AI SEO Statistics: Workers Comp Lawyer (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in workers comp lawyer.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
20-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 workers comp lawyer buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 83% | 73% | 30% | 35% |
| Suggests DIY first | 50% | 43% | 10% | 53% |
| Names specific providers | 0% | 0% | 0% | 100% |
| Gives price or cost info | 18% | 33% | 13% | 63% |
| Tells to check reviews | 5% | 8% | 3% | 93% |
| Tells to verify credentials | 5% | 5% | 3% | 95% |
| Mentions case studies / portfolio | 3% | 5% | 5% | 98% |
| Mentions local proximity | 35% | 20% | 5% | 58% |
| Gives selection criteria | 23% | 15% | 10% | 70% |
| Warns about red flags | 15% | 18% | 15% | 80% |
| Asks a clarifying question | 88% | 83% | 0% | 8% |
| Recommends multiple quotes | 3% | 0% | 0% | 98% |
By model
How each assistant handled Workers Comp Lawyer questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same workers comp lawyer questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 82.5% (ChatGPT) down to 30% (Gemini), a 53-point gap on an identical question set.
Across the 40 workers comp lawyer answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 50% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 17.5% of the time. ChatGPT asked a clarifying question before answering in 87.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 5%, averaging 513 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 35%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 2.5%.
Across the 40 workers comp lawyer answers it produced, Claude recommended hiring a professional in 72.5% of them and suggested a DIY approach first 42.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 32.5% of the time. Claude asked a clarifying question before answering in 82.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 5%, averaging 308 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 40 workers comp lawyer answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 12.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 2.5%, averaging 280 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 10% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a workers comp lawyer buyer to a professional (82.5%) and Gemini the least (30%). ChatGPT produced the longest answers, at 513 words on average. No model named a specific provider in more than 0% of answers.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 19.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a workers comp lawyer buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 87.5% (ChatGPT) — a 88-point spread.
- Recommends hiring a professional: from 30% (Gemini) to 82.5% (ChatGPT) — a 53-point spread.
- Suggests a DIY approach first: from 10% (Gemini) to 50% (ChatGPT) — a 40-point spread.
- Mentions local proximity: from 5% (Gemini) to 35% (ChatGPT) — a 30-point spread.
- Gives price or cost information: from 12.5% (Gemini) to 32.5% (Claude) — a 20-point spread.
The widest single gap — asks a clarifying question, 88 points — means a workers comp lawyer 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 workers comp lawyer market.
Where they agree
The points of near-consensus in Workers Comp Lawyer.
On other behaviors the three models move almost in lockstep — the points of near-consensus for workers comp lawyer, where all three landed within a few points of each other:
- Names a specific provider: 0% across all three models.
- Tells the buyer to verify credentials: 2.5%–5% across all three (a 3-point spread).
- Mentions case studies or portfolio: 2.5%–5% across all three (a 3-point spread).
- Warns about red flags or scams: 15%–17.5% across all three (a 3-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 100% of questions) and least consistently on "asks a clarifying question" (7.5%).
Every behavior, measured
All twelve coded behaviors for Workers Comp Lawyer, averaged across the three models.
The behaviors AI models reproduce most often for workers comp lawyer are recommends hiring a professional (61.7% on average), asks a clarifying question (56.7%) and suggests a DIY approach first (34.2%); the rarest are names a specific provider (0%), recommends multiple quotes (0.8%) and mentions case studies or portfolio (4.2%). Each figure below is the share of a model's 40 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: 61.7% on average (ChatGPT 82.5%, Claude 72.5%, Gemini 30%) — a 53-point spread.
- Asks a clarifying question: 56.7% on average (ChatGPT 87.5%, Claude 82.5%, Gemini 0%) — a 88-point spread.
- Suggests a DIY approach first: 34.2% on average (ChatGPT 50%, Claude 42.5%, Gemini 10%) — a 40-point spread.
- Gives price or cost information: 20.8% on average (ChatGPT 17.5%, Claude 32.5%, Gemini 12.5%) — a 20-point spread.
- Mentions local proximity: 20% on average (ChatGPT 35%, Claude 20%, Gemini 5%) — a 30-point spread.
- Gives selection criteria: 15.8% on average (ChatGPT 22.5%, Claude 15%, Gemini 10%) — a 13-point spread.
- Warns about red flags or scams: 15.8% on average (ChatGPT 15%, Claude 17.5%, Gemini 15%) — a 3-point spread.
- Tells the buyer to check reviews: 5% on average (ChatGPT 5%, Claude 7.5%, Gemini 2.5%) — a 5-point spread.
- Tells the buyer to verify credentials: 4.2% on average (ChatGPT 5%, Claude 5%, Gemini 2.5%) — a 3-point spread.
- Mentions case studies or portfolio: 4.2% on average (ChatGPT 2.5%, Claude 5%, Gemini 5%) — a 3-point spread.
- Recommends multiple quotes: 0.8% on average (ChatGPT 2.5%, Claude 0%, Gemini 0%) — a 3-point spread.
- Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the workers comp lawyer buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the workers comp lawyer buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5% of answers on average. Verifying credentials or certifications appeared in 4.2%. Warning about red flags or scams appeared in 15.8%.
On structuring the decision, a selection-criteria checklist showed up in 15.8% of answers on average and a recommendation to gather multiple quotes in 0.8%. The single least-reproduced protective signal for workers comp lawyer is "recommends multiple quotes" at 0.8% 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 Workers Comp Lawyer providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 workers comp lawyer answers, a specific provider was named in 0% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for workers comp lawyer: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Workers Comp Lawyer questions cover.
The 40 questions behind every percentage on this page were drawn from real workers comp lawyer (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 workers comp lawyer 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific workers comp lawyer 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.
40 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 2026-07-06, 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 →