OpenAI | What to take from it |
|---|---|---|---|
| GPQA Diamond | 96.0% | Gemini 3.8 Flash: 95.3%; GPT-5.6 Sol: 94.6%; Claude Fable 5.1: 93.7%; Claude Opus 5: 93.7% | Astra leads the scores shown for this graduate-level science question test. |
| Humanity’s Last Exam with tools | 57.2% | Claude Fable 5.1: 65.0%; other listed models: not reported in OpenAI’s comparison | On this tool-assisted evaluation, Fable 5.1 scores higher than Astra. |
| ARC-AGI-2 | 95.0% | GPT-5.6 Sol: 92.5%; Claude Fable 5.1: 90.0%; Claude Fable 5: 89.2%; Claude Opus 5: 90.4%; Gemini 3.8 Flash: not reported in the comparison | Astra leads the reported scores in this comparison; the result does not establish performance on every form of reasoning. |
All percentages above are from OpenAI’s September 3, 2026 launch comparison, whose displayed scores represent the maximum at any reasoning effort. OpenAI did not publish a result for every model on every row in that comparison. In particular, Astra’s 57.2% on Humanity’s Last Exam with tools is not evidence that it would trail Fable 5.1 on unrelated work; it is a result for that evaluation.
When the science and abstract-reasoning scores matter
GPQA Diamond is relevant when comparing performance on difficult scientific questions, while ARC-AGI-2 offers a different view of reasoning performance. Astra’s 96.0% and 95.0% results are strong scores on those specific tests. If your work resembles those tests, they are useful evidence for considering Astra. They do not show that it will be more accurate than another model on your own research, nor do they measure the quality of every explanation or the amount of checking your task requires.
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Why the tool-assisted result is a useful counterexample
Humanity’s Last Exam with tools gives a concrete reason not to collapse the table into a single winner. OpenAI reports 57.2% for Astra and 65.0% for Claude Fable 5.1, while leaving other listed model results unreported. The result demonstrates that model ordering can change with the evaluation. It does not, by itself, identify why the scores differ or predict which model will work better with a particular person’s tools.
Computer use: score and task time are separate measures
For computer-use tasks, OpenAI reports an OSWorld 2.0 score of 72.6% for Astra, compared with 65.7% for GPT-5.6 Sol. Its latency simulations put Astra at roughly 40 minutes per task and Sol at roughly 75 minutes per task. These figures are OpenAI’s reported evaluation results; they are not a promise that a real task will take those times on a user’s computer.
OpenAI also reports that Astra completed tasks 1.9 times faster than GPT-5.6 Sol on Mind2Web. That speed comparison combines Astra with OpenAI’s updated Codex harness. The harness qualification is important: it is not an isolated measurement of the model alone, and the claim should not be generalized to every tool, workflow, or everyday interaction.
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Read the computer-use evidence as two related but distinct signals. OSWorld 2.0 reports a score, while the latency simulations report approximate time in those simulations; a higher score does not automatically mean a faster result in a different setup. For users automating a sequence of computer tasks, the published comparison makes Astra worth considering, but the actual fit depends on the tools and environment involved.
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OpenAI’s model-selection guidance recommends GPT-6 Astra for the most demanding reasoning, coding, and professional work; GPT-6.1 Sol for near-Astra performance at lower cost; and GPT-6 Luna for fast, cost-effective focused work. These are OpenAI’s recommendations, not independent endorsements. They are a practical starting point for matching capability and expense to a task rather than defaulting to the most capable option.
| Work pattern | OpenAI’s suggested model | Decision point |
|---|---|---|
| Especially demanding reasoning, coding, or professional work | GPT-6 Astra | Consider it when the task’s difficulty justifies the higher-cost, high-capability option. |
| Work needing near-Astra performance at lower cost | GPT-6.1 Sol | OpenAI positions it as a value-oriented alternative; the cited launch benchmark figures for GPT-5.6 Sol do not establish GPT-6.1 Sol’s scores. |
| Focused tasks where speed and cost matter | GPT-6 Luna | Use the task’s complexity to decide whether a fast, cost-effective model is sufficient. |
For everyday tasks, such as a short rewrite, a straightforward summary, or an answer that does not require extensive reasoning, the useful question is not “Which model is strongest overall?” but “What does this task need?” If it is routine and time-sensitive, OpenAI’s guidance points toward a faster, lower-cost choice. If it is consequential, complex, or difficult to verify, Astra may be more appropriate. That is a selection framework, not a claim that one model will always be faster, cheaper, or more accurate for every prompt.
