Share of model measures the percentage of relevant AI-generated responses in a given category that mention or cite a specific brand. It's the artificial intelligence era's answer to share of voice, the long-standing public relations metric that tracked how often a brand appeared in traditional media coverage relative to competitors. As AI platforms like ChatGPT, Claude, Perplexity, and Google AI Overviews become primary research tools for buyers and consumers, share of model is emerging as one of the most important metrics in modern marketing and communications.

Why Is Share of Model Becoming Critical for Brands?

The shift is driven by fundamental changes in how people research products and services. A meaningful share of buyer journeys now begins inside an AI assistant rather than a traditional search engine. When an AI summarizes "the top five vendors" in a category, the brands named effectively become the shortlist. Brands not mentioned must work harder, longer, and at higher cost to enter the conversation later in the buying process.

The stakes are particularly high in emerging categories. In the cryptocurrency space, for example, Coinbase and Kraken together account for 22% of all crypto-category AI citations across more than 65 tracked queries, more than three times the citation share of the next-largest U.S. exchange. This concentration of visibility directly shapes which platforms new buyers default to when they ask AI assistants where to buy Bitcoin or which exchange is safest.

"Crypto is the cleanest example we've seen of an entire retail-finance category being formed inside AI answers in less than five years. There's no Fidelity-Schwab-Vanguard incumbent set the next 50 million American crypto buyers grew up trusting. The brand AI surfaces when someone asks ChatGPT 'where do I buy Bitcoin' or 'is Robinhood safe for crypto' is the brand they default to," stated Ronn Torossian, Founder of 5W.

Ronn Torossian, Founder of 5W

How Do You Calculate Share of Model?

Measuring share of model involves a structured four-step process that mirrors how brands have historically measured earned media:

• Define the prompt set: Identify the actual queries that buyers, customers, and partners run in AI platforms within your category. Prompt sets typically include 50 to 200 prompts spanning category definition, brand discovery, comparison, recommendation, pricing, and reputation queries.
• Run prompts across platforms: Execute the prompts on ChatGPT, Claude, Perplexity, and Google AI Overviews at minimum. Each platform produces different answers using different underlying models and retrieval systems.
• Code the responses: For each AI response, record whether your brand is mentioned, whether it is recommended or merely listed, whether it is described accurately, what other brands appear, and what sources are cited.
• Calculate the share: Calculate share of model as your brand's percentage of total mentions within the prompt set across the platforms in scope. You can calculate it overall, by platform, or by prompt category.

What matters more than the absolute number is the trajectory and the relative position to direct competitors. Single-digit share of model is typical for emerging brands or brands new to AI visibility work. A strong position for established brands in competitive categories ranges from 10 to 25 percent. Share of model at 25 percent or higher generally indicates category leadership in AI answers, comparable to category-leading share of voice in traditional media.

How Does Share of Model Differ From Traditional Share of Voice?

Share of voice measures presence in traditional and digital media coverage. Share of model measures presence in AI-generated answers. The two metrics overlap because authority signals in media coverage influence what AI systems cite, but they capture different surfaces.

A brand can have strong share of voice with significant earned media coverage and weak share of model if its content is not structured for AI retrieval, if its entity definition is unclear, or if the cited sources in AI answers happen to be different from the outlets the brand has earned coverage in. Conversely, a brand can have growing share of model and stable share of voice as AI engines pull from sources beyond the traditional media universe, including Reddit, YouTube transcripts, podcast transcripts, niche industry blogs, and structured data sources. The two metrics are complements, not substitutes. Modern measurement programs track both.

What Are the Key Benchmarks for Share of Model Performance?

Understanding where your brand stands requires context. In the cryptocurrency space, Gemini holds the number three position with 5.5% citation share, anchored by its New York Department of Financial Services trust-company status and the Winklevoss twins' personal brand. Robinhood Crypto ranks fourth at 5.0%, with AI answers now placing Robinhood ahead of Coinbase in true-beginner prompts following its 2025 spinout into a standalone SEC-compliant entity. BlackRock's IBIT Bitcoin ETF ranks fifth at 4.5% and dominates "Bitcoin ETF" prompts, building citation lock-in in 24 months at a consolidation pace closer to Vanguard's index-fund category formation than to typical ETF adoption.

The research also reveals structural shifts within categories. Hardware wallet citations are softening, with Ledger and Trezor still winning "best crypto wallet" prompts but losing "best way to store crypto" prompts to regulated-exchange custody answers. The post-FTX self-custody narrative is no longer the dominant AI citation frame.

How Can Brands Improve Their Share of Model?

Share of model improves through a combination of earned media in trusted publications, well-structured owned content that answers high-intent questions, accurate and consistent entity definition across the open web, authoritative backlinks, presence in sources that AI systems rely on such as Wikipedia and major business databases, and technical accessibility for AI crawlers.

For B2B brands specifically, the challenge is more nuanced. G2 controls 84% of the review citation market across major AI models, making profile accuracy and review freshness critical. When product positioning evolves, marketing teams must update not just websites and campaign creative but also directory profiles like G2. If your brand narrative is in all your creative but you forgot the G2 step, it does not matter when prospects go to search.

Most brands see meaningful share-of-model improvement within 90 to 180 days of beginning a coordinated Generative Engine Optimization program, with continued compounding gains over the following year as authority signals accumulate.

What Should Marketers Track to Monitor Share of Model?

Effective share of model measurement requires a three-layer view that turns the metric from a vanity number into an actionable diagnostic:

• Headline metric: Overall share of model across the prompt set, this period versus last period, with competitive context to show whether you are gaining or losing ground.
• Platform-level breakdown: How your brand performs on ChatGPT versus Claude versus Perplexity versus Google AI Overviews. Performance often varies meaningfully by platform, and understanding these differences reveals where to focus optimization efforts.
• Prompt-category breakdown: How your brand performs on category-definition prompts, comparison prompts, recommendation prompts, and reputation prompts. The pattern often points to where work is needed most urgently.

The urgency is real. Communications teams that adopt share of model measurement now will have several years of trend data when their CFOs eventually start asking about AI visibility, and several years of compounding improvement work behind them. Teams that wait will be measuring backwards.