LLM Share of Voice

Large language models now write the answers your buyers read — and each one has a different view of your brand. LLM share of voice measures how often those models name, recommend, and cite you across the questions that decide your sales, and compares it against your competitors. It's the metric for who actually owns the conversation in AI.

What Is LLM Share of Voice?

LLM share of voice is the percentage of LLM-generated answers in your category where your brand appears — mentioned, recommended, or cited — measured against the total and against your competitors. It extends AI share of voice across the full family of language models: ChatGPT, Perplexity, Gemini, Claude, and Google AI.

The three counts matter independently:

• Mention share — how often the models name you.
• Recommendation share — how often they suggest you as an option.
• Citation share — how often they use your website as a source.

Different models weigh sources differently, so your share will differ across them. That variance is information, not noise.

Why LLM Share of Voice Matters

The shift from "optimize for AI search" to "measure AI search performance" has made share-of-voice-style metrics the new KPI:

1. It quantifies the market. "We own 12% of LLM answers in our category" is a number your whole company understands. It's measurable, comparable, and trendable.
2. It's becoming a product category. HubSpot's AEO product ships competitor share of voice as a core capability. When the majors productize a metric, the search demand follows.
3. It reveals who's actually winning. Visibility tells you you're in the room. Share of voice tells you how much of the room is yours — and who took the rest.

How to Measure LLM Share of Voice

The method is consistent across all measurement tools:

1. Define your category questions. The real buying questions customers ask LLMs in your space.
2. Ask the models. ChatGPT, Perplexity, Gemini, and Google AI answer each with live browsing.
3. Count your appearances. Mention, recommendation, or citation per question.
4. Count competitors'. The same for every brand the models name.
5. Compute the shares. Your appearances ÷ total appearances, per model and overall.

Gerush automates this across all the major engines and produces a per-model share breakdown you can trend monthly.

Reading Your LLM Share of Voice Report

Three views drive action:

• Per-model variance. If ChatGPT recommends you but Gemini never mentions you, you have an entity or coverage gap in the sources Gemini trusts. Find out which sources Gemini cites in your category.
• Per-question zeros. Questions where no model names you are your biggest untapped claims on the conversation.
• Competitor source maps. The pages driving your competitors' share — the roundups, reviews, and directories you should be in.

LLM Share of Voice vs Related Metrics

Use visibility to confirm presence, citations to understand the mechanics, and share of voice to know the competitive score.

How to Grow Your LLM Share of Voice

1. Win per-question. Publish direct answers to your zero-share questions. See AI search optimization.
2. Earn corroboration. LLMs recommend brands that appear in sources they trust — comparison roundups, review sites, directories. This is the heaviest lever. See how to get cited by AI.
3. Fix per-model gaps. If one model under-weights you, study the sources it cites and get into them.
4. Re-measure monthly. Share is a trend. Track it and prove the movement.

Start Measuring Your LLM Share of Voice

Find out how much of the language-model conversation belongs to your brand. Run your category's questions across ChatGPT, Perplexity, Gemini, and Google AI — and get your share of voice, per model, per question.

Measure your LLM share of voice → — see who owns the conversation.

LLM Share of Voice FAQ

Which models does LLM share of voice cover? ChatGPT, Perplexity, Gemini, and Google AI — the models your customers actually use. Each retrieves and cites the web differently, so you get a per-model share breakdown rather than one blended number.

Why does my share differ between models? Models trust different sources. ChatGPT and Perplexity lean heavily on roundups and editorial; Gemini and Google AI weight their own index and structured data. If your share varies widely, it points to where you're strong or missing on each — a very useful diagnostic.

Is LLM share of voice the same as AI share of voice? AI share of voice is the umbrella term across all AI search. LLM share of voice is the same measure focused on the large language models themselves. If your brand appears in product-focused AI surfaces beyond LLMs, use AI share of voice as your headline metric and LLM share for the model-level detail.