Ask AI the Same Question Again the Same Day. Are the Brands the Same?

Key findings

1. 7.4% vs 44.4%: when the same question was asked again the same day, and after excluding cases where neither answer had a brand, ChatGPT gave an identical brand list only 7.4% of the time; Google AI Mode did so 44.4% of the time.
2. 42.1 vs 21.4: in September 2026, for the same set of questions, ChatGPT's brand lists scored 42.1 in overlap when asked again the same day; comparing ChatGPT's answers with Google AI Overviews' answers, the score was only 21.4.
3. 80 out of 80: from June to September, with the same questions and the same number of comparisons, brand lists from "asking again on the same platform" were always more alike than from "asking on a different platform," with no exception across 80 comparison pairs.
4. 68.8 percentage points: on ChatGPT, when the same question was asked only twice, the statistically estimated 95% interval for a brand's appearance rate averaged 68.8 percentage points wide. Asking twice can't tell you how often a brand actually appears.

A brand manager asks AI the same question twice. The first time, their product is on the list; the second time, it isn't. An agency can point to the first screenshot and say "it's already working," and the client can point to the second and say "we're still not showing up." Both screenshots may be real, but neither answers the real questions: how often does the brand appear for this kind of question, is what's said about it correct, and does it keep improving after optimization?

The Dageno Team searched the Dageno AI answer database for cases from June to September 2026 where the same question was collected two or more times on the same day, across ChatGPT, Google AI Mode, Google AI Overviews, Gemini and Copilot. We only compared answers with the same question, country, day and platform, and the same model version and run mode: 7,030 comparisons in total.

01 | Ask the same question again the same day: is the brand list the same?

Most of the time, no, and platforms differ widely.

June to September combined:

Table: "identical" means exactly the same brands both times, ignoring order. Overlap score = brands in both answers ÷ distinct brands across both × 100.

• ChatGPT, Gemini and Copilot rarely give identical lists, all under 8%, with overlap scores between 38 and 42.
• Google AI Mode is the most consistent, with identical lists 44.4% of the time and an overlap score of 65.0.

How to read the overlap score: if one answer lists A, B, C and the other lists B, C, D, the two share B and C out of 4 distinct brands, so the overlap score is 50. The overlap score is not a brand appearance rate; ChatGPT's 39.7 does not mean a given brand has only a 39.7% chance of appearing.

02 | If the list changes, does that mean your brand is unstable?

Not necessarily. The whole list can change while you stay on it the whole time.

Suppose a brand appears in 8 of 10 answers, but the brands appearing alongside it are different every time. The full list might never repeat once, yet this brand is very stable. Another brand that appeared only once can still leave behind a nice-looking screenshot.

So to judge a brand's performance, keep three metrics apart:

Using the example above again: one answer lists A, B, C and the other lists B, C, D. If you're B, you're there both times; if you're A, you're gone the second time. The same score of 50 means completely different things for different brands.

A January 2026 study by SparkToro and Gumshoe (AIs are highly inconsistent when recommending brands or products) also found that AI rarely gives the same brand list twice, but they consider "how often a brand appears across many runs" a metric worth tracking. They had volunteers ask the same set of questions 60 to 100 times each, which differs from our routine collection, so the two sets of numbers can't be compared directly.

03 | Why count "no brand either time" separately?

Because it inflates the share of "identical" lists.

If neither answer mentions any brand, two empty lists also count as "identical." From June to September, this happened in 3.0% to 6.5% of all comparisons, depending on the platform. Including these, ChatGPT's identical rate is 11.9%; excluding them, it's only 7.4%. Copilot falls from 13.6% to 7.6%, and Gemini from 8.5% to 5.7%.

Not mentioning a brand either time isn't necessarily a bad answer. For example, when a user asks about a basic concept, AI doesn't need to mention brands at all. So before evaluating these questions, first decide whether they call for a brand choice.

Also note that the order of brands in the list is their position parsed from the answer, which isn't necessarily the same as "the top recommendation." Changes in name order shouldn't be described as "AI search ranking fluctuations."

04 | Is each platform's same-day consistency the same every month?

No, and there's no common direction.

Overlap score of the two brand lists when asked again the same day:

• ChatGPT was higher in September than in June, rising from 35.3 to 44.3.
• Google AI Overviews went the other way, falling from 81.7 to 36.9.
• Google AI Mode swung up and down, between 57.8 and 73.1.

Sample size matters too. Google AI Overviews had only 32 comparisons in July and 535 in September, so those two months' numbers can't be treated as equally reliable. The questions and samples also differ by month, so this table does not show that "AI is getting more stable" or "more random."

For a brand, rather than borrowing an all-platform average as the standard, it's better to build a continuous baseline for its own key questions.

