Original research · 2026-07 edition

AI SEO Statistics: Junk Removal (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in junk removal.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

I'm cleaning out my garage and there's a bunch of old paint and electronics. Can regular trash take this or do I need a specialist?
Is it cheaper to rent a dumpster for a weekend or just hire a crew to haul everything away in one go?
What kind of insurance should a junk removal company have to make sure I'm not liable if they get hurt on my property?
How do junk removal companies usually charge? Is it by the hour or how much space it takes up in the truck?
What's the difference between a full-service junk removal company and just hiring someone off TaskRabbit for a haul-off?
Are there any companies that will come inside my house to carry a heavy sofa down two flights of stairs, or do I have to leave it on the curb?
What are some warning signs that a junk removal service might just be illegally dumping my stuff in a field somewhere?
I need a house cleared out by tomorrow for a closing. Is same-day junk removal actually a thing or just marketing?
Show all 15 questions
I have about $300 to spend on clearing out a basement. How much stuff can I realistically expect a pro service to take for that price?
Does anyone actually recycle old mattresses, or do they all just end up in the landfill regardless of which company I hire?
Do I need to bag everything up before the junk haulers arrive, or will they literally pick things up off the floor for me?
How can I verify if a junk removal business is actually disposing of hazardous waste like old car batteries properly?
Is it worth paying more for a 'green' junk removal service, or is the recycling process basically the same across the board?
My tenant left behind a mountain of trash and furniture. Can I get a receipt or itemized list from a junk company to deduct the cost from their security deposit?
Will a junk removal company take a refrigerator that still has Freon in it, or is that something I have to handle separately?

Model by model

29-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about junk removal buyers.

Behavior rates across 15 junk removal buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%73%47%40%
Suggests DIY first20%27%7%73%
Names specific providers13%47%47%47%
Gives price or cost info13%40%40%53%
Tells to check reviews13%13%0%80%
Tells to verify credentials47%33%20%47%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity47%53%20%33%
Gives selection criteria47%53%33%33%
Warns about red flags7%33%20%60%
Asks a clarifying question40%60%0%27%
Recommends multiple quotes27%13%7%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 junk removal buyer to a professional (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.

The divergence index for this study is 29.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a junk removal buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 86.7% (ChatGPT) — a 40-point spread.
  • Names a specific provider: from 13.3% (ChatGPT) to 46.7% (Claude) — a 33-point spread.
  • Mentions local proximity: from 20% (Gemini) to 53.3% (Claude) — a 33-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 40% (Claude) — a 27-point spread.

The widest single gap — asks a clarifying question, 60 points — means a junk removal 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 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%) — a 40-point spread.
  • Gives selection criteria: 44.4% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 33.3%) — a 20-point spread.
  • Mentions local proximity: 40% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 20%) — a 33-point spread.
  • Names a specific provider: 35.6% on average (ChatGPT 13.3%, Claude 46.7%, Gemini 46.7%) — a 33-point spread.
  • Tells the buyer to verify credentials: 33.3% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 20%) — a 27-point spread.
  • Asks a clarifying question: 33.3% on average (ChatGPT 40%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Gives price or cost information: 31.1% on average (ChatGPT 13.3%, Claude 40%, Gemini 40%) — a 27-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 20%) — a 27-point spread.
  • Suggests a DIY approach first: 17.8% on average (ChatGPT 20%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 6.7%) — a 20-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • 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 — 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 — 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 were drawn from real junk removal (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 junk removal 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 15 answers in which the behavior appeared at least once — 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 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →