How to Test Your Brand Across Multiple LLMs

Most brands are flying blind when it comes to AI visibility. They assume that if their SEO is decent, AI engines will mention them too.

That assumption is costing visibility—and customers.

The only way to truly know how your brand is represented across large language models (LLMs) is to test it directly.

Here’s how.

Why You Can’t Rely on One Model

Each AI model has its own dataset, update cycle, and reasoning style.

For example:

ChatGPT (GPT-4o) may rely more on past training data and Reinforcement Learning from Human Feedback (RLHF)
Claude emphasizes helpfulness and sometimes downranks less-known brands
Gemini pulls in web snippets in real time
Grok (xAI) might prioritize different kinds of technical sources or social media

If you're only testing one model, you’re only seeing a fraction of the picture.

Step-by-Step: Multi-Model Brand Testing

Choose your base prompts
- “What is [brand]?”
- “Best tools for [your industry]”
- “Compare [you] vs [competitor]”
Run the same prompt on multiple engines
Tools like PromptSeed do this instantly with side-by-side comparisons.
Extract mentions and context
Who got mentioned? How were they described? Were you skipped entirely?
Track model-specific patterns
Some models may consistently favor certain brands or sources. Use this to guide content updates.

We recommend setting up recurring tests to track progress over time.
Read more on how to measure GEO performance to know what success actually looks like.

Final Word

You can't optimize what you don’t track.
Testing across LLMs is the new equivalent of running site audits or rank tracking.

If you're not doing it, you're already behind.

Want to know how your brand is actually showing up? Try PromptSeed.