AI SEO Statistics: Gas Engineers (2026-07 edition)

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.

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%).

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 — 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 gas engineers (home 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 gas engineers 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.