What Is Share of Model? The AI Visibility Metric Every Brand Needs to Track

What Share of Model actually measures

Share of Model is a metric that measures how often your brand appears in AI-generated answers across the major AI platforms. Specifically, it tracks what percentage of the queries relevant to your category produce an AI response that mentions or cites your brand, calculated across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude. Share of Model is to AI search what share of voice is to traditional media: a measure of how present your brand is in the conversations that matter, relative to the total opportunity.

The reason this metric exists is that traditional search analytics cannot capture AI visibility. Google Search Console shows impressions and clicks from organic search results. It does not show whether your brand appears in an AI Overview. It does not show whether ChatGPT recommends you when a prospect asks for the best provider in your category. It does not show whether Perplexity cites your research when a buyer is evaluating options. Share of Model fills that gap by measuring the surface that traditional analytics misses entirely.

As of 2026, the majority of B2B brands have a Share of Model score of zero or near zero for their most commercially valuable queries. Not because AI tools are ignoring them, but because they have never measured it and therefore have never optimised for it. The brands building Share of Model now are establishing positions that will compound for years, because the signals that drive AI citation take time to build and are difficult for competitors to replicate quickly once established.

How Share of Model is calculated

Calculating Share of Model begins with defining a query set: a list of twenty to one hundred queries representing the questions your target buyers ask AI tools when researching your category. The query set should span several types: definitional queries, tactical queries, comparison queries, and brand queries. Each type reveals a different dimension of your AI visibility.

Each query is submitted to each AI platform being tracked. For each response, three things are recorded: whether the brand is mentioned by name in the answer text, whether the brand URL is cited as a source, and where in the response the mention or citation appears. A mention in the first sentence of an AI response carries significantly more weight than a mention in a footnote, which is why position scoring matters alongside presence scoring.

The raw data produces several aggregate metrics. Citation rate is the percentage of queries where a brand URL appears as a cited source. Mention rate is the percentage where the brand name appears in the answer text. Share of Model score is typically calculated as a weighted combination of both, with citations weighted more heavily because they drive referral traffic and carry stronger authority signals.

Competitive Share of Model adds the most strategic value. By recording which other brands appear across the same query set, a business can see exactly which queries it is winning, which it is losing, and which competitors are taking the positions it wants. This turns Share of Model from an abstract metric into a competitive intelligence tool that directly informs content and authority-building strategy.

Why Share of Model matters more than search rankings in 2026

In 2022, a brand ranking in the top three organic positions on Google for a high-intent B2B query captured a meaningful percentage of clicks from that query. In 2026, many of those same queries trigger AI Overviews that answer the question directly on the results page. The user gets the answer without clicking through to any source. Click-through rates on AI Overview queries have dropped significantly as a result.

This means ranking well on Google is increasingly necessary but no longer sufficient for capturing buyer attention. A brand can rank first organically and still lose the buyer if the AI Overview recommends a competitor. The ranking is still valuable, but the AI layer above it determines whether the brand is visible at the moment the buyer is forming their shortlist.

For platforms like Perplexity and ChatGPT, the organic ranking dynamic does not apply at all. These platforms have their own indexes and their own authority models. A brand with excellent Google rankings but weak entity signals, no Wikidata record, and minimal Reddit or LinkedIn presence will consistently underperform in Perplexity and ChatGPT citations regardless of its Google position. Share of Model measures the visibility layer that search rankings cannot capture.

The buyers who use AI tools for research are also often the most valuable buyers. Perplexity and ChatGPT users skew toward senior professionals who want synthesised, sourced answers rather than a list of links to evaluate. Share of Model measures your brand presence in exactly these high-value decision moments.

The five platforms that make up a complete Share of Model measurement

ChatGPT is the highest-volume AI platform as of 2026 with the broadest user base. In browse mode, ChatGPT uses Bing as its primary index and weights authoritative content and Wikipedia presence heavily. Standard mode responses draw from training data. Both modes matter for Share of Model measurement because a brand can appear in training-data responses without being cited in browse mode, and vice versa.

Perplexity has the most research-oriented user base and always shows its citations, making it the most transparent platform to measure. Perplexity uses its own crawler with a strong recency bias and a proprietary re-ranking model. Brands that invest in Perplexity-specific GEO signals typically see the fastest Share of Model improvements on this platform.

Google AI Overviews reach the largest total audience because they appear inside Google Search. However, AI Overviews measurement is the most complex because their appearance is query-dependent and changes frequently. The good news is that AI Overviews optimisation is effectively standard SEO, so brands with strong Google rankings have a natural advantage here.

Gemini standalone uses Google infrastructure and is deeply integrated with Google Knowledge Graph, meaning entity-level optimisation has a stronger effect on Gemini citations than on most other platforms. Claude with web search uses Brave Search as its index, which has different ranking signals from both Google and Bing. Submitting your sitemap to Brave Webmaster Tools is the first platform-specific step for Claude Share of Model.

How to build Share of Model over time

The fastest Share of Model gains come from fixing the most fundamental gaps first. For most brands, the gap hierarchy is: entity verification, then content structure, then authority signals, then topical coverage depth. Wikidata entity creation, structural content updates, and FAQ additions can produce measurable improvements within four to eight weeks. Domain authority and topical cluster development compound over months.

Entity verification is the highest-leverage single action for brands with no Wikidata record. Creating a Wikidata Q-item with accurate properties makes your brand verifiable as a real entity across all AI platforms simultaneously. AI tools that can verify your entity via structured knowledge sources are significantly more likely to cite you than tools that can only rely on web content for entity resolution.

Content structure improvements produce Share of Model gains without requiring domain authority increases. Adding a direct answer paragraph at the top of key pages, restructuring long paragraphs into shorter extractable blocks, and expanding FAQ sections with question-format headings all improve the chance that existing content is extracted into AI responses.

Third-party mentions are the hardest Share of Model signal to acquire but the most durable. When your brand appears in Reddit discussions, LinkedIn articles, industry publications, and editorial mentions, AI tools learn to associate you with your category through the frequency and consistency of those co-mentions. Each mention contributes to the co-citation network that underlies sustained Share of Model growth.

Setting up your first Share of Model baseline

Before any GEO work can be evaluated, a baseline measurement is needed. Without a baseline, there is no way to determine whether Share of Model is improving, plateauing, or declining in response to what you are doing.

Start with twenty queries covering your four query types: five definitional queries about your category, five tactical queries, five comparison queries, and five branded queries. Run each on ChatGPT, Perplexity, and Google AI Overviews at minimum. Record the date, the exact query, whether your brand is mentioned, whether your URL is cited, and where in the response your brand appears.

This baseline takes two to three hours the first time. After that, monthly spot checks on your top ten queries take thirty minutes. The compound value of consistent measurement is that patterns become visible over time: which query types you are winning, which platforms your GEO work is affecting first, and which competitors are gaining or losing positions alongside you. Share of Model measured consistently over twelve months becomes one of the most powerful strategic planning inputs available to any brand building AI visibility.