Original research · 2026-07 edition

AI SEO Statistics: Employment Lawyer (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 employment lawyer.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

My boss is making the workplace really hostile but I haven't been fired yet, should I talk to a lawyer now or wait?
What is the typical contingency fee percentage for a wrongful termination lawsuit?
I just got a severance agreement with a non-disparagement clause, is it worth paying a lawyer to review it?
How do I prove I was fired for whistleblowing if there is no paper trail?
What is the difference between an employment lawyer who represents employees versus one who works for companies?
Can I sue my employer for unpaid overtime if I am classified as an exempt salaried worker?
Is it better to hire a local employment lawyer or a national firm for a discrimination case?
What are the red flags I should look for during an initial consultation with a labor attorney?
Show all 15 questions
How long does a typical workplace harassment case take to settle out of court?
I am being put on a PIP that feels like a setup, do I need legal representation during the HR meetings?
Are there employment lawyers who offer flat-fee services for reviewing a new job contract?
What specific questions should I ask a lawyer to see if they have experience with FMLA violations?
My company is headquartered in another state, do I hire a lawyer where I live or where the office is?
If I quit because of toxic conditions, can I still sue for constructive discharge?
How much evidence do I need to collect before a lawyer will even consider taking my case on contingency?

Model by model

24-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 employment lawyer buyers.

Behavior rates across 15 employment lawyer buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional100%87%53%47%
Suggests DIY first33%20%13%67%
Names specific providers0%7%0%93%
Gives price or cost info13%53%20%53%
Tells to check reviews20%7%0%80%
Tells to verify credentials20%27%20%67%
Mentions case studies / portfolio27%0%7%73%
Mentions local proximity33%33%13%53%
Gives selection criteria40%40%33%60%
Warns about red flags13%7%13%93%
Asks a clarifying question60%80%0%7%
Recommends multiple quotes13%13%0%73%

By model

How each assistant handled Employment Lawyer questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same employment lawyer questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 100% (ChatGPT) down to 53.3% (Gemini), a 47-point gap on an identical question set.

Across the 15 employment lawyer answers it produced, ChatGPT recommended hiring a professional in 100% of them and suggested a DIY approach first 33.3% 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 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 518 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 employment lawyer answers it produced, Claude recommended hiring a professional in 86.7% of them and suggested a DIY approach first 20% 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 53.3% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 26.7%, averaging 315 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 33.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 employment lawyer answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 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 13.3%, and told the buyer to verify credentials in 20%, 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 6.7%, and framed the choice around local proximity in 13.3%; 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 an employment lawyer buyer to a professional (100%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 518 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 24.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an employment lawyer buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 80% (Claude) — a 80-point spread.
  • Recommends hiring a professional: from 53.3% (Gemini) to 100% (ChatGPT) — a 47-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 53.3% (Claude) — a 40-point spread.
  • Mentions case studies or portfolio: from 0% (Claude) to 26.7% (ChatGPT) — a 27-point spread.
  • Suggests a DIY approach first: from 13.3% (Gemini) to 33.3% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 80 points — means an employment 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 employment lawyer market.

Where they agree

The points of near-consensus in Employment Lawyer.

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

  • Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 20%–26.7% across all three (a 7-point spread).
  • Gives selection criteria: 33.3%–40% across all three (a 7-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" (6.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for employment lawyer are recommends hiring a professional (80% on average), asks a clarifying question (46.7%) and gives selection criteria (37.8%); the rarest are names a specific provider (2.2%), recommends multiple quotes (8.9%) and tells the buyer to check reviews (8.9%). 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 100%, Claude 86.7%, Gemini 53.3%) — a 47-point spread.
  • Asks a clarifying question: 46.7% on average (ChatGPT 60%, Claude 80%, Gemini 0%) — a 80-point spread.
  • Gives selection criteria: 37.8% on average (ChatGPT 40%, Claude 40%, Gemini 33.3%) — a 7-point spread.
  • Gives price or cost information: 28.9% on average (ChatGPT 13.3%, Claude 53.3%, Gemini 20%) — a 40-point spread.
  • Mentions local proximity: 26.6% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
  • Suggests a DIY approach first: 22.2% on average (ChatGPT 33.3%, Claude 20%, Gemini 13.3%) — a 20-point spread.
  • Tells the buyer to verify credentials: 22.2% on average (ChatGPT 20%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Mentions case studies or portfolio: 11.1% on average (ChatGPT 26.7%, Claude 0%, Gemini 6.7%) — a 27-point spread.
  • Warns about red flags or scams: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
  • Recommends multiple quotes: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the employment lawyer buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the employment lawyer buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 22.2%. Warning about red flags or scams appeared in 11.1%.

On structuring the decision, a selection-criteria checklist showed up in 37.8% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for employment lawyer is "tells the buyer to check reviews" at 8.9% 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 Employment Lawyer providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 employment lawyer answers, a specific provider was named in 2.2% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for employment lawyer: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Employment Lawyer questions cover.

The 15 questions behind every percentage on this page were drawn from real employment 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 employment 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 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 employment 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.

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 →