Will a new AI model release change what it says about my brand?

99Visibility Research · Updated October 21, 2026 · 5 min read

Short answer

Yes, it can. A new model brings a new knowledge cutoff, and AI apps route questions between models with different cutoffs. The same buyer question can get a different answer about your brand with nothing on your site changed. Whether the app searches the web also varies per query. Re-baseline after every major release, and record which model and search state produced each answer.

Key takeaways

A model release changes the knowledge cutoff, so answers given without web search can shift overnight.
Inside one app, routing between models can change the cutoff by months, as with ChatGPT's Instant and reasoning paths.
Answers that use live web search depend on access and retrieval, which a model swap can also change.
A score taken before a release is not directly comparable with one taken after, so mark the break.
Record the model, the date and whether search was on for every answer you save.

Why a new model changes your brand answers

An AI answer about your brand comes from one of two places. Either the model answers from what it learned in training, or it searches the web and builds the answer from pages it retrieves.

A new model changes the first source directly. Each model has a knowledge cutoff, a date after which it learned nothing. A newer model may know about your rebrand, new pricing or acquisition. An older one may not. Swap the model and the training-based answer can change.

A new model can also change the second source. Models decide when to search and rewrite your question into their own search queries. A different model may search more often, less often, or with different queries. That changes which pages it reads.

None of this needs an edit on your site. So if your brand answers moved last week, check the model release calendar before you check your CMS. This also differs from switching models yourself: here the vendor makes the switch and often doesn't tell you.

When answers shift, and when they should hold

It helps to separate the four stages an answer passes through.

Parametric: what the model knows with no web access. A new model resets this completely. Nobody can change it this quarter.
Access: whether AI crawlers can fetch your pages. This is yours, and a model release doesn't change it.
Retrieval: whether your page enters the model's context for a given question. The operator controls this, and a new model can change it.
Generation: which retrieved pages the model uses and cites. A new model can weigh the same sources differently.

So expect the biggest swings in answers given without search, and in which sources get cited. Expect stability in the facts your own pages state clearly, as long as crawlers can reach them. If a search-backed answer gets your pricing wrong after a release, the model may be reading a different page. If a no-search answer gets it wrong, the model may simply predate the change. Our guide to hallucination vs outdated answers helps you tell which.

What the evidence shows about cutoffs and routing

OpenAI's model documentation, verified in August 2026, lists a 16 February 2026 knowledge cutoff for the GPT-5.6 family. The Instant path behind the chat-latest alias carries a 31 August 2025 cutoff, about five and a half months older. OpenAI also warns that the snapshot will be regularly updated.

That means "ChatGPT" is not one knowledge state. Secondary reports, as of September 2026, put Free and Go users on gpt-5.6-luna and Plus users on gpt-5.6-sol. OpenAI hasn't confirmed that mapping itself. Plan, routing and date all decide which cutoff answered.

Google is split too. As of September 2026, the Gemini app added gemini-3.6-flash to its model menu in July 2026. The Gemini API released gemini-3.8-flash in September 2026. Google AI Mode has defaulted to gemini-3.5-flash since May 2026. Google publishes only "Gemini 3" for AI Overviews. Google's AI features documentation says AI Overviews and AI Mode "may use different models and techniques."

Search is per query on every platform. OpenAI says ChatGPT search rewrites your question into targeted queries. Google's Gemini API documentation says the model generates and runs its own search queries. A 2026 critical survey of 45 GEO studies found no technique with a stable, cross-platform causal effect on discoverability. Model churn is one reason a stable effect is hard to find.

Edge cases that look like a model change

Not every shift is a new model. Rule these out before you blame a release.

Search switched on or off. The same model can answer from memory one day and from the web the next. If you can't see citations, you don't know which happened.
Personal context. OpenAI says ChatGPT may use saved memories when rewriting a search query. Test logged out or with ChatGPT memory off.
Your own access broke. A CDN or firewall rule can block an AI crawler that robots.txt allows. That looks like an answer change but is a site change.
Citation is not use. Research on attribution (arXiv:2606.28358) shows citation output can be changed without changing the sources. A new citation pattern doesn't prove the model reads you differently.
Small samples. A handful of answers per platform swings easily. Don't read a trend into three questions.

How to re-baseline after a major release

You can't stop vendors shipping models. You can make sure you notice, and that your history stays honest.

Log the model with every answer. Save the platform, model name if shown, plan tier, date, and whether citations appeared.
Watch release notes. When a platform changes its default model, treat that date as a break in your data.
Re-run your core questions within a week. Use the same buyer questions, logged out, so the comparison is clean.
Split the results by search state. Compare answers with citations separately from answers without them.
Mark old scores as pre-release. Don't draw one trend line across a model change without a note.
Fix what you control. If the new answer is wrong, check that the correct fact sits in your HTML before JavaScript runs, and that AI crawlers can reach the page.

For a quick snapshot after a release, the free checker on this site shows how AI answers up to three buyer questions about you. For ongoing records across platforms, see our guide to LLM visibility tracking.

Frequently asked questions

Can I get a new model to learn my latest facts?

Not directly. What a model learned in training is fixed at its cutoff, and nobody outside the vendor controls it. What you can control is the search side. Keep the correct fact stated plainly on a page AI crawlers can fetch, in HTML that loads without JavaScript. That gives search-backed answers a sentence to quote. Our page on [how long AI takes to update](/questions/how-long-ai-update-brand-info) covers timing.

Is my old AI visibility score still valid after a model release?

It was valid for the model that produced it. It isn't directly comparable with a score from a different model. Treat the release date as a break in the series. Compare like with like: same questions, same platform, same search state. If your tool records which model answered, filter on that before reading any trend.

Why do I get different answers from the same model on different days?

Search is decided per question, so the model may answer from training one day and from the web the next. ChatGPT also rewrites your question into its own search queries, which can return different pages. Memory and custom instructions can change the rewrite. Test logged out and save whether citations appeared, so you can tell these cases apart.

Should I track every model each platform offers?

Track what most of your buyers actually see. That usually means the default model on the free and most common paid tiers. Record the model name each time, because defaults change. If your buyers skew toward paid plans, add those models too. More models means more data, so keep the question set fixed to stay comparable.

Sources

This page is AI-assisted. It was written from a verified evidence base and checked against our editorial policy. Engine behaviour changes often; facts are as of October 21, 2026.