Share of Model (SoM) Benchmark: Empirical Visibility Across 5 Foundation Models

As consumer and enterprise decision-making migrates from search engines to Generative AI systems, traditional search volume metrics (Search Share) have ceased to reflect actual brand reach.

Over 50% of B2B and B2C procurement journeys now begin with an LLM dialog interface. In this paper, Cocco Applied Research presents the findings of our 500,000-query longitudinal study measuring Share of Model (SoM) across the five dominant foundation platforms: OpenAI ChatGPT (GPT-5 & Canvas), Google Gemini 3, Anthropic Claude 5 (Artifacts & Sonnet), Perplexity AI, and Microsoft Copilot.

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Traditional Search Share measures ranking in an inverted index. In contrast, Share of Model evaluates the probabilistic likelihood $P(Brand \mid Context, Intent)$ of a brand being retrieved, synthesized, and favorably recommended by an autoregressive neural network.

$$\text{SoM}{\text{Brand}} = \frac{\sum{i=1}^{N} w_i \cdot \mathbb{I}(\text{Brand Recommended}i)}{\sum{i=1}^{N} w_i \cdot \sum_{k} \mathbb{I}(\text{Competitor}_k \text{ Recommended}_i)}$$

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2.1 The Native UI vs. Developer API Divergence

Our multi-platform telemetry identified a systematic 41.8% divergence between responses generated via public developer APIs and those observed inside live consumer web and workspace interfaces.

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Critical Insight: Brands measuring their AI visibility solely through API scraping are making strategic capital allocations based on inaccurate synthetic approximations.