AI SEO Statistics: Solar Company (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your solar company 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 solar company.
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
24.8% 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 solar company benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 73.3% | 40% | 13.3% | 42.2% |
| Suggests DIY first | 26.7% | 13.3% | 20% | 20% |
| Names specific providers | 13.3% | 13.3% | 13.3% | 13.3% |
| Gives price or cost info | 33.3% | 26.7% | 46.7% | 35.6% |
| Tells to check reviews | 6.7% | 6.7% | 0% | 4.5% |
| Tells to verify credentials | 33.3% | 6.7% | 0% | 13.3% |
| Mentions case studies / portfolio | 13.3% | 0% | 0% | 4.4% |
| Mentions local proximity | 53.3% | 20% | 13.3% | 28.9% |
| Gives selection criteria | 33.3% | 33.3% | 26.7% | 31.1% |
| Warns about red flags | 13.3% | 6.7% | 13.3% | 11.1% |
| Asks a clarifying question | 46.7% | 46.7% | 6.7% | 33.4% |
| Recommends multiple quotes | 40% | 13.3% | 0% | 17.8% |
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 | 86.7% |
| Names specific providers | 73.3% |
| Gives price or cost info | 46.7% |
| Tells to check reviews | 86.7% |
| Tells to verify credentials | 60% |
| Mentions case studies / portfolio | 86.7% |
| Mentions local proximity | 53.3% |
| Gives selection criteria | 40% |
| Warns about red flags | 80% |
| Asks a clarifying question | 46.7% |
| Recommends multiple quotes | 53.3% |
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.
Question-level model disagreement is 24.8%. 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 which assistant a solar company buyer happens to ask matters most:
- Recommends hiring a professional: from 13.3% (Gemini) to 73.3% (ChatGPT). The spread is 60 points.
- Mentions local proximity: from 13.3% (Gemini) to 53.3% (ChatGPT). The spread is 40 points.
- Asks a clarifying question: from 6.7% (Gemini) to 46.7% (ChatGPT). The spread is 40 points.
- Recommends multiple quotes: from 0% (Gemini) to 40% (ChatGPT). The spread is 40 points.
- Tells the buyer to verify credentials: from 0% (Gemini) to 33.3% (ChatGPT). The spread is 33 points.
The widest single gap concerns recommends hiring a professional at 60 points. This 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%). The spread is 60 points.
- Gives price or cost information: 35.6% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 46.7%). The spread is 20 points.
- Asks a clarifying question: 33.4% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 6.7%). The spread is 40 points.
- Gives selection criteria: 31.1% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 26.7%). The spread is 7 points.
- Mentions local proximity: 28.9% on average (ChatGPT 53.3%, Claude 20%, Gemini 13.3%). The spread is 40 points.
- Suggests a DIY approach first: 20% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 20%). The spread is 13 points.
- Recommends multiple quotes: 17.8% on average (ChatGPT 40%, Claude 13.3%, Gemini 0%). The spread is 40 points.
- 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%). The spread is 33 points.
- Warns about red flags or scams: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%). The spread is 7 points.
- Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%). The spread is 7 points.
- Mentions case studies or portfolio: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%). The spread is 13 points.
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. 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 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, or 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 form a frozen solar company (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 solar company 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 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 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: Solar Company (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/solar-company