AI SEO Statistics: Heavy Equipment (2026-07 edition)

Reading the 90 answers model by model shows how differently the three assistants treat the same heavy equipment questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 63.3% (ChatGPT) down to 16.7% (Gemini), a 47-point gap on an identical question set.

Across the 30 heavy equipment answers it produced, ChatGPT recommended hiring a professional in 63.3% 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 16.7% of the time. ChatGPT asked a clarifying question before answering in 63.3% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 20%, averaging 601 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 30 heavy equipment answers it produced, Claude recommended hiring a professional in 50% of them and suggested a DIY approach first 23.3% of the time. It named a specific provider in 16.7% of answers (about 0.5 distinct providers per answer) and included price or cost information 23.3% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 16.7%, and told the buyer to verify credentials in 16.7%, averaging 314 words per answer. On the remaining cues it told the buyer to check reviews in 3.3%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 16.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 30 heavy equipment answers it produced, Gemini recommended hiring a professional in 16.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 33.3% of answers (about 1.4 distinct providers per answer) and included price or cost information 30% 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 0%, averaging 252 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 6.7%; a selection-criteria checklist appeared in 23.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a heavy equipment buyer to a professional (63.3%) and Gemini the least (16.7%). ChatGPT produced the longest answers, at 601 words on average. Specific providers were named most often by Gemini (33.3%) — 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 heavy equipment, where all three landed within a few points of each other:

• Tells the buyer to check reviews: 0%–3.3% across all three (a 3-point spread).
• Warns about red flags or scams: 10%–16.7% across all three (a 7-point spread).
• Suggests a DIY approach first: 13.3%–23.3% across all three (a 10-point spread).
• Mentions case studies or portfolio: 0%–10% across all three (a 10-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 96.7% of questions) and least consistently on "asks a clarifying question" (16.7%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the heavy equipment buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 1.1% of answers on average. Verifying credentials or certifications appeared in 12.2%. Warning about red flags or scams appeared in 12.2%.

On structuring the decision, a selection-criteria checklist showed up in 34.4% of answers on average and a recommendation to gather multiple quotes in 5.6%. The single least-reproduced protective signal for heavy equipment is "tells the buyer to check reviews" at 1.1% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 30 questions behind every percentage on this page were drawn from real heavy equipment (manufacturing / industrial B2B; 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 heavy equipment 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.