Original research · 2026-07 edition

AI SEO Statistics: Solicitor (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 solicitor.

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

I'm buying my first home and I'm confused about the legal side—do I need a solicitor or a conveyancer?
What are the typical hourly rates for a family law solicitor in a mid-sized city?
Is it possible to handle a small claims court case without hiring a solicitor to save money?
What specific questions should I ask during a first meeting to see if a solicitor is right for my probate case?
I've been offered a settlement agreement from my job; does my employer have to pay for my solicitor to review it?
How do I know if a solicitor is actually an expert in medical negligence or just a general personal injury lawyer?
Are fixed-fee legal services better than hourly billing for a straightforward divorce?
My neighbor is building on my land—how quickly can a solicitor get an injunction to stop them?
Show all 15 questions
What are the red flags I should look out for when reading a solicitor's terms of engagement?
Can I use a solicitor located in another part of the country for a property transaction, or is local knowledge essential?
How much should I expect to pay upfront as a retainer for a criminal defense solicitor?
What happens if my solicitor misses a deadline in my case? Can I sue them for malpractice?
Is there a way to compare the success rates of different solicitors for employment tribunal cases?
I need to update my will after a second marriage; should I use the same solicitor who did my first one?
Why do some solicitors charge so much more for the exact same service like power of attorney?

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 solicitor buyers.

Behavior rates across 15 solicitor buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%80%53%53%
Suggests DIY first20%13%7%87%
Names specific providers7%7%7%100%
Gives price or cost info13%27%47%60%
Tells to check reviews13%27%0%73%
Tells to verify credentials20%33%7%60%
Mentions case studies / portfolio7%13%0%87%
Mentions local proximity27%47%20%60%
Gives selection criteria47%73%33%33%
Warns about red flags7%20%13%73%
Asks a clarifying question60%53%0%33%
Recommends multiple quotes27%53%0%40%

By model

How each assistant handled Solicitor questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same solicitor 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 53.3% (Gemini), a 33-point gap on an identical question set.

Across the 15 solicitor answers it produced, ChatGPT 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 0.1 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 6.7%, and told the buyer to verify credentials in 20%, averaging 579 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 6.7%, 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 26.7%.

Across the 15 solicitor answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 26.7% of the time. Claude 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 33.3%, averaging 317 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 53.3%.

Across the 15 solicitor answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 46.7% 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 6.7%, averaging 261 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 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 solicitor buyer to a professional (86.7%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 579 words on average. Specific providers were named most often by ChatGPT (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 24.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a solicitor buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (ChatGPT) — a 60-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 53.3% (Claude) — a 53-point spread.
  • Gives selection criteria: from 33.3% (Gemini) to 73.3% (Claude) — a 40-point spread.
  • Recommends hiring a professional: from 53.3% (Gemini) to 86.7% (ChatGPT) — a 33-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 46.7% (Gemini) — a 33-point spread.

The widest single gap — asks a clarifying question, 60 points — means a solicitor 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 solicitor market.

Where they agree

The points of near-consensus in Solicitor.

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

  • Names a specific provider: 6.7% across all three models.
  • Suggests a DIY approach first: 6.7%–20% across all three (a 13-point spread).
  • Mentions case studies or portfolio: 0%–13.3% across all three (a 13-point spread).
  • Warns about red flags or scams: 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 100% of questions) and least consistently on "asks a clarifying question" (33.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for solicitor are recommends hiring a professional (73.3% on average), gives selection criteria (51.1%) and asks a clarifying question (37.8%); the rarest are mentions case studies or portfolio (6.7%), names a specific provider (6.7%) and warns about red flags or scams (13.3%). 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: 73.3% on average (ChatGPT 86.7%, Claude 80%, Gemini 53.3%) — a 33-point spread.
  • Gives selection criteria: 51.1% on average (ChatGPT 46.7%, Claude 73.3%, Gemini 33.3%) — a 40-point spread.
  • Asks a clarifying question: 37.8% on average (ChatGPT 60%, Claude 53.3%, Gemini 0%) — a 60-point spread.
  • Mentions local proximity: 31.1% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 20%) — a 27-point spread.
  • Gives price or cost information: 28.9% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 46.7%) — a 33-point spread.
  • Recommends multiple quotes: 26.7% on average (ChatGPT 26.7%, Claude 53.3%, Gemini 0%) — a 53-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 20%, Claude 33.3%, Gemini 6.7%) — a 27-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to check reviews: 13.3% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 0%) — a 27-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 6.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Mentions case studies or portfolio: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the solicitor buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the solicitor buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 13.3% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 13.3%.

On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 26.7%. The single least-reproduced protective signal for solicitor is "tells the buyer to check reviews" at 13.3% 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 Solicitor providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 solicitor answers, a specific provider was named in 6.7% 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 solicitor: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Solicitor questions cover.

The 15 questions behind every percentage on this page were drawn from real solicitor (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 solicitor 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 solicitor 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 →