AI SEO Statistics: Ndis Provider (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same ndis provider questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 57.5% (ChatGPT) down to 40% (Gemini), a 18-point gap on an identical question set.

Across the 40 ndis provider answers it produced, ChatGPT recommended hiring a professional in 57.5% of them and suggested a DIY approach first 30% of the time. It named a specific provider in 2.5% of answers (about 0 distinct providers per answer) and included price or cost information 2.5% of the time. ChatGPT asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 27.5%, averaging 509 words per answer. On the remaining cues it told the buyer to check reviews in 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 42.5% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 ndis provider answers it produced, Claude recommended hiring a professional in 57.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 7.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 22.5%, averaging 305 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 ndis provider answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 15% of answers (about 0.5 distinct providers per answer) and included price or cost information 17.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 7.5%, averaging 263 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 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a ndis provider buyer to a professional (57.5%) and Gemini the least (40%). ChatGPT produced the longest answers, at 509 words on average. Specific providers were named most often by Gemini (15%) — even there, roughly one answer in 7 carried a name.

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

• Tells the buyer to check reviews: 5%–10% across all three (a 5-point spread).
• Mentions case studies or portfolio: 0%–5% across all three (a 5-point spread).
• Mentions local proximity: 20%–25% across all three (a 5-point spread).
• Warns about red flags or scams: 7.5%–15% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 92.5% of questions) and least consistently on "asks a clarifying question" (12.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the ndis provider 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 19.2%. Warning about red flags or scams appeared in 10%.

On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for ndis provider is "recommends multiple quotes" at 6.7% 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 ndis provider (healthcare 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 ndis provider 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.