Generative Engine Optimization Updated 2026-07-04

Share of Model

Share of Model is the proportion of citations or recommendations your brand receives within responses generated by a specific LLM or AI model, measuring relative visibility within that system's output.

Definition

Share of Model isolates performance within a single LLM rather than aggregating across platforms. For example, your brand might have 35% share of voice within ChatGPT responses but only 15% within Perplexity, due to differences in training data, retrieval methods, and inference tuning. Measurement requires platform-specific monitoring and comparison against competitor presence within responses from identical models. This metric reveals which systems favor or deprioritize your content and brand signals.

Organizations track Share of Model to understand platform-specific performance, identify retraining effects when systems update, and target optimization efforts where they yield highest impact. Different models may favor different attributes: some reward recency, others domain authority, and others novelty or specific data formats. Share of Model analysis informs decisions about which platforms warrant content investment and where competitive gaps exist.

Why it matters for AI visibility

Different AI platforms reach different audience segments and carry different influence on customer decisions. Understanding your Share of Model within each system reveals where your visibility is strong and where you're losing to competitors. This informs resource allocation and helps you target optimization efforts where competitive positioning is weakest or where platform influence is highest.

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