05 | How different is it when you ask on another platform?

Much more different than asking again on the same platform.

To rule out the effects of question mix and sample size, we only compare the two situations with the same questions and the same number of comparisons. ChatGPT's results in September 2026:

Table: numbers are brand-list overlap scores. The Copilot pair has fewer comparisons; read for direction only.

It isn't just ChatGPT. From June to September, each platform was compared once with each of the other 4, for 80 comparison pairs in total, and "asking again on the same platform" scored higher than "asking on a different platform" every time. Across the 4 industry question sets, there were only 2 exceptions, with just 38 and 29 comparisons respectively.

This means results seen on ChatGPT can't be extended to other AI platforms. Brands with limited resources can pick one priority platform while keeping a baseline for the others. Accurate product facts can be shared, but cited pages and specific gaps need to be checked platform by platform.

06 | Is it the same across industries?

No, and there's no single benchmark.

ChatGPT's brand-list overlap score when asked again the same day: omnichannel customer messaging 47.1, charging and power banks 45.4, AI gateways 35.8, web scraping APIs 35.0. The numbers of comparisons were 196, 234, 167 and 159.

These industry question sets differ in timing and regional mix, so the differences can't all be attributed to industry. The takeaway: one client's level of fluctuation can't be used as another client's standard.

07 | Can asking two or three times measure a brand's appearance rate accurately?

No.

Start with the simplest arithmetic: if you ask the same question only 2 times, a brand's appearance rate can only be 0%, 50% or 100%; with 3 times, only 0%, 33%, 67% or 100%.

With a scale this coarse, you can't explain "the appearance rate rose by 5 percentage points."

Our data shows this too. For question groups asked only 2 times, the statistically estimated 95% interval for a brand's appearance rate averaged 67.7 to 69.0 percentage points wide across platforms; ChatGPT, for example, was 68.8. For groups asked 3 times, it was 58.9 to 63.6 percentage points across platforms. In other words, after asking only two or three times, a brand's true appearance rate could fall anywhere in a very wide range.

Note that the groups asked 2 times and 3 times are different question groups (823 and 70 groups respectively for ChatGPT), not the same questions going from 2 runs to 3, so they can't be used to calculate "how much precision one more run adds." Also, multiple answers on the same day aren't necessarily independent of each other, and they don't represent changes across different days.

So first decide what kind of judgment you need to make, then decide how many times and over how many days to ask:

• To find factual errors: a single verifiable incorrect statement is worth fixing.
• To find content direction: only invest when you see the same gap repeatedly on different dates.
• To evaluate small improvements: you need a fixed question group, enough observations and a baseline.

08 | What should marketing teams do next?

1. Don't draw conclusions from a single screenshot. Being included once doesn't prove it works, and being absent once doesn't prove it doesn't. In monthly reports, look at how often the brand appears across the same set of questions.
2. Report the three metrics separately. Identical-list rate, list overlap score and target brand appearance rate answer different questions. Don't judge a single brand by changes in the whole list.
3. Fix the conditions of comparison. Record the questions, region, language, AI platform, collection date and run conditions. Look at newly added questions separately; don't mix them into the original overall score.
4. Look at each platform separately. Results from asking again on the same platform are much more stable than switching platforms; results on ChatGPT don't represent other platforms.
5. Keep both frequency and the original answers. Frequency shows how often you appear; the original answers show whether what's said is correct and why competitors were chosen.
6. Asking 30 times in one day is not the same as 30 days of records. It can give you a rough baseline, but it can't draw a historical trend. To evaluate optimization, retest with the same set of questions on multiple dates, and note any product, pricing and PR changes in the same period.

Methodology

The data in this article comes from the Dageno AI answer database, June to September 2026, covering ChatGPT, Google AI Mode, Google AI Overviews, Gemini and Copilot. Same-platform repeat comparisons were grouped by question, country, date, platform, model version and run mode, and different answers within each group were compared: 7,030 comparisons in total.

"Identical" uses all comparisons as the denominator; when excluding "no brand either time," these comparisons are removed from both the numerator and the denominator. Overlap scores are only calculated for comparisons where at least one answer had a brand name, and monthly totals are weighted by number of comparisons. Cross-platform comparisons only compare results with the same questions and the same number of comparisons. The 95% interval for brand appearance rate is estimated per group and then averaged. These repeats come from routine data collection, not a pre-designed randomized retest experiment, so this article does not give a ranking of platform randomness, a precision guarantee or a universal minimum number of repeats. Brands are automatically detected names and are not the same as recommendations.

References

1. SparkToro, "NEW Research: AIs are highly inconsistent when recommending brands or products; marketers should take care when tracking AI visibility", January 28, 2026.