AI SEO Statistics: Aviation (2026-07 edition)
5 questions · 15/15 expected AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — a frozen buyer-intent benchmark for aviation.
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.
Model by model
26.7% 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 5 aviation benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 100% | 60% | 20% | 60% |
| Suggests DIY first | 20% | 20% | 20% | 20% |
| Names specific providers | 0% | 40% | 40% | 26.7% |
| Gives price or cost info | 20% | 60% | 20% | 33.3% |
| Tells to check reviews | 20% | 0% | 0% | 6.7% |
| Tells to verify credentials | 20% | 20% | 0% | 13.3% |
| Mentions case studies / portfolio | 20% | 0% | 0% | 6.7% |
| Mentions local proximity | 40% | 20% | 20% | 26.7% |
| Gives selection criteria | 60% | 20% | 40% | 40% |
| Warns about red flags | 20% | 20% | 40% | 26.7% |
| Asks a clarifying question | 40% | 20% | 0% | 20% |
| Recommends multiple quotes | 0% | 0% | 0% | 0% |
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 5 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 20% |
| Suggests DIY first | 100% |
| Names specific providers | 40% |
| Gives price or cost info | 60% |
| Tells to check reviews | 80% |
| Tells to verify credentials | 60% |
| Mentions case studies / portfolio | 80% |
| Mentions local proximity | 40% |
| Gives selection criteria | 20% |
| Warns about red flags | 80% |
| Asks a clarifying question | 40% |
| Recommends multiple quotes | 100% |
By model
How each assistant handled Aviation questions.
Reading the 15 answers model by model shows how differently the three assistants treat the same aviation questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 100% (ChatGPT) down to 20% (Gemini), a 80-point gap on an identical question set.
Across the 5 aviation answers it produced, ChatGPT recommended hiring a professional in 100% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 831 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 5 aviation answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 0.6 distinct providers per answer) and included price or cost information 60% of the time. Claude asked a clarifying question before answering in 20% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 344 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 5 aviation answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 1 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 0%, averaging 193 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route an aviation buyer to a professional (100%) and Gemini the least (20%). ChatGPT produced the longest answers, at 831 words on average. Specific providers were named most often by Claude (40%) — 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 26.7% — 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 an aviation buyer happens to ask matters most:
- Recommends hiring a professional: from 20% (Gemini) to 100% (ChatGPT) — a 80-point spread.
- Names a specific provider: from 0% (ChatGPT) to 40% (Claude) — a 40-point spread.
- Gives price or cost information: from 20% (ChatGPT) to 60% (Claude) — a 40-point spread.
- Gives selection criteria: from 20% (Claude) to 60% (ChatGPT) — a 40-point spread.
- Asks a clarifying question: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
The widest single gap — recommends hiring a professional, 80 points — means an aviation 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 aviation market.
Where they agree
The points of near-consensus in Aviation.
On other behaviors the three models move almost in lockstep — the points of near-consensus for aviation, where all three landed within a few points of each other:
- Suggests a DIY approach first: 20% across all three models.
- Recommends multiple quotes: 0% across all three models.
- Tells the buyer to check reviews: 0%–20% across all three (a 20-point spread).
- Tells the buyer to verify credentials: 0%–20% across all three (a 20-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 100% of questions) and least consistently on "gives selection criteria" (20%).
Every behavior, measured
All twelve coded behaviors for Aviation, averaged across the three models.
The behaviors AI models reproduce most often for aviation are recommends hiring a professional (60% on average), gives selection criteria (40%) and gives price or cost information (33.3%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (6.7%) and tells the buyer to check reviews (6.7%). Each figure below is the share of a model's 5 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: 60% on average (ChatGPT 100%, Claude 60%, Gemini 20%) — a 80-point spread.
- Gives selection criteria: 40% on average (ChatGPT 60%, Claude 20%, Gemini 40%) — a 40-point spread.
- Gives price or cost information: 33.3% on average (ChatGPT 20%, Claude 60%, Gemini 20%) — a 40-point spread.
- Names a specific provider: 26.7% on average (ChatGPT 0%, Claude 40%, Gemini 40%) — a 40-point spread.
- Mentions local proximity: 26.7% on average (ChatGPT 40%, Claude 20%, Gemini 20%) — a 20-point spread.
- Warns about red flags or scams: 26.7% on average (ChatGPT 20%, Claude 20%, Gemini 40%) — a 20-point spread.
- Suggests a DIY approach first: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
- Asks a clarifying question: 20% on average (ChatGPT 40%, Claude 20%, Gemini 0%) — a 40-point spread.
- Tells the buyer to verify credentials: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%) — a 20-point spread.
- Mentions case studies or portfolio: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%) — a 20-point spread.
- Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the aviation buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the aviation buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 26.7%.
On structuring the decision, a selection-criteria checklist showed up in 40% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for aviation is "recommends multiple quotes" at 0% 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 Aviation providers?
For service providers the decisive question is whether these systems name anyone at all. Across 15 aviation answers, a specific provider was named in 26.7% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for aviation: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 5 Aviation questions cover.
The 5 questions behind every percentage on this page form a frozen aviation (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 aviation 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 5 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 aviation 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.
5 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: Aviation (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/aviation