I'll be honest: the first time I saw three model names with a sun, a moon and a star in them, I assumed it was marketing fluff and I'd just pick the biggest one. That was a mistake. A few evenings of reading the launch posts, the pricing pages and the independent benchmark write-ups changed my mind completely.
One quick note before we start: I have not run my own lab benchmarks on these models. Everything below comes from OpenAI's announcements, the model documentation and third-party testing that I cross-checked. Where a number is OpenAI's own claim, I say so. If you want a number you can trust for your work, you'll need to test it on your own prompts. I'll show you how at the end.
OpenAI's Tri-Model Strategy for the GPT-6 Generation
GPT-6 is not one model. It's a family with three tiers.
GPT-6 Astra came first, in early September 2026, and sits at the top. GPT-6 Sol and GPT-6 Luna followed on September 22, 2026. OpenAI says Sol and Luna were trained with methods similar to Astra, so a lot of Astra's gains in reasoning, factual accuracy, coding, computer use and alignment were carried into faster, cheaper models. You can read the details in OpenAI's Sol and Luna announcement and the GPT-6 Astra launch post.
Something that confused me at first: the older GPT-5.6 line had three models (Sol, Terra and Luna). The GPT-6 lineup so far skips Terra, so it's just Astra, Sol and Luna.
Here's the quick version of how they differ:
| Feature / Model | GPT-6 Astra (Flagship) | GPT-6 Sol (Balanced) | GPT-6 Luna (Efficient / High Volume) |
|---|---|---|---|
| Primary role | Most capable model for demanding, large-scale projects | Complex work such as coding and professional tasks | High-volume tasks with a clear goal |
| Ideal for | Hardest coding and computer-use jobs, big projects | Software development, automation, agent workflows | Summarizing documents, extracting information, quick answers |
| API model ID | gpt-6-astra |
gpt-6-sol |
gpt-6-luna |
| API price (per 1M tokens) | Check OpenAI's pricing page | $2 input / $10 output | $0.10 input / $0.50 output |
| Reasoning effort "none" | Not supported | Supported | Supported |
| Speed and context window | See the model page | See the model page | See the model page |
I left speed and context window pointing to the model pages on purpose. Those figures get updated, and I'd rather send you to the source than print a number that goes stale in a month.
Astra: the all-rounder at the top. It's the one OpenAI calls its best model across the board, including computer use.
Sol ("Sun"): the workhorse in the middle. Smart enough for complex coding and professional work, priced far below the flagship.
Luna ("Moon"): the budget option for the repetitive, high-volume jobs where you'd otherwise burn money on a bigger model.
GPT-6 Astra: The Multimodal Enterprise Workhorse
Astra is the model you reach for when a task is big, messy and expensive to get wrong. Think a large codebase refactor, a long multi-step agent run, or a computer-use workflow where the model has to click through real software.
OpenAI describes Astra as its most capable model and says it remains the best model for using a computer. Both of those are OpenAI's own claims, so treat them as a starting point rather than gospel.
Who should actually use it?
- Teams running long, high-stakes agent tasks where one wrong step wastes an hour
- Developers doing complex, multi-file engineering work
- Anyone who tried Sol or Luna first and hit a ceiling
If your task is simple, Astra is overkill. That's the mistake I see people make most often: they use the flagship for everything, then wonder why the bill looks scary.
GPT-6 Sol: The Balanced Model for Serious Daily Work
Sol is the one I'd call the sweet spot for most people.
OpenAI says Sol makes about half as many mistakes as the previous-generation Sol on its internal factuality evaluation, which is built from real conversations where users flagged errors. That's a vendor-reported figure, but it matches the direction of the independent write-ups I read.
Some benchmark numbers worth knowing, all from OpenAI's launch material unless noted:
- AutomationBench (multi-app business workflows): Sol at "xhigh" effort scored 33.2%, ahead of Astra at low effort (30.3%) and Claude Opus 5 at max (26.9%), according to OpenAI.
- OSWorld 2.0 offline (computer use): Sol at xhigh scored 60.5%, roughly level with Opus 5 at medium (60.3%), at around 80% lower cost per task, according to OpenAI.
Notice the pattern. Sol isn't claiming to beat Astra everywhere. It's claiming to get you most of the way there for a fraction of the price. For a lot of real jobs, that trade is a bargain.
An unexpected result worth remembering
Independent testing by ComputingForGeeks found that Sol at "max" effort (32.0%) actually scored lower than Sol at "xhigh" (33.2%) on AutomationBench, while costing more. The same thing happened with Luna: xhigh (12.6%) scored below high (14.5%).
Lesson: cranking the effort dial to maximum is not automatically better. More thinking can mean more cost with no gain, or even a slightly worse result.
GPT-6 Luna: Cheap, Fast Capacity for High-Volume Work
Luna is the cheapest of the three, at $0.10 per million input tokens and $0.50 per million output tokens. OpenAI positions it for clerical, high-volume work: summarizing documents, pulling information out of text, answering quick questions.
