AI SEO Statistics: Heavy Equipment (2026-07 edition)
30 questions · 90/90 expected AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — a frozen buyer-intent benchmark for heavy equipment.
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 30 questions
Model by model
20% 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 30 heavy equipment benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 63.3% | 50% | 16.7% | 43.3% |
| Suggests DIY first | 20% | 23.3% | 13.3% | 18.9% |
| Names specific providers | 0% | 16.7% | 33.3% | 16.7% |
| Gives price or cost info | 16.7% | 23.3% | 30% | 23.3% |
| Tells to check reviews | 0% | 3.3% | 0% | 1.1% |
| Tells to verify credentials | 20% | 16.7% | 0% | 12.2% |
| Mentions case studies / portfolio | 10% | 6.7% | 0% | 5.6% |
| Mentions local proximity | 20% | 16.7% | 6.7% | 14.5% |
| Gives selection criteria | 33.3% | 46.7% | 23.3% | 34.4% |
| Warns about red flags | 10% | 16.7% | 10% | 12.2% |
| Asks a clarifying question | 63.3% | 70% | 0% | 44.4% |
| Recommends multiple quotes | 10% | 6.7% | 0% | 5.6% |
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 30 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 50% |
| Suggests DIY first | 83.3% |
| Names specific providers | 63.3% |
| Gives price or cost info | 70% |
| Tells to check reviews | 96.7% |
| Tells to verify credentials | 76.7% |
| Mentions case studies / portfolio | 86.7% |
| Mentions local proximity | 70% |
| Gives selection criteria | 56.7% |
| Warns about red flags | 83.3% |
| Asks a clarifying question | 16.7% |
| Recommends multiple quotes | 86.7% |
By model
How each assistant handled Heavy Equipment questions.
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.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 20% — 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 heavy equipment buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 70% (Claude) — a 70-point spread.
- Recommends hiring a professional: from 16.7% (Gemini) to 63.3% (ChatGPT) — a 47-point spread.
- Names a specific provider: from 0% (ChatGPT) to 33.3% (Gemini) — a 33-point spread.
- Gives selection criteria: from 23.3% (Gemini) to 46.7% (Claude) — a 23-point spread.
- Tells the buyer to verify credentials: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 70 points — means a heavy equipment 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 heavy equipment market.
Where they agree
The points of near-consensus in Heavy Equipment.
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%).
Every behavior, measured
All twelve coded behaviors for Heavy Equipment, averaged across the three models.
The behaviors AI models reproduce most often for heavy equipment are asks a clarifying question (44.4% on average), recommends hiring a professional (43.3%) and gives selection criteria (34.4%); the rarest are tells the buyer to check reviews (1.1%), recommends multiple quotes (5.6%) and mentions case studies or portfolio (5.6%). Each figure below is the share of a model's 30 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Asks a clarifying question: 44.4% on average (ChatGPT 63.3%, Claude 70%, Gemini 0%) — a 70-point spread.
- Recommends hiring a professional: 43.3% on average (ChatGPT 63.3%, Claude 50%, Gemini 16.7%) — a 47-point spread.
- Gives selection criteria: 34.4% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 23.3%) — a 23-point spread.
- Gives price or cost information: 23.3% on average (ChatGPT 16.7%, Claude 23.3%, Gemini 30%) — a 13-point spread.
- Suggests a DIY approach first: 18.9% on average (ChatGPT 20%, Claude 23.3%, Gemini 13.3%) — a 10-point spread.
- Names a specific provider: 16.7% on average (ChatGPT 0%, Claude 16.7%, Gemini 33.3%) — a 33-point spread.
- Mentions local proximity: 14.5% on average (ChatGPT 20%, Claude 16.7%, Gemini 6.7%) — a 13-point spread.
- Tells the buyer to verify credentials: 12.2% on average (ChatGPT 20%, Claude 16.7%, Gemini 0%) — a 20-point spread.
- Warns about red flags or scams: 12.2% on average (ChatGPT 10%, Claude 16.7%, Gemini 10%) — a 7-point spread.
- Mentions case studies or portfolio: 5.6% on average (ChatGPT 10%, Claude 6.7%, Gemini 0%) — a 10-point spread.
- Recommends multiple quotes: 5.6% on average (ChatGPT 10%, Claude 6.7%, Gemini 0%) — a 10-point spread.
- Tells the buyer to check reviews: 1.1% on average (ChatGPT 0%, Claude 3.3%, Gemini 0%) — a 3-point spread.
Trust signals
How well the models protect the heavy equipment buyer.
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.
Referral behavior
Do AI models name Heavy Equipment providers?
For service providers the decisive question is whether these systems name anyone at all. Across 90 heavy equipment answers, a specific provider was named in 16.7% of responses on average — roughly 0.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for heavy equipment: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 30 Heavy Equipment questions cover.
The 30 questions behind every percentage on this page form a frozen heavy equipment (manufacturing / industrial B2B; 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 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.
How to read this
A note on the numbers.
A percentage here is the share of a model's 30 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 heavy equipment 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.
30 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: Heavy Equipment (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/manufacturing/heavy-equipment