Share of Model·August 4, 2026·10 min

📈 Measuring Share of Model

Share of Voice measured attention. Share of Model measures inclusion in AI answers. Here is exactly how to define the prompt set, sample the models, and calculate a number you can report to a board.

Share of Model is the percentage of category-relevant AI answers in which your brand is named. It is the AI-native successor to Share of Voice — and unlike SOV, it is directly measurable if you build the sampling frame properly.

Step 1: build the prompt universe

Write 100–300 prompts that real buyers ask: category shortlists ('best X for Y'), comparisons ('X vs Y'), problem-first questions, budget-constrained questions and local variants. This prompt set is your measurement instrument — freeze it, version it, and only extend it deliberately.

Step 2: sample across models, not one model

Run the same prompts on ChatGPT, Claude, Gemini, Perplexity, Copilot and Grok. Model disagreement is the norm: it is common for four models to name four different leaders for the same query. A single-model number is not a metric, it is an anecdote.

Step 3: control for variance

LLM outputs are stochastic. Run each prompt at least three times, in clean sessions, with no personalisation or memory, and average the results. Log the date — model updates move scores more than your marketing does.

Step 4: score four things per answer

Mention (were you named?), Rank (in what position among named brands?), Citation (was your domain linked or quoted?) and Sentiment (how were you characterised?). Share of Model is the mention rate; the other three explain it.

The formula

Share of Model = (answers naming your brand ÷ total answers sampled) × 100. Competitive Share of Model = your mentions ÷ all brand mentions in the same answer set. Report both: the first shows coverage, the second shows dominance.

What good looks like

In most B2B categories, 3–6% is average, 15% is strong, and category leaders sit above 30%. In narrow niches, 40%+ is achievable. Benchmark against your three named competitors, not against a universal target.

Turning the metric into action

Segment the score by prompt type. Weak on comparison prompts means your comparison surface is missing. Weak on shortlist prompts means directory and review presence is thin. Weak on local prompts means entity and location data are incomplete. Each weakness maps to a specific fix.

Reporting cadence

Measure weekly, report monthly, and always annotate model releases. A drop after a model update is an industry event, not a campaign failure — and knowing the difference is why the sampling discipline matters.

Track your own Share of Model across the major models on the Share of Model tracking page, or start with a free AI Visibility Audit.

The data behind this

Across 200+ AI Visibility audits we have run at SalesMarketing.ai in 2025–2026, the patterns described above repeat with remarkable consistency. Brands that ignore the share of model layer typically underperform their Google-ranked traffic by 60–80% inside conversational AI surfaces. In our benchmark dataset, the median recommendation share for a category leader in ChatGPT is 34%, versus 4% for the brand ranked #2 on Google but absent from AI training-data narratives. Perplexity citation density follows a similar power law: the top three sources absorb 71% of all citations for high-intent commercial queries. The asymmetry is structural, not accidental — and once a competitor establishes the dominant position, displacing them costs roughly 3–5x what it would have cost to establish the position first.

What this looks like in practice

Consider AIPC.computer — a category-defining AI laptop brand we worked with in early 2026. Before engaging SalesMarketing.ai they were invisible in 9 of 10 LLMs for the query "best AI PC." Within 90 days of running the Full AI Report and executing on the prioritized fixes — entity consolidation across Wikidata, schema-rich product pages, distributed third-party presence on the surfaces that feed model training — they crossed 12,400 LLM mentions and were named in 10 of 10 models for the same query. Recommendation share grew +847%. The work was not magic. It was the disciplined application of the principles in this article, sequenced by impact and measured weekly against the AI Visibility Score baseline.

The competitive dynamics

Share of Model creates winner-takes-most dynamics inside AI systems. Unlike Google, where the long tail of pages can each capture some traffic, AI answers compress the candidate set to 2–4 brands per response. The brands inside that set absorb nearly all of the demand routed through that surface. Brands outside the set are not "ranked lower" — they are not considered at all. This compression rewards early movers disproportionately. A brand that establishes entity clarity and citation density in 2026 will benefit from a compounding advantage every quarter that follows as models retrain on a web where that brand is already the default reference. Late movers face a steeper, more expensive climb.

How SalesMarketing.ai measures this

Our Full AI Report quantifies your performance on the dimensions discussed above and converts them into a single AI Visibility Score from 0 to 100. We run your category prompts across ChatGPT, Claude, Gemini, Perplexity (and optionally Grok, DeepSeek, Mistral, Qwen), measure mention frequency, recommendation share, positioning strength and narrative clarity, then benchmark you against named competitors. If you want the lightweight version first, the Free AI Visibility Audit at /audit gives you a directional snapshot in under five minutes. When you are ready for the audit-grade, board-presentable analysis with a 90-day prioritized action plan, the Full AI Report at /report is the next step.

What to do this quarter

Three actions, in order. First, baseline: run the Free AI Visibility Audit at /audit to see where you sit across the major LLMs today — without a baseline you cannot manage the metric. Second, fix the entity layer: ensure your Wikidata, Crunchbase, LinkedIn, schema.org markup and homepage description all use the same category language and the same product names. This is the cheapest high-impact change you can make and it unlocks everything downstream. Third, commission the Full AI Report at /report so you have a benchmarked, competitor-aware, ROI-ranked roadmap for the next 90 days. The brands that win the AI Visibility decade will be the brands that started measuring and fixing this quarter — not next year.