OpenAI's GPT-6 family arrived in two steps. GPT-6 Astra came first in early September 2026, and GPT-6 Sol and GPT-6 Luna followed on 22 September. Three names, three price points and a lot of benchmark charts can make the choice feel harder than it needs to be. Here is a practical way to think about which one to use.
The three tiers at a glance
Astra is the most capable model. It is built for the hardest problems: complex reasoning with competing constraints, difficult debugging, and sensitive agent work where the model operates software on your behalf.
Sol is the balanced middle option. It handles coding, document analysis and multi-step agent tasks well, at a much lower price than Astra.
Luna is the cheapest and fastest. It is built for high-volume, simpler work such as classification, extraction, summarisation and first-line customer answers.
All three share the same very large context window of roughly 1.05 million tokens and can produce up to 128,000 tokens of output, according to OpenAI's published specifications as reported by developer sites. That means even the cheapest model can read very long documents.
The price gap is huge
Published API list prices show how differently the three are positioned. Astra is listed at $10 per million input tokens and $50 per million output tokens. Sol is listed at $2 and $10. Luna is listed at $0.10 and $0.50.
That makes Luna roughly 100 times cheaper than Astra and 20 times cheaper than Sol. For a business running millions of requests, picking the right tier can be the difference between an affordable product and an expensive one. Prices change, so always confirm current rates on OpenAI's official pricing page before you budget.
How much better is Astra really?
The capability gap depends on the type of task. For simple jobs, such as summarising an email or pulling a date out of a document, all three perform well and the cheaper models are hard to beat on value.
The gap widens as tasks become more autonomous and complex. On OSWorld 2.0, a test of how well a model can operate a computer, reported scores are about 73.5 percent for Astra, 64.4 percent for Sol and 52.7 percent for Luna. That spread matters when a model has to click through software, recover from errors and complete long multi-step tasks without help.
A practical decision guide
Choose Luna when the task is simple, repetitive and high volume. Examples include tagging support tickets, extracting fields from invoices, summarising short documents, translating standard messages, and answering common customer questions. Some developers report that Luna at a high reasoning setting can match Sol at a low setting on certain tasks while costing less, so it is worth testing Luna first.
Choose Sol for most professional work. Writing and editing reports, analysing long documents, everyday coding, research summaries and agent workflows of moderate complexity all suit Sol. For many teams, Sol will be the default.
Choose Astra when quality is critical and the problem is genuinely hard. Examples include diagnosing a production outage, untangling a complex codebase, high-stakes analysis where mistakes are costly, and long autonomous tasks that need careful judgement.
The smart approach: routing
Many companies do not pick one model. They route requests to different models based on difficulty. A simple classifier, sometimes running on Luna itself, decides whether a request is easy, medium or hard and sends it to Luna, Sol or Astra accordingly.
Routing keeps costs low for the majority of requests while reserving the expensive model for the small share of tasks that need it. Even a rough routing system can cut costs dramatically without users noticing any drop in quality.
What about ChatGPT users?
If you use ChatGPT rather than the API, the choice is partly made for you. Reports from early October 2026 say paid ChatGPT users get GPT-6 Sol in the main chat, while free and Go users began receiving GPT-6 Luna from 8 October. Paid plans may also offer access to more capable options, depending on the plan. Check the model selector in your ChatGPT app to see what is available to you.
Knowledge cutoffs
Each model has a knowledge cutoff, the date after which it has no built-in information. Reports list Astra's cutoff as 30 April 2026, Sol's as 20 April 2026 and Luna's as 18 May 2026. For anything that happened after those dates, turn on web search or provide the information yourself.
How to test before committing
Benchmarks are useful, but your own work is what matters. A simple test takes an afternoon:
- Collect 20 to 50 real examples of the task you want to automate.
- Write down what a good answer looks like for each.
- Run all three models on the same examples.
- Compare accuracy, speed and cost per request.
- Pick the cheapest model that meets your quality bar.
You may be surprised how often the cheaper model is good enough.
A note for Indian startups
For Indian startups and developers, where budgets are often tight, the GPT-6 tiers open useful options. Luna's very low price makes high-volume features, such as customer support in multiple languages or processing large numbers of documents, much more affordable. Sol offers strong general capability at a moderate price. Astra can be reserved for the few tasks where nothing else will do.
The bottom line
There is no single best GPT-6 model. Astra is the most capable, Sol is the sensible default for professional work, and Luna is the cost-efficient workhorse. Match the model to the task, test on your own data, and consider routing requests between tiers. That approach gets you most of GPT-6's power at a fraction of the cost.
