---
title: 'The 2026 AEO Benchmark: Does ChatGPT Give the Same Recommendations to Everyone?'
subtitle: 'We ran 1,000 multi-engine prompt iterations across Gemini, Claude, and Perplexity to measure recommendation variance, confidence intervals, and engine bias.'
date: '2026-08-06'
author: 'Spotlight Links Data Lab'
categories:
  - AEO Auditing
---

## Measuring AI Answer Variance

A common misconception in digital marketing is that conversational AI search engines provide fixed static answers. In reality, Large Language Models sample answers probabilistically based on temperature settings and real-time retrieval grounding.

## Statistical Methodology: Multi-Run Probing

To measure true recommendation rates, Spotlight Links utilizes multi-repeat probing (2 to 5 repeats per prompt) and calculates 95% Wilson Score confidence bounds. This ensures that reported recommendation percentages reflect true market distributions rather than single-run anomalies.

> Empirical Variance Breakdown Across AI Models:
>
> - **Google Gemini:** Highest consistency for local map-grounded intent queries (88% consistency).
> - **Perplexity AI:** Highly dynamic, sampling live news and directory citations in real time.
> - **Claude 3.5:** Strongest reasoning alignment for complex, multi-attribute customer requests.