Original research · 2026-07 edition

AI SEO Statistics: Solar Company (2026-07 edition)

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

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The question bank

The questions we tested — sampled from real buyer journeys in solar company.

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

My monthly electric bill is averaging $250, how do I calculate if the ROI on solar panels actually makes sense for my specific house?
Is it cheaper to buy the solar panels myself and hire an electrician to install them, or should I go with a full-service solar company?
What are the most important questions to ask a solar consultant during the initial home visit to make sure they aren't just a high-pressure salesperson?
I've heard horror stories about roof leaks after solar installation; how can I verify a company's specific warranty regarding roof penetrations?
What is the current average price per kilowatt for a residential solar setup in my area, including labor and permits?
Should I opt for a solar lease or a PPA, or is it always better to own the equipment outright through financing?
My roof is about 12 years old; do I need to replace the shingles before I have solar panels installed, or can they work around it?
What are the red flags I should look for in a solar contract regarding equipment degradation and performance guarantees?
Show all 15 questions
How does net metering work if my utility company doesn't offer a 1-to-1 credit for the energy I send back to the grid?
If I plan on buying an electric vehicle next year, how many additional panels should I include in my system design now to handle the extra load?
Does having a solar system actually increase my home's resale value, or does it make the house harder to sell if the panels are leased?
What is the difference between string inverters and microinverters, and which one is better for a roof that gets partial shade in the afternoon?
Are there any state-specific rebates or local incentives currently available that I can stack with the 30% federal tax credit?
How do I compare two solar quotes that use different brands of panels and have wildly different estimated annual production numbers?
If the solar company I hire goes out of business in five years, who becomes responsible for servicing my system and honoring the equipment warranties?

Model by model

25-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 solar company buyers.

Behavior rates across 15 solar company buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%40%13%40%
Suggests DIY first27%13%20%87%
Names specific providers13%13%13%73%
Gives price or cost info33%27%47%47%
Tells to check reviews7%7%0%87%
Tells to verify credentials33%7%0%60%
Mentions case studies / portfolio13%0%0%87%
Mentions local proximity53%20%13%53%
Gives selection criteria33%33%27%40%
Warns about red flags13%7%13%80%
Asks a clarifying question47%47%7%47%
Recommends multiple quotes40%13%0%53%

By model

How each assistant handled Solar Company questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same solar company questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 13.3% (Gemini), a 60-point gap on an identical question set.

Across the 15 solar company answers it produced, ChatGPT 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 13.3% of answers (about 0.2 distinct providers per answer) and included price or cost information 33.3% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 33.3%, averaging 664 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 40%.

Across the 15 solar company answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 13.3% of answers (about 0.2 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 342 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, 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 13.3%.

Across the 15 solar company answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.4 distinct providers per answer) and included price or cost information 46.7% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 198 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 13.3%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a solar company buyer to a professional (73.3%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 664 words on average. Specific providers were named most often by ChatGPT (13.3%) — even there, roughly one answer in 8 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 24.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a solar company buyer happens to ask matters most:

  • Recommends hiring a professional: from 13.3% (Gemini) to 73.3% (ChatGPT) — a 60-point spread.
  • Mentions local proximity: from 13.3% (Gemini) to 53.3% (ChatGPT) — a 40-point spread.
  • Asks a clarifying question: from 6.7% (Gemini) to 46.7% (ChatGPT) — a 40-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.

The widest single gap — recommends hiring a professional, 60 points — means a solar company 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 solar company market.

Where they agree

The points of near-consensus in Solar Company.

On other behaviors the three models move almost in lockstep — the points of near-consensus for solar company, where all three landed within a few points of each other:

  • Names a specific provider: 13.3% across all three models.
  • Gives selection criteria: 26.7%–33.3% across all three (a 7-point spread).
  • Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-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 86.7% of questions) and least consistently on "gives selection criteria" (40%).

Every behavior, measured

All twelve coded behaviors for Solar Company, averaged across the three models.

The behaviors AI models reproduce most often for solar company are recommends hiring a professional (42.2% on average), gives price or cost information (35.6%) and asks a clarifying question (33.4%); the rarest are mentions case studies or portfolio (4.4%), tells the buyer to check reviews (4.5%) and warns about red flags or scams (11.1%). 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: 42.2% on average (ChatGPT 73.3%, Claude 40%, Gemini 13.3%) — a 60-point spread.
  • Gives price or cost information: 35.6% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 46.7%) — a 20-point spread.
  • Asks a clarifying question: 33.4% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 6.7%) — a 40-point spread.
  • Gives selection criteria: 31.1% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 26.7%) — a 7-point spread.
  • Mentions local proximity: 28.9% on average (ChatGPT 53.3%, Claude 20%, Gemini 13.3%) — a 40-point spread.
  • Suggests a DIY approach first: 20% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 20%) — a 13-point spread.
  • Recommends multiple quotes: 17.8% on average (ChatGPT 40%, Claude 13.3%, Gemini 0%) — a 40-point spread.
  • Names a specific provider: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 33.3%, Claude 6.7%, Gemini 0%) — a 33-point spread.
  • Warns about red flags or scams: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Mentions case studies or portfolio: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the solar company buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the solar company buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 11.1%.

On structuring the decision, a selection-criteria checklist showed up in 31.1% of answers on average and a recommendation to gather multiple quotes in 17.8%. The single least-reproduced protective signal for solar company is "tells the buyer to check reviews" at 4.5% 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 Solar Company providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 solar company answers, a specific provider was named in 13.3% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for solar company: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Solar Company questions cover.

The 15 questions behind every percentage on this page were drawn from real solar company (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 solar company 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 solar company 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 →