An AI model returns different brands for identical prompts for seven reasons: model randomness, wording, language, question type, timing, personal context, and version drift. The deeper cause is co-occurrence. When a user asks an LLM a question, the model retrieves a novel set of sources, different sources link brands to different topics, and the model builds a unique recommendation for every interaction. A stable internal link between a brand and its category forms once a pair appears together roughly 1,000 to 2,000 times in a model's training data.
Last updated October 26, 2026.
What Does Co-occurrence Mean in Plain Language?
Co-occurrence counts how often brands appear alongside related topics like categories, competitor names, features, price discussions, and commonly used phrases. AI models do not rank like traditional search engines. Co-occurrence impacts consumer searches in large language models more than most technical optimizations, and brand recommendations are driven by two forms of it. The first is historical: how often brands appeared alongside relevant terms in the model's training data. The second is contextual: how often those brands appear alongside key terms in the live search results the model reads when generating an answer.
LLMs learn through patterns rather than fact-checking. If a brand appears frequently alongside a query like "best CRM for a five-person team" in its training data, the model will likely associate that brand with that search. If a brand appears once or twice, the model may overlook it. Researchers call this effect "co-occurrence bias," and it persists even after scaling or fine-tuning the model (Kang and Choi, arXiv:2310.08256).
Layer One: How Does Co-occurrence in LLM Training Data Work?
Frequency has a measurable threshold. This study found that a stable internal link forms once a pair appears together 1,000 to 2,000 times in a model's training data. Below that count, the model holds no dependable link between a brand and its category. From there, it reaches for whatever it links to, usually the market leader, and occasionally a non-existent product.
Layer one explains a pattern that frustrates clients: a technically sound website with minimal third-party mentions often loses a recommendation to a weaker competitor with mentions across multiple online sources.
Layer Two: How Does Co-occurrence Behave in Live Search?
Most brand-specific searches trigger a live web search, and that query decides the model's shortlist. One analysis of 46 business software prompts, tracked over four months, found that ChatGPT used web search 95.5% of the time and that 92.7% of the brands it recommended appeared on its cited pages. The same research found that a cited brand maintains a recommendation 75% of the time, while a brand missing from cited pages holds only 13% of the time.
A single query pulls roughly 20 to 40 URLs, and ChatGPT often relies on Bing's index rather than Google's. Perplexity runs a separate pipeline, and Claude leans on Brave. A page missing from one index cannot be read by the tool built on it.
Narrow searches help explain the split. A study of 22.7 million citations across five models found that 79.6% of cited sources appeared in only one model, and that Perplexity never cited 89.1% of the sources that ChatGPT cited for the same prompt. Models named the same brand 30.3% of the time while landing on the same page only 6.8%.
Brand-level associations carry across models. Individual URLs almost never do.
Why Do Ten Identical Searches Create Ten Different Responses? 7 Reasons
Randomness inside the model. One lab sent an identical prompt 1,000 times with randomness switched off and received 80 different completions; the first 102 words matched, then the responses diverged. The cause is the way servers batch requests, so an answer varies with traffic from other users. OpenAI states that its API runs non-deterministically by default and that its seed setting yields mostly repeatable output.
Wording. Reword one consumer question, and the overlap between the two brand lists drops to 0.288, versus a 0.50 to 0.61 baseline when running an identical prompt twice. Add a constraint, and overlap falls to 0.135.
Language. A study of 12,933 responses across 20 brands, 8 languages, and 3 models found that query language accounted for 26.5% of the variance, while brand identity accounted for 1.5%.
Question type. Comparison prompts recommended the same brands 30.1% of the time; background prompts about the same brands did so 7.5% of the time.
Timing. A Google AI summary carries about a 70% chance of varying between searches, and a given version lasts roughly 2.15 days.
Personal context. Memory, earlier turns in the chat, account settings, and rough location all factor at varying weights into the user's request.
Version drift. Vendors release model updates and retrieval changes without notice. Reddit's share of ChatGPT citations fell from roughly 60% of prompt responses in early August 2025 to about 10% by mid-September without a documented cause.
Seven layers of drift sit between the question and the answer, before anyone touches the content.
What Does Co-occurrence Mean for AI Visibility Reporting?
A single response does not provide much information. This variance study put the reliability of a single answer at about 0.01, and reliability climbed only to 0.36 once every language and model entered the design. Rerunning the same prompt a sixth time cuts the error by 0.0003, so the popular habit of averaging five runs provides minimal value. Adding languages, models, and paraphrases is a better use of resources. One informal study asked five questions over 100 times and received an identical set of recommendations in 23% of responses, and the same set in the same order in 7%.
Read the statistic for its denominator. Several viral claims about Reddit trace back to a single, misleading dataset: Reddit accounts for 1.8% of all ChatGPT citations, not the 11.3% that circulates, because the larger figure counts each model's top ten sources. Citation counts per answer disagree similarly, with one study putting ChatGPT at 7.92 sources and another at 13.3.
Reliability rises with breadth of design, not with repeated runs of the same prompt.
How Does Co-occurrence Move a Brand Into LLM Recommendations?
Third-party collaborations remain important. Ahrefs measured 75,000 brands and found off-site brand mentions tracked with AI summary visibility at 0.664, while backlinks managed 0.218.
Prioritize earned coverage. Controlled tests across several categories found AI search weighs heavily toward third-party sources and away from brand-owned pages and social posts.
Appear on pages with consistent citations. Comparison posts, roundups, review sites, and community threads are popular sources, and brands need to appear in them alongside relevant category terms.
Identify important use cases. Mention rates jump when content pairs a brand with a buying situation, which is what a comparison prompt asks for.
Treat page-level tweaks as supplemental. Structured data findings conflict across large studies, and one cross-platform study of 1,006 pages found schema no more common on cited pages than on uncited ones, 43.1% against 44.8%.
Be wary of popular "optimization playbooks." The original paper on optimizing for AI answers reported gains near 30% to 40% from adding quotes and statistics, but an independent replication found a real positive effect in only 3 of 54 cases, and adding statistics lowered rankings in 19 of 24 settings.
Ahrefs measured 75,000 brands: off-site mentions track AI visibility roughly three times harder than backlinks.
Co-occurrence in two sentences: LLMs write novel answers for every prompt. Their answers depend on retrieved sources, and those sources differ by model, language, and minute. A strong search presence makes your brand a go-to source for AI models, and success is measured as trends across models from a set of targeted, relevant prompts based on user needs, not "best" queries.
Co-occurrence makes it hard to optimize for a single prompt, model, or page and expect consistent recommendations. Brands need repeated associations with the right categories, use cases, product attributes, and consumer needs across owned content, earned media, reviews, comparison pages, and other sources that models retrieve.
Frequently Asked Questions
What is co-occurrence in LLMs?
Co-occurrence is how often a brand appears alongside related topics, categories, competitors, and phrases, both in a model's training data and in the live search results it reads when answering. It shapes which brands a model associates with a query.
How many mentions create a stable brand and category link?
One study found that a stable internal link forms once a brand and category pair appears together roughly 1,000 to 2,000 times in a model's training data. Below that, the model tends to substitute the market leader.
Why do identical prompts return different brands?
Seven factors drive the variation: model randomness, wording, language, question type, timing, personal context, and version drift.
How should brands measure AI visibility if answers vary?
Measure trends across models, languages, and paraphrases from a targeted prompt set rather than averaging repeated runs of one prompt. A single answer has a reliability of about 0.01.
5W is a PR, creative and GEO agency. Explore 5W's AI Search and Generative Engine Optimization services to see how your brand can earn more citations and recommendations across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
