ChatGPT Astra vs Sol vs Luna

Astra, Sol, Luna. OpenAI’s new model names sound like planets, and picking one is just as confusing. (New to the tool? Start with our guide on what ChatGPT is.) We skipped the spec sheets and tried something more useful. We gave five versions of ChatGPT Astra, Sol and Luna the same messy business problem and scored every answer. The winner wasn’t the biggest model, and it barely touched our usage limit. 

What Is ChatGPT Astra?

ChatGPT Astra (GPT-6 Astra) is OpenAI’s most powerful GPT-6 model. It is built for hard, multi-step work like deep analysis, research and complex planning. It has two siblings:

• Sol is the balanced middle model. It is strong, but cheaper and lighter to run. GPT-6.1 Sol is its latest version.
• Luna is the fast, low-cost model for quick, simple tasks.

In ChatGPT, Astra is mainly for Pro, Business and Enterprise users. Plus users get limited Astra access inside Work and Codex. Free and Go users get Luna.

We Tested 5 ChatGPT Models on One Business Problem: Here's Which One Won

Here’s the exact prompt we gave every model: 

Why We Ran This Test

GPT-6.1 Sol Medium gave the best answer and it used only 1% of our 5-hour limit. That was the big surprise of this test.

New ChatGPT models keep coming out and every one claims to be smarter. We wanted to see the difference for ourselves. So we gave five models the exact same business problem and compared them side by side.

For each model, we tracked three things:

• Quality: how good and correct the answer was
• Speed: how long it took to finish
• Usage: how much of our plan limit it used

The Test: A Small Online Store in Trouble

Every model got the story of "Kaveri Home", a small online home-goods store in India. In one month, three things went wrong:

• Customer complaints went up by 40%
• Sales went down by 25%, even though more people visited the website
• The support team took 12 hours to reply to customers

We also gave the models real numbers to work with: website visits, orders, complaint types and team size. Plus a list of changes the store made that month, like a new payment system and a cheaper courier company.

Why this test is tricky

We hid a few traps in the data to see which models actually think:

1. The real problem is hidden. The same share of visitors added items to their cart. So people still wanted to buy. The orders were lost at the payment step.
2. A fake clue. Complaints about damaged items went down. A careless model might call this good news, but it is not important.
3. Two wrong opinions. The marketer said the influencer campaign was a success because traffic went up. The founder said prices were too high and wanted a 20% discount. A good model should check both claims against the numbers.

The models also had to suggest 5 ranked solutions, a 7-day action plan within a ₹1,50,000 budget and explain how AI tools could help.

The Five Models We Tested

We ran each model in a fresh chat with the same prompt:

• GPT-6 Luna on High effort
• GPT-6.1 Sol on Light effort
• GPT-6.1 Sol on Medium effort
• GPT-6 Astra on Light effort
• GPT-6 Astra on Medium effort

To judge quality, we used a 100-point scorecard. It checked whether the maths was right, whether the model found the hidden problem, how it handled the traps, how realistic the plan was and whether it followed our instructions.

The Final Ranking

Sol Medium came first with 94 out of 100, but the gap between all five models was small: just 12 points from first to last.

One good sign: none of the five models made a single maths mistake.

Speed and Usage: The Price of Each Answer

The best model was not the most expensive one. Sol Medium used just 1% of our 5-hour limit, while Astra Light used 4% and still scored lower. Surprisingly, Astra Light used more than Astra Medium in our test.

In simple words: the Sol models gave the most quality for the least usage. The Astra models cost more without giving clearly better answers.

Each Model, Explained Simply

1. GPT-6.1 Sol Medium: the practical planner

Sol Medium thought like a real manager. Most models assumed support staff work all 8 hours on tickets. Sol Medium said that is not realistic and used 6 working hours instead. Then it worked out exactly how many tickets the team could handle and set its targets from that. It also kept ₹10,000 aside as backup money. The only downside: it took 3 minutes, the longest of all.

2. GPT-6 Astra Medium: the deep thinker

Astra Medium found ideas no other model found. It showed that complaints per 100 orders almost doubled, from about 35 to 65. It also noticed a hidden cycle: cutting costs (like the cheaper courier) leads to worse service, which leads to more cost-cutting. And it warned that auto-reply messages should not count as real replies to customers. Its weak spot was being less sure about the main reason sales fell.

3. GPT-6 Astra Light: the careful one

Astra Light handled the fake clue best. It explained that fewer damaged-item complaints does not mean better quality, because there were also fewer orders. Its plan was solid, but it set aside ₹40,000 for a shipping test, which is a lot for a small store. It also used the most of our plan limit.

4. GPT-6.1 Sol Light: good, but a bit rough

Sol Light did clear maths on the founder's discount idea. It showed that after a 20% price cut, the store would need 25% more orders just to earn the same money. It also made a good point that hiring more staff alone will not fix support. But its main solutions table came out broken, and it did not clearly say that the checkout step was where sales were lost.

5. GPT-6 Luna High: fast but shallow

Luna High finished in under a minute and barely touched our usage limit. Its answer was correct and clean. But it went less deep than the others. It skipped the discount maths, and it planned to spend the whole ₹1,50,000 budget with nothing kept for surprises.

What Every Model Missed

Not one model found the single most powerful number in the data.

In September, about 1 in 3 shoppers who added items to their cart went on to buy. In October, only 1 in 5 did. If October had kept September's rate, the store would have got around 1,800 orders. That is even more than September's 1,440. This proves the visitors were fine and wanted to buy. The checkout was broken.

All five models talked about the drop after the cart step, but none of them did this simple sum.

They also played it very safe. Almost every target came with words like "not a forecast" or "proposed cap". That is honest, but a busy store owner usually wants a clearer answer.

Which Model Should You Use?

For most people, GPT-6.1 Sol Medium is the best pick. Here is a quick guide:

How Much Does It Cost to Use Astra, Sol and Luna?

You don't get access to every ChatGPT model with every plan. The model you can use depends on your subscription.

Here's a quick look at what each plan offers:

Which plan should you choose?

If you mainly use ChatGPT for everyday questions, Free or Go should be enough. Plus gives you access to Sol for more demanding tasks, while Pro is the better option if you regularly need Astra for complex work. Business plans are designed for teams.

One thing we noticed during our testing is that Astra uses more of your usage limit than Sol and Luna. This isn't surprising because Astra is OpenAI's more expensive model to run. Its API pricing is $10 per million input tokens and $50 per million output tokens. Sol costs $2/$10 and Luna just $0.10/$0.50 per million tokens.

In our test, Astra also consumed more of our limit without delivering a clearly better overall result than Sol.

These are US prices. Actual prices may vary by country. See our ChatGPT pricing plans guide for a complete breakdown.

Final Thoughts

The most expensive model is not always the best. In our test, GPT-6.1 Sol Medium gave the smartest, most practical answer while using very little of our plan. If you only try one new model, start there.

Have you tested these models yourself? We would love to hear if your results match ours.

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