OpenAI’s current GPT-6 range offers three distinct starting points: Astra for demanding work, GPT-6.1 Sol for a balance of capability and cost, and Luna for focused requests at volume. The right choice depends on how much reasoning the task needs and how expensive an incorrect result would be to correct.
The product details and prices below were checked against official documentation on 19 October 2026. Our suggested trials are practical assessments, rather than results from a benchmark we have run.
What each model is positioned to do
GPT-6 Astra is OpenAI’s most capable model for demanding reasoning, coding, research and professional work. It supports text and image input, with text output.
GPT-6.1 Sol is positioned as a lower-cost option with performance close to Astra on complex work. Its documentation explicitly recommends comparing the two on your own tasks.
GPT-6 Luna is intended for focused, high-volume workloads. A useful trial would be a narrowly defined extraction or classification task with an answer that can be checked automatically.
Put the published prices in context
These are standard API text rates in US dollars per million tokens, within the base prompt-pricing tier. They are not ChatGPT subscription prices or rand quotes.
| Model | Input | Output |
|---|---|---|
| GPT-6 Astra | US$10.00 | US$50.00 |
| GPT-6.1 Sol | US$2.00 | US$10.00 |
| GPT-6 Luna | US$0.10 | US$0.50 |
The individual model pages linked above are the sources for these rates. Caching, long prompts, service tiers, tools and regional processing can change the bill. Check the relevant conditions before estimating a production workload.
For an illustrative workload of 10 million uncached input tokens and 2 million billed output tokens, the base text calculation would be US$200 for Astra, US$40 for Sol and US$2 for Luna. That example excludes tools and other adjustments, and assumes the same token totals for each model. Real tasks may use different totals or need retries.
Keep the dollar estimate separate from your rand budget. Record the exchange rate and conversion costs used when the budget is approved, rather than treating a dollar price as a fixed local amount.
Compare outcomes, not just rates
Take a workflow such as reading a supplier quote and producing a structured summary. Use sample documents with missing information, inconsistent dates and totals that need checking. Write an expected answer for each case.
Measure whether each model returns the right fields, identifies uncertainty and stays within the requested scope. Then record response time, billed usage and the time a person spends correcting the result. A low token rate is only a saving if the output is suitable for the next step.
For a software task, include the existing behaviour that must remain intact. Review the diff and test the customer journey, rather than judging only whether the generated code compiles.
Route requests with a clear fallback
If a focused model handles routine requests well, define which cases should go to a stronger model or a person. Use explicit signals, such as missing required fields or a failed validation. Do not make a model’s confident wording the only escalation rule.
Before switching an existing integration, read OpenAI’s GPT-6 guidance. Confirm the supported settings and tools for the exact model and endpoint your application uses. Keep the previous configuration available until the new version has passed your acceptance checks.