AI SEO Statistics: Family Law Firm (2026-07 edition)
15 questions · 45 AI responses · 3 models · measured 2026-07-04
The question bank
The questions we tested — sampled from real buyer journeys in family law firm.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
19-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 family law firm buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 93% | 100% | 47% | 47% |
| Suggests DIY first | 27% | 7% | 13% | 80% |
| Names specific providers | 0% | 7% | 7% | 93% |
| Gives price or cost info | 13% | 27% | 20% | 67% |
| Tells to check reviews | 0% | 7% | 0% | 93% |
| Tells to verify credentials | 7% | 13% | 0% | 80% |
| Mentions case studies / portfolio | 0% | 13% | 0% | 87% |
| Mentions local proximity | 40% | 53% | 20% | 53% |
| Gives selection criteria | 7% | 33% | 20% | 67% |
| Warns about red flags | 0% | 13% | 7% | 87% |
| Asks a clarifying question | 33% | 67% | 0% | 27% |
| Recommends multiple quotes | 0% | 13% | 0% | 87% |
By model
How each assistant handled Family Law Firm questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same family law firm questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 100% (Claude) down to 46.7% (Gemini), a 53-point gap on an identical question set.
Across the 15 family law firm answers it produced, ChatGPT recommended hiring a professional in 93.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 6.7%, averaging 630 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 40%; a selection-criteria checklist appeared in 6.7% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 family law firm answers it produced, Claude recommended hiring a professional in 100% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 13.3%, averaging 317 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 13.3%.
Across the 15 family law firm answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 250 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 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, Claude is the assistant most likely to route a family law firm buyer to a professional (100%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 630 words on average. Specific providers were named most often by Claude (6.7%) — even there, roughly one answer in 15 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 18.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a family law firm buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 66.7% (Claude) — a 67-point spread.
- Recommends hiring a professional: from 46.7% (Gemini) to 100% (Claude) — a 53-point spread.
- Mentions local proximity: from 20% (Gemini) to 53.3% (Claude) — a 33-point spread.
- Gives selection criteria: from 6.7% (ChatGPT) to 33.3% (Claude) — a 27-point spread.
- Suggests a DIY approach first: from 6.7% (Claude) to 26.7% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 67 points — means a family law firm 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 family law firm market.
Where they agree
The points of near-consensus in Family Law Firm.
On other behaviors the three models move almost in lockstep — the points of near-consensus for family law firm, where all three landed within a few points of each other:
- Names a specific provider: 0%–6.7% across all three (a 7-point spread).
- Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
- Tells the buyer to verify credentials: 0%–13.3% across all three (a 13-point spread).
- Mentions case studies or portfolio: 0%–13.3% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (26.7%).
Every behavior, measured
All twelve coded behaviors for Family Law Firm, averaged across the three models.
The behaviors AI models reproduce most often for family law firm are recommends hiring a professional (80% on average), mentions local proximity (37.8%) and asks a clarifying question (33.3%); the rarest are tells the buyer to check reviews (2.2%), recommends multiple quotes (4.4%) and mentions case studies or portfolio (4.4%). 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: 80% on average (ChatGPT 93.3%, Claude 100%, Gemini 46.7%) — a 53-point spread.
- Mentions local proximity: 37.8% on average (ChatGPT 40%, Claude 53.3%, Gemini 20%) — a 33-point spread.
- Asks a clarifying question: 33.3% on average (ChatGPT 33.3%, Claude 66.7%, Gemini 0%) — a 67-point spread.
- Gives price or cost information: 20% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
- Gives selection criteria: 20% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 20%) — a 27-point spread.
- Suggests a DIY approach first: 15.6% on average (ChatGPT 26.7%, Claude 6.7%, Gemini 13.3%) — a 20-point spread.
- Tells the buyer to verify credentials: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Warns about red flags or scams: 6.7% on average (ChatGPT 0%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
- Names a specific provider: 4.5% on average (ChatGPT 0%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
- Mentions case studies or portfolio: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Recommends multiple quotes: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Tells the buyer to check reviews: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
Trust signals
How well the models protect the family law firm buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the family law firm buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 6.7%.
On structuring the decision, a selection-criteria checklist showed up in 20% of answers on average and a recommendation to gather multiple quotes in 4.4%. The single least-reproduced protective signal for family law firm is "tells the buyer to check reviews" at 2.2% 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 Family Law Firm providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 family law firm answers, a specific provider was named in 4.5% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for family law firm: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Family Law Firm questions cover.
The 15 questions behind every percentage on this page were drawn from real family law firm (legal 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 family law firm 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 family law firm 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 →