AI SEO Statistics: Gas Engineers (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your gas engineers SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for gas engineers.
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
19.9% 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 gas engineers benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 90% | 90% | 65% | 81.7% |
| Suggests DIY first | 25% | 25% | 22.5% | 24.2% |
| Names specific providers | 0% | 5% | 12.5% | 5.8% |
| Gives price or cost info | 17.5% | 15% | 15% | 15.8% |
| Tells to check reviews | 17.5% | 5% | 0% | 7.5% |
| Tells to verify credentials | 62.5% | 55% | 40% | 52.5% |
| Mentions case studies / portfolio | 5% | 5% | 0% | 3.3% |
| Mentions local proximity | 32.5% | 12.5% | 12.5% | 19.2% |
| Gives selection criteria | 45% | 35% | 20% | 33.3% |
| Warns about red flags | 15% | 15% | 10% | 13.3% |
| Asks a clarifying question | 80% | 75% | 0% | 51.7% |
| Recommends multiple quotes | 15% | 12.5% | 0% | 9.2% |
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 | 60% |
| Suggests DIY first | 92.5% |
| Names specific providers | 85% |
| Gives price or cost info | 72.5% |
| Tells to check reviews | 82.5% |
| Tells to verify credentials | 60% |
| Mentions case studies / portfolio | 92.5% |
| Mentions local proximity | 67.5% |
| Gives selection criteria | 62.5% |
| Warns about red flags | 82.5% |
| Asks a clarifying question | 5% |
| Recommends multiple quotes | 80% |
By model
How each assistant handled Gas Engineers questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same gas engineers questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 90% (ChatGPT) down to 65% (Gemini), a 25-point gap on an identical question set.
Across the 40 gas engineers answers it produced, ChatGPT recommended hiring a professional in 90% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 17.5% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 62.5%, averaging 434 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 15%.
Across the 40 gas engineers answers it produced, Claude recommended hiring a professional in 90% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 55%, averaging 288 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 12.5%.
Across the 40 gas engineers answers it produced, Gemini recommended hiring a professional in 65% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 12.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 40%, averaging 293 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 12.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a gas engineers buyer to a professional (90%) and Gemini the least (65%). ChatGPT produced the longest answers, at 434 words on average. Specific providers were named most often by Gemini (12.5%). Even there, roughly one answer in 8 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 19.9%. This is 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 gas engineers buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT). The spread is 80 points.
- Recommends hiring a professional: from 65% (Gemini) to 90% (ChatGPT). The spread is 25 points.
- Gives selection criteria: from 20% (Gemini) to 45% (ChatGPT). The spread is 25 points.
- Tells the buyer to verify credentials: from 40% (Gemini) to 62.5% (ChatGPT). The spread is 23 points.
- Mentions local proximity: from 12.5% (Claude) to 32.5% (ChatGPT). The spread is 20 points.
The widest single gap concerns asks a clarifying question at 80 points. This means a gas engineers 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 gas engineers market.
Where they agree
The points of near-consensus in Gas Engineers.
On other behaviors the three models move almost in lockstep. The points of near-consensus for gas engineers, where all three landed within a few points of each other:
- Suggests a DIY approach first: 22.5%–25% across all three (a 3-point spread).
- Gives price or cost information: 15%–17.5% across all three (a 3-point spread).
- Mentions case studies or portfolio: 0%–5% across all three (a 5-point spread).
- Warns about red flags or scams: 10%–15% across all three (a 5-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 92.5% of questions) and least consistently on "asks a clarifying question" (5%).
Every behavior, measured
All twelve coded behaviors for Gas Engineers, averaged across the three models.
The behaviors AI models reproduce most often for gas engineers are recommends hiring a professional (81.7% on average), tells the buyer to verify credentials (52.5%) and asks a clarifying question (51.7%); the rarest are mentions case studies or portfolio (3.3%), names a specific provider (5.8%) and tells the buyer to check reviews (7.5%). 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: 81.7% on average (ChatGPT 90%, Claude 90%, Gemini 65%). The spread is 25 points.
- Tells the buyer to verify credentials: 52.5% on average (ChatGPT 62.5%, Claude 55%, Gemini 40%). The spread is 23 points.
- Asks a clarifying question: 51.7% on average (ChatGPT 80%, Claude 75%, Gemini 0%). The spread is 80 points.
- Gives selection criteria: 33.3% on average (ChatGPT 45%, Claude 35%, Gemini 20%). The spread is 25 points.
- Suggests a DIY approach first: 24.2% on average (ChatGPT 25%, Claude 25%, Gemini 22.5%). The spread is 3 points.
- Mentions local proximity: 19.2% on average (ChatGPT 32.5%, Claude 12.5%, Gemini 12.5%). The spread is 20 points.
- Gives price or cost information: 15.8% on average (ChatGPT 17.5%, Claude 15%, Gemini 15%). The spread is 3 points.
- Warns about red flags or scams: 13.3% on average (ChatGPT 15%, Claude 15%, Gemini 10%). The spread is 5 points.
- Recommends multiple quotes: 9.2% on average (ChatGPT 15%, Claude 12.5%, Gemini 0%). The spread is 15 points.
- Tells the buyer to check reviews: 7.5% on average (ChatGPT 17.5%, Claude 5%, Gemini 0%). The spread is 18 points.
- Names a specific provider: 5.8% on average (ChatGPT 0%, Claude 5%, Gemini 12.5%). The spread is 13 points.
- Mentions case studies or portfolio: 3.3% on average (ChatGPT 5%, Claude 5%, Gemini 0%). The spread is 5 points.
Trust signals
How well the models protect the gas engineers buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the gas engineers buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 7.5% of answers on average. Verifying credentials or certifications appeared in 52.5%. Warning about red flags or scams appeared in 13.3%.
On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 9.2%. The single least-reproduced protective signal for gas engineers is "tells the buyer to check reviews" at 7.5% on average. This is 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 Gas Engineers providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 gas engineers answers, a specific provider was named in 5.8% of responses on average, or roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for gas engineers: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Gas Engineers questions cover.
The 40 questions behind every percentage on this page form a frozen gas engineers (home 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 gas engineers question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific gas engineers 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: Gas Engineers (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/gas-engineers