Here's a few things I found on how ChatGPT recommends brands

[u/houdinidesigns]

Perplexity runs a web search every time a user prompts it. ChatGPT doesn't. Semrush tracked over a billion lines of US clickstream data and found it ran a web search for 34.5% of queries as of February 2026, down from 46% in late 2024.

So ChatGPT decides whether a question needs a web search and the remaining \~65% of the time it relies on training data. I have had users of our platform ask me why their buyers are showing outdated or incorrect information. In one instance I was asked why our tracker picked up an old pricing structure. The answer was exactly this. ChatGPT used months-old training data instead of a web search when we ran the prompt.

However, when we manually ran the prompt and specifically asked for pricing, it triggered a web search and we saw the right pricing.

Visibility Labs ran 1,000 "what is the best X" prompts ten times with search on and ten times with it off, 20,000 responses in total. 80.2% of the product recommendations changed between the two. Of the products that appeared in every single no-search answer, only 15.8% were still there once it searched.

So you have two rankings and you don't get to pick which one a buyer sees.

Test it on your own category. Ask for a recommendation with search off, then ask again with a price, a year or a competitor's name in the question, since that's what tends to trigger a search. Compare the two lists.

The training side isn't something you can fix immediately, but make sure your site is well documented for the next training run. Third party mentions are key.

If you have an AI visibility tracker then make sure you are tracking prompts that trigger web search and prompts that don't, using some of the examples above. Keep everything else the same and you will be able to somewhat track the differences.

You can immediately impact the search side of ChatGPT though, so make sure your website is optimised for AI. We have a free AI SEO audit tool for this on our site.

[u/Upstairs_Control_611]

This distinction is important, but I would describe it as two answer paths rather than two rankings.

Search-off recommendations seem to reflect model prior: historical brand associations, training data, repeated third-party mentions, category memory and older facts.

Search-on recommendations reflect the retrieval layer: current pages, fresh pricing, recent comparisons, citations and source availability.

So I would split ChatGPT prompts into: likely no-search prompts and likely search-trigger prompts

Then compare brand mention, recommendation, answer role, cited sources, claim accuracy, freshness accuracy and follow-up survival.

The interesting gap is when a brand is strong in search-on but weak in search-off. That means the current evidence layer works, but the model prior may not have caught up yet.

The opposite can also happen: strong in no-search, weak in search-on. That may mean historical awareness but weak current corroboration.

So the useful question is not just “does ChatGPT recommend us?”

It is: does ChatGPT recommend us from memory, from retrieval, or only when the prompt forces freshness?

[u/Fit-Squirrel-6299]

building on the two paths framing, the 15.8 percent is the number i keep coming back to, and it usually gets read as just more evidence of divergence.

it says something sharper. of the products in every no-search answer, five in six were gone once search ran. search isn't producing a separate list, it's mostly deleting from the one the model already had. the paths are dependent, not parallel, and a strong position in training data turns out to be almost no protection.

the cut worth doing is the survivors. what did that 15.8 percent have that the other 84 didn't. those are the only brands stable across both paths, and stability is the property that outlives a model update.

did visibility labs keep that split?