AI SEO Statistics: Tax Law (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same tax law 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 62.5% (Gemini), a 20-point gap on an identical question set.

Across the 40 tax law answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 15%, averaging 522 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 tax law answers it produced, Claude recommended hiring a professional in 75% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 22.5% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 316 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 tax law answers it produced, Gemini recommended hiring a professional in 62.5% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 10%, averaging 263 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 7.5%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a tax law buyer to a professional (82.5%) and Gemini the least (62.5%). ChatGPT produced the longest answers, at 522 words on average. Specific providers were named most often by Claude (5%) — even there, roughly one answer in 20 carried a name.

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

• Names a specific provider: 2.5%–5% across all three (a 3-point spread).
• Tells the buyer to check reviews: 2.5%–10% across all three (a 8-point spread).
• Mentions case studies or portfolio: 5%–12.5% across all three (a 8-point spread).
• Recommends multiple quotes: 0%–7.5% across all three (a 8-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 90% of questions) and least consistently on "asks a clarifying question" (17.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the tax law buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 7.5% of answers on average. Verifying credentials or certifications appeared in 15%. Warning about red flags or scams appeared in 12.5%.

On structuring the decision, a selection-criteria checklist showed up in 35.8% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for tax law 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.

The 40 questions behind every percentage on this page were drawn from real tax law (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 tax law 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.