AI SEO Statistics: Junk Removal (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your junk removal 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 junk removal.
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 15 questions
Model by model
29.3% 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 15 junk removal benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 86.7% | 73.3% | 46.7% | 68.9% |
| Suggests DIY first | 20% | 26.7% | 6.7% | 17.8% |
| Names specific providers | 13.3% | 46.7% | 46.7% | 35.6% |
| Gives price or cost info | 13.3% | 40% | 40% | 31.1% |
| Tells to check reviews | 13.3% | 13.3% | 0% | 8.9% |
| Tells to verify credentials | 46.7% | 33.3% | 20% | 33.3% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 46.7% | 53.3% | 20% | 40% |
| Gives selection criteria | 46.7% | 53.3% | 33.3% | 44.4% |
| Warns about red flags | 6.7% | 33.3% | 20% | 20% |
| Asks a clarifying question | 40% | 60% | 0% | 33.3% |
| Recommends multiple quotes | 26.7% | 13.3% | 6.7% | 15.6% |
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 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 40% |
| Suggests DIY first | 73.3% |
| Names specific providers | 46.7% |
| Gives price or cost info | 53.3% |
| Tells to check reviews | 80% |
| Tells to verify credentials | 46.7% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 33.3% |
| Gives selection criteria | 33.3% |
| Warns about red flags | 60% |
| Asks a clarifying question | 26.7% |
| Recommends multiple quotes | 80% |
By model
How each assistant handled Junk Removal questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same junk removal questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 86.7% (ChatGPT) down to 46.7% (Gemini), a 40-point gap on an identical question set.
Across the 15 junk removal answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 46.7%, averaging 463 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 26.7%.
Across the 15 junk removal answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 46.7% of answers (about 1.3 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 33.3%, averaging 280 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 junk removal answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 1.2 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 268 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 33.3% of its answers and a recommendation to gather multiple quotes in 6.7%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching junk removal toward professional help (86.7%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 463 words on average. Specific providers were named most often by Claude (46.7%). Even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 29.3%. 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 the choice of assistant matters most for a buyer researching junk removal:
- Asks a clarifying question: from 0% (Gemini) to 60% (Claude). The spread is 60 points.
- Recommends hiring a professional: from 46.7% (Gemini) to 86.7% (ChatGPT). The spread is 40 points.
- Names a specific provider: from 13.3% (ChatGPT) to 46.7% (Claude). The spread is 33 points.
- Mentions local proximity: from 20% (Gemini) to 53.3% (Claude). The spread is 33 points.
- Gives price or cost information: from 13.3% (ChatGPT) to 40% (Claude). The spread is 27 points.
The widest single gap concerns asks a clarifying question at 60 points. This means a buyer researching junk removal 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 junk removal market.
Where they agree
The points of near-consensus in Junk Removal.
On other behaviors the three models move almost in lockstep. The points of near-consensus for junk removal, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Tells the buyer to check reviews: 0%–13.3% across all three (a 13-point spread).
- Suggests a DIY approach first: 6.7%–26.7% across all three (a 20-point spread).
- Gives selection criteria: 33.3%–53.3% across all three (a 20-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (26.7%).
Every behavior, measured
All twelve coded behaviors for Junk Removal, averaged across the three models.
The behaviors AI models reproduce most often for junk removal are recommends hiring a professional (68.9% on average), gives selection criteria (44.4%) and mentions local proximity (40%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (8.9%) and recommends multiple quotes (15.6%). Each figure below is the share of a model's 15 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: 68.9% on average (ChatGPT 86.7%, Claude 73.3%, Gemini 46.7%). The spread is 40 points.
- Gives selection criteria: 44.4% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 33.3%). The spread is 20 points.
- Mentions local proximity: 40% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 20%). The spread is 33 points.
- Names a specific provider: 35.6% on average (ChatGPT 13.3%, Claude 46.7%, Gemini 46.7%). The spread is 33 points.
- Tells the buyer to verify credentials: 33.3% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 20%). The spread is 27 points.
- Asks a clarifying question: 33.3% on average (ChatGPT 40%, Claude 60%, Gemini 0%). The spread is 60 points.
- Gives price or cost information: 31.1% on average (ChatGPT 13.3%, Claude 40%, Gemini 40%). The spread is 27 points.
- Warns about red flags or scams: 20% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 20%). The spread is 27 points.
- Suggests a DIY approach first: 17.8% on average (ChatGPT 20%, Claude 26.7%, Gemini 6.7%). The spread is 20 points.
- Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 6.7%). The spread is 20 points.
- Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%). The spread is 13 points.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the junk removal buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the junk removal buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 33.3%. Warning about red flags or scams appeared in 20%.
On structuring the decision, a selection-criteria checklist showed up in 44.4% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for junk removal is "tells the buyer to check reviews" at 8.9% 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 Junk Removal providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 junk removal answers, a specific provider was named in 35.6% of responses on average, or roughly 0.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for junk removal: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Junk Removal questions cover.
The 15 questions behind every percentage on this page form a frozen junk removal (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 junk removal 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 15 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific junk removal 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.
15 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 2026-07-04 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: Junk Removal (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/junk-removal