ChatGPT, Gemini and Grok frequently cite different sources for the same question — disagreement is closer to the rule than the exception. Because each model uses different training data, retrieval, recency windows and ranking logic, the same buyer query can return three largely non-overlapping source sets. The practical effect: a domain that “owns” a query in one model may be entirely absent from another. You measure this with a source-overlap metric — for a given query, what fraction of cited domains appears in two or three models versus just one. Mentn’s index tracks ChatGPT today; Gemini and Grok are on the roadmap, so that overlap becomes a live measurement here as those models come online rather than a number we assert now. The takeaway is firm even without a figure of our own: plan to measure across every model your buyers use, never one.
What does “model disagreement” mean?
For a single buyer question, you can ask all three models the same thing and collect the domains each one cites. Model disagreement is the degree to which those three source sets differ. If all three cite the same handful of domains, agreement is high. If each names a mostly different set, disagreement is high. The cleanest way to quantify it is source-overlap: of all the distinct domains cited across the three answers, what share is cited by only one model, by exactly two, and by all three.
This is a metric you essentially cannot produce from a single tool that samples one model. It requires querying ChatGPT, Gemini and Grok independently for the same questions and comparing — which is what Mentn’s query–domain index is built to do. The index tracks ChatGPT today; Gemini and Grok are on the roadmap.
So how often do they actually disagree?
The pattern reported by teams that compare the models side by side, and consistent with how the assistants observably behave, is that single-model citations are common and full three-way agreement is comparatively rare, especially for commercial and “best-of” queries where the candidate set is large. Informational, definitional questions tend toward higher agreement — there are fewer defensible sources, so the models converge. Open-ended “which tool/brand should I pick” questions tend toward higher disagreement, because there are many reasonable candidates and each model weights them differently.
We deliberately avoid quoting a single headline percentage as gospel. Overlap depends heavily on the query mix, how you canonicalise domains, and the window you sample. The robust finding is directional: disagreement is large enough that one-model measurement materially under-counts your exposure. Treat any specific ratio you see elsewhere as an estimate tied to a particular sample; Mentn will publish its own overlap figures once Gemini and Grok are live in the index.
Why do the models disagree?
- Different retrieval. Each assistant pulls from a different live index / search partner, so the candidate pool differs before ranking even starts.
- Different training and recency. Knowledge cutoffs and update cadence vary, so newer pages surface unevenly across models.
- Different weighting. One model may lean on community sources (Reddit, forums), another on documentation or review platforms — see which content types get cited most.
- Non-determinism. Even the same model can vary run to run, which is why disagreement and volatility are related but distinct.
Why this changes your strategy
If you only watch ChatGPT, three things go wrong. You over-estimate the value of a ChatGPT win (it may not transfer). You miss gaps where Gemini or Grok cite a competitor you don’t even know about. And you misattribute changes — a drop in one model can look like a catastrophe when your other-model coverage is intact.
Grok is the most under-watched of the three, which makes it both a blind spot and an opportunity; the SMB-side guide AI visibility on Grok covers why. The practical rule from this research: build for all three, and measure each as it becomes measurable — Mentn tracks ChatGPT today, with Gemini and Grok on the roadmap.
How to measure your own coverage
Start with the free Domain Check, which returns the queries ChatGPT cites your domain on today. Gemini and Grok are on the roadmap; when they go live, the same report will show coverage per query and per model, so single-model versus multi-model coverage becomes directly visible. Run it on your domain and a competitor’s now to baseline your category. For the full picture across all the structural findings, see the State of AI Citations 2026 report.
Frequently asked questions
Do ChatGPT, Gemini and Grok usually cite the same sources?
No. For the same buyer question they regularly cite different sources. Overlap between any two models is partial, and all-three agreement is the exception rather than the rule.
Why do the three models disagree?
They are different systems with different training data, retrieval, and freshness weighting. Each composes its own answer from its own view of the web, so past the most obvious source the citations diverge quickly.
Do you publish an exact agreement-rate percentage?
Not yet. We describe the pattern qualitatively — disagreement is the norm — and will only publish a figure once it is sampled, dated and sourced per our methodology. Mentn’s index tracks ChatGPT today; the cross-model figure arrives as Gemini and Grok come online. We do not invent a number.
What should I do about model disagreement?
Measure visibility per model rather than as one blended score. The free Domain Check shows your ChatGPT coverage today; Gemini and Grok are on the roadmap and will appear per model in the same report when they are live.