ChatGPT and Gemini Rarely Agree on Top Local Businesses, Study Finds

AI visibility is not a single score that a business can measure once and treat as settled. A cross-engine study of local-service searches found that ChatGPT and Gemini named the same top business in only 4.2% of identical queries . For small businesses trying to be discovered through AI assistants, that gap means a strong result in one engine may say very little about how another assistant presents the market. The research, published by Steady Demand in its AI Citation Ledger , examined 1,487 queries across 50 U.S. metropolitan areas and 10 service verticals. It focused on prompts such as “best plumber near me,” tracking the businesses named and the sources used to ground responses. Its central finding is practical: AI-driven discovery is fragmented by engine, source mix, location, and category . That does not prove that AI responses drive more leads than conventional local search. The study measures citations and top-name outcomes, not conversions or overall ranking quality. But it provides a useful baseline for marketers because it shows why checking a brand in one AI assistant is not enough to understand its broader AI visibility. What the cross-engine data shows The study compared how Gemini and ChatGPT answered the same local-business prompts. Their differences extended beyond the final recommendation. The systems often drew on different source ecosystems, which helps explain why they surface different businesses. Measure Gemini ChatGPT Exact top-business match between engines 4.2% of identical queries produced the same top business Typical citation mix About 60% of citations were business websites More reliance on Reddit and traditional directories Overlap in cited domains About 8% overlap Repeated-query source alignment About 40% alignment, described as grounding drift Top-result repeatability benchmark About 7% top-match stability in AI-generated results Not specified separately in the supplied research The contrast with Google’s local pack is notable. In th