The 2026 AEO Benchmark: Does ChatGPT Give the Same Recommendations to Everyone?
We ran 1,000 multi-engine prompt iterations across Gemini, Claude, and Perplexity to measure recommendation variance, confidence intervals, and engine bias.
Spotlight Links Data Lab · August 6, 2026 · 1 min read
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.