Original research · 2026-07 edition

AI SEO Statistics: Personal Injury Lawyer (2026-07 edition)

8 questions · 24/24 expected AI responses · 3 models · measured 2026-07-04

The question bank

The questions we tested — a frozen buyer-intent benchmark for personal injury lawyer.

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.

I got rear-ended last week and my neck is starting to hurt now, is it too late to file a claim if I told the police I was fine at the scene?
Is it worth hiring a lawyer for a dog bite if the medical bills are under $5,000 or will the legal fees eat up the whole settlement?
How can I tell if a personal injury lawyer actually goes to trial versus just settling every case they take on for a quick payout?
If I lose my lawsuit, do I still have to reimburse the law firm for the court filing fees and expert witness costs they paid upfront?
What's the difference between hiring a general practice lawyer and someone who specializes specifically in traumatic brain injury cases?
If I was injured while on vacation in another state, do I need to hire a lawyer from my home town or from the city where the accident happened?
My current attorney hasn't returned my calls in three weeks and is pushing me to accept a low settlement, how do I go about firing them?
I was hit by a delivery driver who was working at the time, do I sue the driver personally or is the company responsible for my medical expenses?

Model by model

21.5% 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 matrixModel-by-model evidence
Measured

Behavior prevalence across 8 personal injury lawyer benchmark questions, 2026-07 edition. Last column: equal-model mean.

Behavior prevalence across 8 personal injury lawyer benchmark questions, 2026-07 edition. Last column: equal-model mean.
BehaviorChatGPTClaudeGeminiEqual-model mean
Recommends hiring a professional75%75%75%75%
Suggests DIY first37.5%37.5%0%25%
Names specific providers0%12.5%12.5%8.3%
Gives price or cost info12.5%37.5%25%25%
Tells to check reviews25%0%0%8.3%
Tells to verify credentials25%12.5%12.5%16.7%
Mentions case studies / portfolio12.5%0%12.5%8.3%
Mentions local proximity37.5%12.5%12.5%20.8%
Gives selection criteria37.5%37.5%12.5%29.2%
Warns about red flags0%12.5%25%12.5%
Asks a clarifying question62.5%62.5%0%41.7%
Recommends multiple quotes0%12.5%0%4.2%

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.

Behavior matrixModel-by-model evidence
Measured

All-model binary agreement by behavior across 8 benchmark questions.

All-model binary agreement by behavior across 8 benchmark questions.
BehaviorAll-model agreement
Recommends hiring a professional75%
Suggests DIY first50%
Names specific providers87.5%
Gives price or cost info62.5%
Tells to check reviews75%
Tells to verify credentials75%
Mentions case studies / portfolio87.5%
Mentions local proximity75%
Gives selection criteria50%
Warns about red flags62.5%
Asks a clarifying question25%
Recommends multiple quotes87.5%

By model

How each assistant handled Personal Injury Lawyer questions.

Reading the 24 answers model by model shows how differently the three assistants treat the same personal injury lawyer questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 75% (ChatGPT) down to 75% (ChatGPT), a 0-point gap on an identical question set.

Across the 8 personal injury lawyer answers it produced, ChatGPT recommended hiring a professional in 75% of them and suggested a DIY approach first 37.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 12.5% of the time. ChatGPT asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 25%, averaging 503 words per answer. On the remaining cues it told the buyer to check reviews in 25%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 37.5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 8 personal injury lawyer answers it produced, Claude recommended hiring a professional in 75% of them and suggested a DIY approach first 37.5% of the time. It named a specific provider in 12.5% of answers (about 0.4 distinct providers per answer) and included price or cost information 37.5% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 12.5%, averaging 322 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 12.5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 8 personal injury lawyer answers it produced, Gemini recommended hiring a professional in 75% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 12.5% of answers (about 0.8 distinct providers per answer) and included price or cost information 25% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 25%, and told the buyer to verify credentials in 12.5%, averaging 277 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 12.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a personal injury lawyer buyer to a professional (75%) and ChatGPT the least (75%). ChatGPT produced the longest answers, at 503 words on average. Specific providers were named most often by Claude (12.5%) — 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 21.5% — 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 personal injury lawyer buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 62.5% (ChatGPT) — a 63-point spread.
  • Suggests a DIY approach first: from 0% (Gemini) to 37.5% (ChatGPT) — a 38-point spread.
  • Gives price or cost information: from 12.5% (ChatGPT) to 37.5% (Claude) — a 25-point spread.
  • Tells the buyer to check reviews: from 0% (Claude) to 25% (ChatGPT) — a 25-point spread.
  • Mentions local proximity: from 12.5% (Claude) to 37.5% (ChatGPT) — a 25-point spread.