It's not a toy, though. OpenAI says Luna at max effort beats GPT-5.6 Sol at medium effort for about a tenth of the cost. Independent testing from Artificial Analysis, as reported by ComputingForGeeks, put the cost of running its Intelligence Index at roughly $0.07 per task on Luna (max) versus $0.18 on the previous Luna.
There's a trade-off to be aware of. The same independent write-up noted that Luna lost a couple of points on its overall index versus GPT-5.6 Luna, with weaker scores on some coding evaluations. So if you're doing hard software engineering, don't assume the cheapest model will hold up. Test it first.
Good fits for Luna:
- Bulk summarization of support tickets or meeting notes
- Data extraction from invoices, emails or forms
- First-pass classification and tagging
- Quick Q&A bots where speed and price matter more than depth
Free and Go users can also use Luna in the ChatGPT desktop app, which makes it an easy way to try the family at no cost.
Speed, Cost, and Accuracy Matrix: Choosing the Right Variant
Here's how I'd decide, in plain steps.
Step 1: Start with the cheapest model that might work. Begin with Luna. If the output is good enough, you're done and you've saved money.
Step 2: Move up only when you can name the failure. "It feels dumb" isn't a reason. "It missed the second requirement in a four-step instruction" is. That tells you whether Sol will fix it.
Step 3: Reserve Astra for the hard 10%. Long agent runs, tricky computer-use flows and large engineering tasks are where the flagship earns its price.
Step 4: Test your effort setting. Try medium, high and xhigh on 20 of your own real prompts. Don't just assume max is best.
| If your job is... | Start with | Why |
|---|---|---|
| Summaries, extraction, quick answers at scale | Luna | Lowest cost, built for high-volume tasks |
| Everyday coding, automation, professional work | Sol | Strong results at a mid-range price |
| Large, high-stakes, multi-step projects | Astra | OpenAI's most capable model |
| Computer-use agents | Astra, then test Sol | Astra leads, Sol is much cheaper |
Where you can use them
All three are available in the API as gpt-6-astra, gpt-6-sol and gpt-6-luna, and in Codex through a ChatGPT plan that includes them. In the Codex CLI you can pick one with the -m flag, for example codex -m gpt-6-sol. Sol and Luna are in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu plans.
Common mistakes to avoid
- Using Astra for everything. You'll overspend on tasks Luna could handle.
- Trusting vendor charts blindly. Benchmark numbers are useful, but run your own prompts.
- Always picking max effort. As shown above, it can cost more without improving results.
- Ignoring model names in your code. Hard-code the model ID in one config file so you can switch in a minute.
- Forgetting that pricing and limits change. Check the official pages before you budget.
FAQs
What is the difference between GPT-6 Astra, Sol and Luna?
Astra is OpenAI's flagship and most capable GPT-6 model. Sol is a balanced, lower-cost model for complex work like coding and automation. Luna is the cheapest, aimed at high-volume tasks such as summarizing and extracting information.
Which GPT-6 model is the cheapest?
GPT-6 Luna. It costs $0.10 per million input tokens and $0.50 per million output tokens. Sol costs $2 input and $10 output per million tokens.
When were GPT-6 Astra, Sol and Luna released?
Astra launched in early September 2026 (September 3). Sol and Luna were released on September 22, 2026.
Is GPT-6 Sol better than GPT-6 Astra?
Not overall. OpenAI says Astra remains its best model. Sol can beat Astra at low effort on some benchmarks, such as AutomationBench, at a much lower cost, but Astra is still the stronger choice for the most demanding work.
What are the API model IDs for GPT-6?
gpt-6-astra, gpt-6-sol and gpt-6-luna.
Is there a GPT-6 Terra model?
Not at launch. The GPT-5.6 family had Sol, Terra and Luna, but the GPT-6 lineup so far lists only Astra, Sol and Luna.
Can I use GPT-6 Luna for free?
Free and Go users can use GPT-6 Luna in the ChatGPT desktop app, according to reports on the launch. Availability can change, so check OpenAI's current plan details.
Which GPT-6 model should I pick for coding?
Start with Sol for everyday coding. Move to Astra for large, complex engineering tasks. Test Luna for simple, repetitive code chores.
Does a higher reasoning effort always give better results?
No. In independent AutomationBench testing, Sol at max effort scored lower than at xhigh, and Luna at xhigh scored lower than at high. Test a few effort levels on your own tasks.
Final Thoughts
If I had to give one piece of advice, it would be this: stop thinking of GPT-6 as "pick the smartest model" and start thinking of it as "pick the cheapest model that passes my test."
Build a small set of prompts from your own work, run them through Luna, Sol and Astra, and compare. It takes an afternoon and it will save you real money. Explore all our in-depth guides in the OpenAI Ecosystem Hub, read our hands-on Claude Sonnet 5.5 vs. Sonnet 5 comparison, or check the full collection of technical teardowns in the vnhax Blog.
Sources: OpenAI: Introducing GPT-6 Sol and Luna, OpenAI: GPT-6 Astra, ComputingForGeeks: GPT-6 Sol and Luna pricing, benchmarks and real tests. Figures reflect information available as of October 2026 and may change.
