How do I compare visibility across ChatGPT, Gemini, and Claude?

To compare visibility across ChatGPT, Gemini, and Claude, first define what you mean by “visibility,” because it can mean a few different things:

1. Answer visibility: whether your content/brand appears in the model’s responses.
2. Ranking/mention frequency: how often each model mentions you versus competitors.
3. Search or grounding visibility: whether the model surfaces your site when using web/search tools.
4. Citation visibility: whether your brand is explicitly cited as a source.

A practical way to compare them

1) Create a standardized prompt set

Use the same prompts in all three models. Example categories:

• Discovery: “What are the best tools for X?”
• Comparison: “Compare A, B, and C.”
• Recommendation: “Which companies solve Y best?”
• Brand-specific: “What do you know about [your brand]?”
• Intent-based: “What’s the best option for [use case]?”

Keep:

• prompt wording identical
• location/language consistent
• model settings as similar as possible

2) Run the prompts repeatedly

Because outputs vary, do multiple runs:

• 10–30 prompts per category
• repeat each prompt 3–5 times if possible
• test across different dates/times

3) Score visibility with a simple rubric

For each response, track:

• Mentioned? yes/no
• Position: first mention, top 3, bottom, not mentioned
• Sentiment: positive / neutral / negative
• Accuracy: correct / partial / incorrect
• Source/citation present? yes/no
• Recommendation strength: strong / weak / none

You can turn this into a numeric score, for example:

• Mentioned = 1 point
• In top 3 = 2 points
• Recommended = 3 points
• Cited = 1 point
• Positive sentiment = 1 point

Then compare totals across ChatGPT, Gemini, and Claude.

4) Track by use case

Visibility can differ by model and prompt type:

• ChatGPT may be stronger on conversational recall and broad recommendations.
• Gemini may surface more web-connected/Google-aligned results when grounded.
• Claude may be more cautious and less likely to hallucinate brand mentions.

So compare separately for:

• awareness queries
• category queries
• competitor comparisons
• factual queries
• purchase-intent queries

5) Use a benchmark table

Example structure:

Prompt	ChatGPT	Gemini	Claude
Best tools for X	Mentioned, #2	Mentioned, #1	Not mentioned
Compare A vs B	Neutral mention	Positive mention	Positive mention
What is [Brand]?	Accurate	Accurate	Partial

6) Include grounding/citation checks

If your goal is web visibility:

• ask each model to answer with sources
• note whether it cites your site
• check whether it uses current web info or only internal knowledge

7) Normalize for model differences

Don’t compare raw answers only. Consider:

• whether the model has browsing enabled
• what system/tooling is active
• model version
• region/language
• time of query

Best practice: build a “visibility audit”

A lightweight audit can include:

• prompt library
• repeated test runs
• scoring sheet
• competitor set
• date/model/version
• summary dashboard

If you want a more operational method

You can automate this with:

• a spreadsheet or database
• API calls to each model
• prompt batches
• a script to extract mentions/citations
• a scorecard per brand/topic

Suggested KPIs

• Share of mention
• Share of recommendation
• Top-3 appearance rate
• Citation rate
• Accuracy rate
• Sentiment score
• Competitor displacement rate

If you want, I can give you:

1. a ready-to-use prompt template set,
2. a Google Sheets scorecard, or
3. a Python script to compare visibility across the three models.