AI SEO Statistics: Legal Services (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your legal services 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 legal services.
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
25.9% 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 legal services benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 86.7% | 66.7% | 46.7% | 66.7% |
| Suggests DIY first | 33.3% | 40% | 26.7% | 33.3% |
| Names specific providers | 6.7% | 20% | 6.7% | 11.1% |
| Gives price or cost info | 33.3% | 53.3% | 33.3% | 40% |
| Tells to check reviews | 13.3% | 6.7% | 0% | 6.7% |
| Tells to verify credentials | 6.7% | 6.7% | 6.7% | 6.7% |
| Mentions case studies / portfolio | 20% | 13.3% | 6.7% | 13.3% |
| Mentions local proximity | 53.3% | 26.7% | 6.7% | 28.9% |
| Gives selection criteria | 53.3% | 46.7% | 33.3% | 44.4% |
| Warns about red flags | 20% | 20% | 20% | 20% |
| Asks a clarifying question | 53.3% | 60% | 0% | 37.8% |
| Recommends multiple quotes | 26.7% | 20% | 0% | 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 | 53.3% |
| Suggests DIY first | 73.3% |
| Names specific providers | 80% |
| Gives price or cost info | 53.3% |
| Tells to check reviews | 80% |
| Tells to verify credentials | 80% |
| Mentions case studies / portfolio | 66.7% |
| Mentions local proximity | 40% |
| Gives selection criteria | 40% |
| Warns about red flags | 73.3% |
| Asks a clarifying question | 33.3% |
| Recommends multiple quotes | 60% |
By model
How each assistant handled Legal Services questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same legal services 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 legal services answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 6.7% of answers (about 0 distinct providers per answer) and included price or cost information 33.3% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 608 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 20%, 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 26.7%.
Across the 15 legal services answers it produced, Claude recommended hiring a professional in 66.7% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 20% of answers (about 0.2 distinct providers per answer) and included price or cost information 53.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 319 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 26.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 20%.
Across the 15 legal services answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 26.7% 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 33.3% 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 6.7%, averaging 263 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching legal services toward professional help (86.7%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 608 words on average. Specific providers were named most often by Claude (20%). Even there, roughly one answer in 5 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 25.9%. 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 legal services:
- Asks a clarifying question: from 0% (Gemini) to 60% (Claude). The spread is 60 points.
- Mentions local proximity: from 6.7% (Gemini) to 53.3% (ChatGPT). The spread is 47 points.
- Recommends hiring a professional: from 46.7% (Gemini) to 86.7% (ChatGPT). The spread is 40 points.
- Recommends multiple quotes: from 0% (Gemini) to 26.7% (ChatGPT). The spread is 27 points.
- Gives price or cost information: from 33.3% (ChatGPT) to 53.3% (Claude). The spread is 20 points.
The widest single gap concerns asks a clarifying question at 60 points. This means a buyer researching legal services 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 legal services market.
Where they agree
The points of near-consensus in Legal Services.
On other behaviors the three models move almost in lockstep. The points of near-consensus for legal services, where all three landed within a few points of each other:
- Tells the buyer to verify credentials: 6.7% across all three models.
- Warns about red flags or scams: 20% across all three models.
- Suggests a DIY approach first: 26.7%–40% across all three (a 13-point spread).
- Names a specific provider: 6.7%–20% 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 80% of questions) and least consistently on "asks a clarifying question" (33.3%).
Every behavior, measured
All twelve coded behaviors for Legal Services, averaged across the three models.
The behaviors AI models reproduce most often for legal services are recommends hiring a professional (66.7% on average), gives selection criteria (44.4%) and gives price or cost information (40%); the rarest are tells the buyer to verify credentials (6.7%), tells the buyer to check reviews (6.7%) and names a specific provider (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: 66.7% on average (ChatGPT 86.7%, Claude 66.7%, Gemini 46.7%). The spread is 40 points.
- Gives selection criteria: 44.4% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 33.3%). The spread is 20 points.
- Gives price or cost information: 40% on average (ChatGPT 33.3%, Claude 53.3%, Gemini 33.3%). The spread is 20 points.
- Asks a clarifying question: 37.8% on average (ChatGPT 53.3%, Claude 60%, Gemini 0%). The spread is 60 points.
- Suggests a DIY approach first: 33.3% on average (ChatGPT 33.3%, Claude 40%, Gemini 26.7%). The spread is 13 points.
- Mentions local proximity: 28.9% on average (ChatGPT 53.3%, Claude 26.7%, Gemini 6.7%). The spread is 47 points.
- Warns about red flags or scams: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
- Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%). The spread is 27 points.
- Mentions case studies or portfolio: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%). The spread is 13 points.
- Names a specific provider: 11.1% on average (ChatGPT 6.7%, Claude 20%, Gemini 6.7%). The spread is 13 points.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%). The spread is 13 points.
- Tells the buyer to verify credentials: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
Trust signals
How well the models protect the legal services buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the legal services buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 6.7%. 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 legal services is "tells the buyer to check reviews" at 6.7% 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 Legal Services providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 legal services answers, a specific provider was named in 11.1% of responses on average, or roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for legal services: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Legal Services questions cover.
The 15 questions behind every percentage on this page form a frozen legal (legal 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 legal services 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 legal services 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: Legal Services (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/legal/legal