Last updated September 2026.
Nothing on your site changed on Aug. 26, 2026. ChatGPT changed anyway. OpenAI retired o3 that day, and GPT-5.6 became the only model answering a default ChatGPT session.
That kind of shift is a default model swap: a change to which model answers a prompt by default, without the user picking a specific model. It is not a feature update or a UI redesign. The model doing the reasoning changes, so the words it writes can change too.
A tracked-prompt baseline is the set of answers your panel recorded the last time you ran it, the number every future run gets compared against. On Aug. 26, 2026, every baseline built on ChatGPT before that date started measuring a model that no longer exists.
What actually changed
OpenAI ran a 90-day sunset window for o3 before shutting it off. During that window, some ChatGPT sessions still landed on o3 depending on rollout stage and account tier. After Aug. 26, 2026, that mix ended. GPT-5.6 answers every default ChatGPT session now.
| Before Aug. 26, 2026 | After Aug. 26, 2026 | |
|---|---|---|
| Default model | A mix of o3 and GPT-5.6, depending on rollout stage | GPT-5.6 (Sol, Terra, and Luna) only |
| Prompt-panel baseline | Reflects a lineup that no longer exists | Reflects the current default |
| Score comparison | Comparable only to other pre-swap runs | Needs a fresh baseline before it means anything |
You cannot line up a score from Aug. 20 next to a score from Sept. 3 and call the gap a content win or a content loss. Some of that gap traces back to the model swap, not to your content.
Why this breaks your baseline
A visibility score is not a fact about your brand. It is a record of what one model said to one prompt on one day. Change the model, and the prompt can pull a different answer, a different citation, or a different brand mention, with your site untouched the entire time.
This is not a hypothetical. It is the same reason a search-rank tracker gets rerun after a major Google algorithm update instead of trusted blindly across it. A model swap is that update, just for a chat assistant instead of a search engine.
Treat any trend line that crosses Aug. 26, 2026 as two charts stitched into one, not a single continuous measurement. The fix is a rerun, not a recalibration formula. Compare your next panel run to a baseline dated on or after the swap, then keep comparing forward from there.
What to check on your own dashboard
Every prompt-panel tool you run should tell you, without a support ticket, whether a given data point sits before or after a known model swap. Profound, Temso, Peec AI, and Otterly.AI all track ChatGPT as part of their standard engine coverage, so all four sit inside this event’s blast radius.
Open the trend chart for each tool on your stack. Look at the date axis around Aug. 26, 2026. Some dashboards mark known model swaps with a note or a version label near the date; others plot the line straight through as if nothing happened. Only your own screen can tell you which one you have. Do not assume either way.
This is not a ranking of which tool handles the swap better. It is a question every buyer should ask directly, of whatever platform sits in front of them right now.
Dashboard-check checklist
Run through this list for every engine and every tool in your stack, this week and the next time a vendor swaps a default model:
- Find the date of your last full prompt-panel run.
- Compare that date to Aug. 26, 2026, or to whatever swap date applies next time.
- Check your tracker’s trend chart for a note, marker, or version label near that date.
- Ask your vendor directly whether scheduled reruns already absorbed the swap, or whether you need to trigger one.
- Rerun your prompt panel now and set the result as your new baseline.
- Repeat this whole check the next time OpenAI, Google, or Microsoft rotates a default model. This will not be the last one.
Benchmark stability is not something a vendor hands you by default. It is something you check for, every time the ground underneath your prompts moves.
Run the checklist above against your own stack this week, then check the full LLM visibility tool comparison to see how each platform’s prompt-panel cadence and rerun policy hold up against a live model swap. The site’s measurement protocol explains how a valid rerun gets defined.