Most brand visibility checks start the same way. Someone types the company name into ChatGPT, reads the answer, and either relaxes or panics. That check is nearly always run from a Plus account, because the person running it is a marketer who pays for the better version. Their customers mostly are not.
On 6 August OpenAI said it is removing limits on text chats and making GPT-5.6 Luna the default model for Free and Go accounts, replacing GPT-5.5 Instant. Free and Go users also get a Think button that routes harder questions through more reasoning. Plus and Pro accounts move to an updated GPT-5.6 Sol with a slider controlling how much thinking goes into an answer. Limits on files, images, voice and image generation stay in place. The announcement arrived shortly after ChatGPT passed a billion weekly users, with unlimited text chats for free accounts scheduled to land the following week.
The number worth holding onto is not the billion. It is the split.
Two tiers, two models, two answers about you
Until now, free accounts had a ceiling. Hit it and the conversation stopped, or dropped to a weaker fallback. That ceiling quietly rationed how many people used ChatGPT for the kind of open-ended question that mentions brands: which accounting tool suits a sole trader, who does commercial kitchen servicing near Leeds, what is the difference between two CRMs. Remove the ceiling and that behaviour has nowhere to stop.
So the model answering most brand questions is now GPT-5.6 Luna, and almost nobody in marketing has tested against it. OpenAI’s own evaluation puts factual errors 62% less common in Luna than in GPT-5.5 Instant, and 68% less common in Sol. Take those as directional rather than settled, since they are internal numbers with no published methodology. But the gap between the two figures is the point. Luna is better than what free users had. It is still not the model you are testing in.
A worked example. A regional B2B supplier checks how ChatGPT describes them, using the founder’s Plus account with the thinking slider left high. The answer is detailed, cites two trade publications, mentions a certification they hold. Reassuring. A buyer on a free account asks the same thing, gets a shorter reply from a different model, and the certification does not appear because the source it lives on was not retrieved. Nothing about the company changed between those two answers. Only the tier did.
What to actually do this week
Run the check twice, from two accounts, and write down which model produced each answer. That is the whole method. It takes about twenty minutes and it is the only way to see the gap.
Three questions are usually enough: your brand name alone, your main category with a location or qualifier attached, and a comparison question naming a competitor. Ask each in a fresh conversation. Log the date, the tier, the model, and whether any link was offered. Repeat monthly rather than daily, because the answers move around enough that a single day tells you very little.
If the free-tier answer is thinner, look at what the paid answer used that the free one did not. Usually it is a source that takes more retrieval effort to reach: a PDF, a page buried three clicks deep, a claim that only exists on a third-party directory. The fix is rarely clever. It is putting the fact somewhere shallower.
What can go wrong here
Three failure modes, and the first is the common one.
Treating a handful of answers as data. You are running a sample of two on one afternoon. Chatbot output varies between sessions for reasons that have nothing to do with your website. If you rewrite a page because of one bad answer, you will be rewriting it again next month. We covered the broader version of this problem in how AI describes your brand across many queries rather than one, and the same caution applies here.
Chasing model changes as a strategy. Default models will change again. Building a workflow around Luna specifically is building on something with a short shelf life. What lasts is the habit of checking both tiers, not the model name.
Assuming unlimited means used. Removing a cap raises the ceiling on usage. It does not prove your buyers were hitting that cap in the first place. If you sell into a market that researches through trade bodies, procurement portals or a rep they have known for years, none of this may show up in your pipeline for a long time.
When this is not worth your time
If your business runs on repeat customers and referrals, and search has never driven meaningful revenue, this is a twenty minute curiosity rather than a project. The teams that should take it seriously are the ones where a stranger comparing three options is a normal way to get discovered. For everyone else, knowing the gap exists is enough for now.
The uncomfortable part of this update is not the model change. It is that the AI answer most of your market sees has always been a different answer from the one you have been reading, and the tier that produces it just got a much bigger audience. Check both. Then decide whether the difference is worth fixing.
Editor’s note: This area changes quickly, so check the latest platform policy before making compliance decisions.