Share of Model: The New Share of Voice for AI Search
Share of model is how often an AI engine names your brand versus your competitors across a set of buyer questions. It is the natural successor to share of voice: in a world where an AI answer names only two or three brands, the question is no longer how loud you are — it is whether the model reaches for you at all.
Why share of voice needed an update
Share of voice measured your slice of attention: your ads against all ads, your rankings against the SERP, your press against the category’s press. It assumed a crowded field where more presence meant more of the pie.
Generative engines break that assumption. When someone asks ChatGPT or Perplexity for a recommendation, the answer is short. Often it names a handful of brands and stops. There is no page two. Either the model reaches for you or it doesn’t — and “share of voice” across a hundred blue links doesn’t capture that. Share of model does.
If the concept of optimizing for AI answers is new to you, start with what GEO is; share of model is how you keep score once you’re doing it.
How to define share of model
Keep the definition simple and hold it steady. Two versions are useful:
- Presence share — the percentage of relevant answers that name your brand at all. If you run 30 prompts and appear in 12, your presence share is 40%.
- Competitive share — your mentions as a percentage of all brand mentions across those same answers. If the model names brands 50 times total across your prompt set and 10 of them are you, your competitive share is 20%.
Presence share tells you how visible you are. Competitive share tells you how visible you are relative to rivals — which is the number that mirrors classic share of voice.
How to calculate it, step by step
- Fix a prompt set. Use real buyer questions — category, comparison, problem, and recommendation prompts. Keep the set stable so cycles compare fairly.
- Run each prompt several times. Answers vary, so run each 3–5 times and treat the runs as your sample.
- Count mentions. For each answer, record whether your brand appears and which competitor brands appear.
- Do the math.
- Presence share = (answers naming you) ÷ (total answers)
- Competitive share = (your mentions) ÷ (all brand mentions)
- Segment. Break the number down by engine, by prompt type, and by the stage of the buying journey. An average across everything hides where you actually win and lose.
A worked shape of the output:
| Segment | Presence share | Competitive share |
|---|---|---|
| Category prompts | 45% | 22% |
| Comparison prompts | 30% | 15% |
| Recommendation prompts | 20% | 10% |
The numbers above are an illustrative layout, not a claim about any brand.
What share of model tells you to do
The value isn’t the single figure — it’s the gaps it exposes.
- Low presence share overall — the models don’t have enough clear, quotable material to reach for you. That is a content and AI SEO gap.
- Decent presence, low competitive share — you appear, but rivals appear more. Study what they publish and where they’re cited.
- Strong on category, weak on recommendation prompts — the model knows your topic but doesn’t yet trust you as the pick. That’s an entity and authority problem, closely tied to entity SEO and consistent schema markup.
- Uneven across engines — each engine sources differently, so you may need engine-specific work.
How to track it over time
Share of model is only useful as a trend. Run the full prompt set on a monthly cadence, log every result, and chart presence and competitive share per engine. Watch direction, not decimals — this is sampling a moving target, so a three-point monthly rise matters more than any single reading.
It pairs naturally with the other AI visibility metrics that actually matter, and with a clear method for measuring your brand’s visibility in ChatGPT as your data source.
A note on honesty
Share of model is a framework, not an official platform metric — no one publishes it for you. That’s fine, as long as you’re disciplined: one clear definition, one stable prompt set, one consistent counting method, tracked long enough to trust the trend. Precision theater helps no one; a well-kept directional number is worth far more than a fabricated exact figure.
Want your share of model measured against your real competitors and turned into a plan to raise it? Book an AI visibility audit and we’ll benchmark where you stand across the major engines.
Frequently asked questions
What is share of model?
Share of model is the percentage of relevant AI answers that name your brand, compared with the brands the model names for the same prompt set. It is the generative-search equivalent of share of voice.
How is share of model different from share of voice?
Share of voice measures your presence across ads, media, or search rankings. Share of model measures how often an AI engine actually names you in its synthesized answers to buyer questions — a single answer that may mention only two or three brands.
How do I calculate share of model?
Run a fixed set of buyer prompts, count how many answers name your brand, and divide by the total answers. To get competitive share, divide your mentions by all brand mentions across those same answers.
Is share of model an official metric?
No. It is a practical framework for measuring brand presence in AI answers, not a number any platform publishes. Define your method clearly and keep it consistent so the trend is meaningful.