Does ChatGPT Give the Same Answers to Everyone? I Asked It Twice and Got Two Different Lists

I asked ChatGPT one question, twice: "What are the best rank tracking tools?" Two fresh sessions, both logged out, minutes apart.

Run one gave me ten tools with Ahrefs on top. Run two gave me eight, led by Semrush. Two tools from the first list had simply vanished, and only one of the runs bothered to search the web before answering.

Same question, same engine, same minute, different answer. This post is about why that happens, how big the drift really is, and what it means when being the name ChatGPT picks is worth money to you.

Does ChatGPT give the same answers to everyone?

No. Not to everyone, and not even to you twice.

Here is run one. Ten tools in the table, Ahrefs first, a "Potential drawbacks" column, and no sources cited anywhere.

And here is run two, minutes later in a fresh session. Eight tools, Semrush first, the drawbacks column swapped for pricing, and Wincher and Moz Pro gone. This run also decided to search the web and cite TechRadar; the first answered from its training memory alone.

Both sessions were logged out. No chat history, no memory, no custom instructions, the two things people usually blame, and the answers still disagreed on a fifth of the list.

The stable part matters too. Semrush, Ahrefs, SE Ranking and AccuRanker showed up both times. The core names held while the edges churned. Keep that pattern in mind, because the research says it is the rule, not a coincidence.

How different are the answers, really?

My two runs are an anecdote. The measured data goes further, and in the same direction.

The St. Gallen team re-ran the same prompts on four engines and measured how much the cited sources overlapped. Runs fired within the same 24 hours agreed on only 32 to 43% of their sources. Their paper's title is the whole finding: "Don't Measure Once."

SparkToro and Gumshoe.ai went wider. Volunteers ran 12 brand-recommendation prompts 60 to 100 times each, 2,961 runs in total. The chance of two runs returning the identical brand list was under 1 in 100, and the same list in the same order was closer to 1 in 1,000. Yet in their headphones test, Bose, Sony, Sennheiser and Apple still appeared in 55 to 77% of 994 responses.

Profound compared identical prompts a month apart: 40 to 60% of cited domains had changed, with Google's AI Overviews drifting hardest at 59.3%. Over six months, 70 to 90% were different.

The exact answer almost never repeats. The set of names it draws from is far steadier.

Why does ChatGPT give different answers to the same question?

Nothing is broken. Five mechanics stack on top of each other, and any one of them is enough to change your answer.

It picks every word from a probability list

Each time ChatGPT writes a word, it chooses from a list of likely next words rather than always taking the top option. The setting that controls this is called temperature. One early pick lands differently, the sentence bends, and three paragraphs later Wincher is missing from your table. This alone means two identical prompts rarely produce identical text.

One run searches the web, the next does not

ChatGPT decides per query whether to search the web, and what to fetch when it does. My first run cited nothing; the second searched and leaned on TechRadar. Different sources in, different list out. This is also why cited sources churn even faster than brand names in every study above: the retrieval step re-rolls before the writing step does.

Memory and custom instructions rewrite your prompt

With memory on, notes from your earlier chats ride along with every new question, and custom instructions sit on top of the framing before the model sees it. Two people typing the same words are not sending the same prompt. My test dodged this entirely by staying logged out, and the answers still differed.

The model underneath keeps changing

Silent updates, A/B tests, and routing between model versions mean the ChatGPT you asked on Monday is not always the one answering on Friday. Profound's month-apart drift, where more than half of ChatGPT's cited domains changed, is partly just this: the machine itself moved.

Location and language shift the sources

Ask from Delhi and from Dallas and the search layer reaches for different regional sources, prices and availability. Same for language. If your buyers are in the US and you check answers from India, you are not looking at their answer, which is a real trap when you start checking AI visibility for your own brand.

Can you make ChatGPT give the same answer every time?

Mostly no, and chasing it is the wrong goal.

In the app there is no consistency setting. What you can control: use a temporary chat or switch memory off for a clean baseline, keep the same model selected, and ask in a fresh chat instead of continuing an old thread where the earlier conversation steers the reply.

Developers get closer through the API by setting temperature to 0. Closer, not all the way; identical calls can still return different text, and OpenAI has never promised deterministic output.

A single ChatGPT answer is a sample, not a fact. Treat any one response, including a flattering one about your own brand, the way you would treat one visitor session in your analytics: interesting, never evidence.

What does this mean for your brand's AI visibility?

This is where the variance stops being trivia, because I have watched it with real money attached.

My test store took more than 80 of its 91 orders from ChatGPT and Perplexity referrals, and I documented the whole thing in the ChatGPT SEO case study. Yet when I asked ChatGPT about the niche myself, it often did not show me my own store. The orders were real. My personal screenshot meant nothing, in both directions.

So, three working rules.

A screenshot is not a ranking. If a tool or an agency shows you "your brand is #2 in ChatGPT" from one run, you now know the odds behind that claim: the same list repeats less than 1 time in 100. The AI visibility trackers worth using run your prompts on a schedule and report mention rate, the share of runs where you appear at all.

Measure like the St. Gallen paper says. Many runs, mention share, trend over weeks. One check per month tells you nothing except what the dice did that day.

Work the steady layer. The exact list churns, but the pool of names the answers draw from is stable and earned: clear entity signals, consistent brand facts everywhere, and third-party mentions on the pages engines keep citing. That is the same work behind getting cited by ChatGPT and Perplexity, and it moves every roll of the dice in your favour instead of one screenshot.

Final take

ChatGPT is not a results page. It is a dice cup, and the dice are weighted.

You cannot make it show everyone the same answer, and you do not need to. The brands winning AI search are not the ones that ranked first on one lucky run; they are the ones printed on more faces of the dice.

So ask your question ten times before you believe anything, measure mentions instead of positions, and put the effort into the entity and mention work that raises your share of the rolls. The inconsistency stops being a threat the day you start measuring like it exists.