The widest single gap — asks a clarifying question, 63 points — means a personal injury 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 personal injury lawyer market.

Where they agree

The points of near-consensus in Personal Injury Lawyer.

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

  • Recommends hiring a professional: 75% across all three models.
  • Names a specific provider: 0%–12.5% across all three (a 13-point spread).
  • Tells the buyer to verify credentials: 12.5%–25% across all three (a 13-point spread).
  • Mentions case studies or portfolio: 0%–12.5% 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 87.5% of questions) and least consistently on "asks a clarifying question" (25%).

Every behavior, measured

All twelve coded behaviors for Personal Injury Lawyer, averaged across the three models.

The behaviors AI models reproduce most often for personal injury lawyer are recommends hiring a professional (75% on average), asks a clarifying question (41.7%) and gives selection criteria (29.2%); the rarest are recommends multiple quotes (4.2%), mentions case studies or portfolio (8.3%) and tells the buyer to check reviews (8.3%). Each figure below is the share of a model's 8 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: 75% on average (ChatGPT 75%, Claude 75%, Gemini 75%).
  • Asks a clarifying question: 41.7% on average (ChatGPT 62.5%, Claude 62.5%, Gemini 0%) — a 63-point spread.
  • Gives selection criteria: 29.2% on average (ChatGPT 37.5%, Claude 37.5%, Gemini 12.5%) — a 25-point spread.
  • Suggests a DIY approach first: 25% on average (ChatGPT 37.5%, Claude 37.5%, Gemini 0%) — a 38-point spread.
  • Gives price or cost information: 25% on average (ChatGPT 12.5%, Claude 37.5%, Gemini 25%) — a 25-point spread.
  • Mentions local proximity: 20.8% on average (ChatGPT 37.5%, Claude 12.5%, Gemini 12.5%) — a 25-point spread.
  • Tells the buyer to verify credentials: 16.7% on average (ChatGPT 25%, Claude 12.5%, Gemini 12.5%) — a 13-point spread.
  • Warns about red flags or scams: 12.5% on average (ChatGPT 0%, Claude 12.5%, Gemini 25%) — a 25-point spread.
  • Names a specific provider: 8.3% on average (ChatGPT 0%, Claude 12.5%, Gemini 12.5%) — a 13-point spread.
  • Tells the buyer to check reviews: 8.3% on average (ChatGPT 25%, Claude 0%, Gemini 0%) — a 25-point spread.
  • Mentions case studies or portfolio: 8.3% on average (ChatGPT 12.5%, Claude 0%, Gemini 12.5%) — a 13-point spread.
  • Recommends multiple quotes: 4.2% on average (ChatGPT 0%, Claude 12.5%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the personal injury lawyer buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the personal injury lawyer buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.3% of answers on average. Verifying credentials or certifications appeared in 16.7%. Warning about red flags or scams appeared in 12.5%.

On structuring the decision, a selection-criteria checklist showed up in 29.2% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for personal injury lawyer is "recommends multiple quotes" at 4.2% 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 Personal Injury Lawyer providers?

For service providers the decisive question is whether these systems name anyone at all. Across 24 personal injury lawyer answers, a specific provider was named in 8.3% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for personal injury lawyer: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 8 Personal Injury Lawyer questions cover.

The 8 questions behind every percentage on this page form a frozen personal injury lawyer (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 personal injury 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 8 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 personal injury 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.

8 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: Personal Injury Lawyer (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/legal/personal-injury-lawyer