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OpenAI’s Help Center says model availability varies by account and workspace. If a suggested model does not appear in your interface, its absence does not mean it is unavailable to everyone; access depends on the account or workspace. The same guidance cautions that higher reasoning effort can use more of an allowance without always producing a better result. Choose effort and model based on the task, rather than assuming the highest setting is automatically best.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.API specifications, token costs, and context
For developers, the API listing distinguishes three practical constraints: how much text can fit in a request, how much can be returned in one response, and the price of input and output tokens. The current Astra API page, reviewed in 2026, lists the following text specifications and prices:
| API detail | GPT-6 Astra listing | How to interpret it |
|---|---|---|
| Context window | 1,050,000 tokens | The listed context capacity; it is not the maximum response length. |
| Maximum output | 128,000 tokens | The listed maximum for generated output. |
| Knowledge cutoff | April 30, 2026 | Do not assume the model’s built-in knowledge includes later events. |
| Input text | $10 per million tokens | API token pricing, not a consumer subscription fee. |
| Cached input text | $1 per million tokens | The listed cached-input rate; it is distinct from ordinary input pricing. |
| Output text | $50 per million tokens | API token pricing, not a consumer subscription fee. |
These figures are the values listed on OpenAI’s API page at the time reviewed in 2026; API prices and specifications can change. Check the live API listing before budgeting or building an integration. The token prices apply to API use and should not be confused with the cost of a ChatGPT subscription.
A large context window can help when an application needs to provide a substantial amount of material in one request, but it does not guarantee better reasoning or remove the need to manage what is sent. The output limit is a separate ceiling on generated text. For an application that handles current information, the April 30, 2026 cutoff also means that built-in model knowledge alone is not a reliable substitute for an up-to-date source.
Safety findings and monitorability
OpenAI’s safety overview reports two findings that should be considered together rather than reduced to a simple “safe” or “unsafe” label. First, OpenAI says Astra has reached its Critical cybersecurity capability threshold. That is a capability-threshold statement, not a guarantee that every use is harmful or benign.
Second, OpenAI reports that a simulation involving more than 54,000 internal Codex tasks produced roughly half as many higher-severity misalignment flags for Astra as for GPT-5.6 Sol. That result describes the stated simulation and its flags; it is not a universal rate for every deployment or a direct measure of all safety risks.
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A practical way to make the comparison
Use the published evidence as a filter, then evaluate the actual task and constraints. The following sequence keeps benchmark results, account availability, and operating costs in their proper roles:
- Define the task. Decide whether it is routine and focused, or demanding enough to justify a higher-capability reasoning model. For computer use, identify the software environment and actions the system must perform.
- Use relevant evidence, not a single headline score. For graduate-level science questions, compare the GPQA Diamond results; for abstract reasoning, look at ARC-AGI-2; for tool-assisted exam performance, note the different Humanity’s Last Exam result. Treat each as evidence about its own test.
- Check access in your account or workspace. OpenAI says availability varies, so confirm that the model is actually offered in the environment where you plan to use it.
- Match effort to need. Higher reasoning effort can consume more allowance without necessarily improving an answer. Use it when the task warrants it, not as a universal default.
- For API use, calculate the relevant cost. Distinguish ordinary input, cached input, and output tokens, and verify the current rate and limits on the API listing before committing to a budget.
- For consequential work, plan for verification and oversight. Benchmarks and safety simulations do not replace checking outputs against authoritative sources or assessing the risks of the specific deployment.
Verdict: a strong specialist option, not a universal winner
OpenAI’s published comparison supports considering GPT-6 Astra for demanding reasoning, professional work, and computer-use tasks: it leads the reported GPQA Diamond and ARC-AGI-2 results, and its OSWorld 2.0 score and simulated task time compare favorably with GPT-5.6 Sol. But Claude Fable 5.1 scores higher on Humanity’s Last Exam with tools, model access varies, and the available comparisons are vendor-published rather than an independent overall leaderboard. Choose on task fit, speed, budget, and the evidence that actually applies—not on a single benchmark or the assumption that the most capable model is the right choice for every everyday task.
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