AI SEO Statistics: Movie Theaters (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — a frozen buyer-intent benchmark for movie theaters.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
25.3% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 movie theaters benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 82.5% | 70% | 62.5% | 71.7% |
| Suggests DIY first | 7.5% | 5% | 2.5% | 5% |
| Names specific providers | 10% | 17.5% | 35% | 20.8% |
| Gives price or cost info | 27.5% | 15% | 22.5% | 21.7% |
| Tells to check reviews | 32.5% | 12.5% | 0% | 15% |
| Tells to verify credentials | 35% | 22.5% | 10% | 22.5% |
| Mentions case studies / portfolio | 42.5% | 20% | 5% | 22.5% |
| Mentions local proximity | 27.5% | 15% | 7.5% | 16.7% |
| Gives selection criteria | 52.5% | 45% | 30% | 42.5% |
| Warns about red flags | 10% | 5% | 7.5% | 7.5% |
| Asks a clarifying question | 50% | 67.5% | 0% | 39.2% |
| Recommends multiple quotes | 17.5% | 12.5% | 0% | 10% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 62.5% |
| Suggests DIY first | 87.5% |
| Names specific providers | 67.5% |
| Gives price or cost info | 62.5% |
| Tells to check reviews | 67.5% |
| Tells to verify credentials | 62.5% |
| Mentions case studies / portfolio | 52.5% |
| Mentions local proximity | 62.5% |
| Gives selection criteria | 30% |
| Warns about red flags | 87.5% |
| Asks a clarifying question | 25% |
| Recommends multiple quotes | 77.5% |
By model
How each assistant handled Movie Theaters questions.
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.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 25.3% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a movie theaters buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 67.5% (Claude) — a 68-point spread.
- Mentions case studies or portfolio: from 5% (Gemini) to 42.5% (ChatGPT) — a 38-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 32.5% (ChatGPT) — a 33-point spread.
- Names a specific provider: from 10% (ChatGPT) to 35% (Gemini) — a 25-point spread.
- Tells the buyer to verify credentials: from 10% (Gemini) to 35% (ChatGPT) — a 25-point spread.
The widest single gap — asks a clarifying question, 68 points — means a movie theaters buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the movie theaters market.
Where they agree
The points of near-consensus in Movie Theaters.
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%).
Every behavior, measured
All twelve coded behaviors for Movie Theaters, averaged across the three models.
The behaviors AI models reproduce most often for movie theaters are recommends hiring a professional (71.7% on average), gives selection criteria (42.5%) and asks a clarifying question (39.2%); the rarest are suggests a DIY approach first (5%), warns about red flags or scams (7.5%) and recommends multiple quotes (10%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Recommends hiring a professional: 71.7% on average (ChatGPT 82.5%, Claude 70%, Gemini 62.5%) — a 20-point spread.
- Gives selection criteria: 42.5% on average (ChatGPT 52.5%, Claude 45%, Gemini 30%) — a 23-point spread.
- Asks a clarifying question: 39.2% on average (ChatGPT 50%, Claude 67.5%, Gemini 0%) — a 68-point spread.
- Tells the buyer to verify credentials: 22.5% on average (ChatGPT 35%, Claude 22.5%, Gemini 10%) — a 25-point spread.
- Mentions case studies or portfolio: 22.5% on average (ChatGPT 42.5%, Claude 20%, Gemini 5%) — a 38-point spread.
- Gives price or cost information: 21.7% on average (ChatGPT 27.5%, Claude 15%, Gemini 22.5%) — a 13-point spread.
- Names a specific provider: 20.8% on average (ChatGPT 10%, Claude 17.5%, Gemini 35%) — a 25-point spread.
- Mentions local proximity: 16.7% on average (ChatGPT 27.5%, Claude 15%, Gemini 7.5%) — a 20-point spread.
- Tells the buyer to check reviews: 15% on average (ChatGPT 32.5%, Claude 12.5%, Gemini 0%) — a 33-point spread.
- Recommends multiple quotes: 10% on average (ChatGPT 17.5%, Claude 12.5%, Gemini 0%) — a 18-point spread.
- Warns about red flags or scams: 7.5% on average (ChatGPT 10%, Claude 5%, Gemini 7.5%) — a 5-point spread.
- Suggests a DIY approach first: 5% on average (ChatGPT 7.5%, Claude 5%, Gemini 2.5%) — a 5-point spread.
Trust signals
How well the models protect the movie theaters buyer.
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.
Referral behavior
Do AI models name Movie Theaters providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 movie theaters answers, a specific provider was named in 20.8% of responses on average — roughly 1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for movie theaters: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Movie Theaters questions cover.
The 40 questions behind every percentage on this page form a frozen movie theaters (professional services; buyer hiring decisions for this specific service) buyer-intent benchmark. 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.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific movie theaters question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
40 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Movie Theaters (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/movie-theaters