How to Calculate Share of Model: Formula, Sample Design, and Tracking

By maxaeo.ai ｜ Published 2026-10-03 ｜ Updated 2026-10-03

If you need to know how to calculate share of model, divide your brand’s eligible mentions—or weighted visibility points—by the equivalent total for all brands in a controlled sample, then multiply by 100. The arithmetic is easy. The difficult part is defining prompts, competitors, AI engines, and counting rules consistently.

What Is Share of Model?

Share of Model, or SoM, is a brand’s portion of competitive visibility within AI-generated answers. It measures how much of the category conversation your brand receives when buyers ask ChatGPT, Gemini, Perplexity, and other answer engines relevant questions.

Current definitions commonly use one of two denominators: total AI responses or total category-brand mentions. Those methods answer different questions and should not be reported as interchangeable. Published methodologies also differ on prompt selection, deduplication, and position weighting. (getmentioned.com)

• Mention rate asks: In what percentage of eligible answers did the brand appear?
• Competitive Share of Model asks: What percentage of all measured brand visibility belonged to the brand?
• Citation share asks: What percentage of relevant source citations pointed to the brand’s domain?
• Recommendation rate asks: How often was the brand explicitly suggested as an option?

A reliable dashboard should show these separately rather than compressing them into one opaque score.

How to Calculate Share of Model With the Right Formula

The most defensible competitive formula is:

Share of Model (%) =
Your brand’s visibility points
÷
Visibility points for all measured brands
× 100

For an unweighted calculation, one qualifying brand appearance equals one point:

Unweighted SoM (%) =
Your brand mentions
÷
Total mentions of all category brands
× 100

You can also calculate response-level presence:

Mention rate (%) =
Eligible responses mentioning your brand
÷
All eligible responses
× 100

The distinction matters. If 40 of 100 answers mention your company, your mention rate is 40%. But if those answers contain 200 total competitor mentions and your company earns 40, its competitive Share of Model is only 20%.

For a broader cross-engine scoring method, see the LLM share-of-voice formula and position-weighting framework.

Which Sampling Framework Produces Comparable Results?

A valid sample holds the measurement environment stable. Before collecting answers, document a five-field SoM Measurement Contract: prompt universe, engine set, market, counting rule, and reporting window. This original framework prevents teams from treating a changed sample as genuine performance growth.

1. Define the decision category. Specify the product category, audience, country, and language. “CRM software” and “CRM for US healthcare startups” represent different markets.

2. Build prompt strata. Include discovery, comparison, use-case, alternative, integration, and purchase-validation prompts. Use AI search intent mapping to avoid overloading the panel with “best tools” questions.

3. Freeze the competitive set. Decide whether the denominator includes named competitors only or every relevant brand detected. Record additions separately.

4. Select engines and runs. Test the same prompts across each chosen platform. If one engine receives more observations, normalize its weight so it does not dominate the composite score.

5. Set counting rules. Define aliases, misspellings, parent-company names, duplicate mentions, negative mentions, citations, and non-recommendation references before analysis.

6. Preserve a baseline panel. Keep a fixed core set for trend reporting. Test new prompts in an experimental panel until they can be incorporated without rewriting historical comparisons.

Worked Example: A 120-Answer SaaS Panel

Consider an illustrative SaaS measurement panel containing 20 prompts, three AI engines, and two runs per prompt. That produces 120 answers. Eight answers name no relevant vendor, leaving 112 eligible responses.

The 27.1% score uses a disclosed prominence model: 1.0 point for the first brand presented, 0.7 for second, 0.5 for third, and 0.3 for later positions. These weights are analytical choices, not universal standards. Changing them creates a new metric version.

This example reveals a useful diagnostic: the brand appears in 34.8% of eligible answers but captures only 16.7% of citations. The visibility gap is therefore not simply “get mentioned more.” The brand also needs stronger sources that AI systems can retrieve and cite.

How Should Share of Model Be Automated?

Automation should rerun a controlled prompt panel, retain the original answers, extract brand-level observations, and update trends without silently changing the methodology. Every record should include the prompt, engine, run date, market, answer text, detected brands, recommendation position, sentiment, and cited sources.

Use version labels whenever you change:

• Prompt wording or intent mix
• Competitor inclusion rules
• AI engines or model configurations
• Geographic or language settings
• Position weights and eligibility filters
• Brand aliases or entity-matching logic

MaxAEO monitors brand mentions, recommendations, sentiment, competitive position, and citations across eight AI engines on a daily cadence. It also stores raw AI answers for traceability and compares brand performance with competitors by mention frequency, ranking position, and citation source.

Teams can start with a free AI visibility diagnostic before establishing ongoing monitoring. No internal revenue data, customer lists, or technical installation are required for the basic diagnostic.

How Should Teams Interpret the Score?

Share of Model is a visibility metric, not market share, revenue attribution, or proof that an optimization caused a sale. Interpret it alongside recommendation rate, sentiment, factual accuracy, citation share, and downstream traffic or conversions.

Use the following decision sequence:

• Low mention rate: Improve topic and prompt coverage.
• High mentions but low recommendations: Clarify positioning, use cases, and evidence.
• High recommendations but low citation share: Strengthen citable product pages, documentation, research, and third-party coverage.
• Strong overall score but weak priority prompts: Segment performance by buyer intent rather than relying on the average.
• Sudden score movement: Check prompt, competitor, and engine versions before attributing the change to marketing.

A broader framework for measuring brand visibility in LLMs can connect these diagnostic metrics, while an AI search performance reporting framework helps translate them into executive reporting.

Frequently Asked Questions

What is the simplest way to calculate Share of Model?

Divide your brand’s mentions by all relevant brand mentions in the same prompt-and-engine sample, then multiply by 100. Publish the denominator and counting rules with the score.

Should answers with no brands be included?

Include them when calculating mention or recommendation rate. Exclude them from a mention-share denominator because they contribute no brand points. Report the number excluded so readers can audit the calculation.

How many prompts are required?

There is no universal minimum. Start with enough prompts to represent major buyer intents, industries, use cases, and funnel stages. Coverage quality matters more than an arbitrary prompt count.

Can Share of Model scores from different tools be compared?

Usually not directly. Tools may use different prompts, engines, competitors, markets, run frequencies, and weighting systems. Compare trends within one documented methodology unless both datasets use identical rules.

How often should Share of Model be measured?

Daily or weekly monitoring can reveal direction, but decisions should use multi-period trends rather than one answer or screenshot. Preserve the same baseline panel so changes remain interpretable.