AI SEO Statistics: Movie Theaters (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same movie theaters questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 82.5% (ChatGPT) down to 62.5% (Gemini), a 20-point gap on an identical question set.

Across the 40 movie theaters answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 10% of answers (about 0.9 distinct providers per answer) and included price or cost information 27.5% of the time. ChatGPT asked a clarifying question before answering in 50% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 35%, averaging 630 words per answer. On the remaining cues it told the buyer to check reviews in 32.5%, pointed to case studies or a portfolio in 42.5%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 52.5% of its answers and a recommendation to gather multiple quotes in 17.5%.

Across the 40 movie theaters answers it produced, Claude recommended hiring a professional in 70% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 17.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 22.5%, averaging 296 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 movie theaters answers it produced, Gemini recommended hiring a professional in 62.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 35% of answers (about 1.4 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 10%, averaging 262 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a movie theaters buyer to a professional (82.5%) and Gemini the least (62.5%). ChatGPT produced the longest answers, at 630 words on average. Specific providers were named most often by Gemini (35%) — even there, roughly one answer in 3 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for movie theaters, where all three landed within a few points of each other:

• Suggests a DIY approach first: 2.5%–7.5% across all three (a 5-point spread).
• Warns about red flags or scams: 5%–10% across all three (a 5-point spread).
• Gives price or cost information: 15%–27.5% across all three (a 13-point spread).
• Recommends multiple quotes: 0%–17.5% across all three (a 18-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 87.5% of questions) and least consistently on "asks a clarifying question" (25%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the movie theaters buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15% of answers on average. Verifying credentials or certifications appeared in 22.5%. Warning about red flags or scams appeared in 7.5%.

On structuring the decision, a selection-criteria checklist showed up in 42.5% of answers on average and a recommendation to gather multiple quotes in 10%. The single least-reproduced protective signal for movie theaters is "warns about red flags or scams" at 7.5% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real movie theaters (professional services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact movie theaters question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.