How do I compare what different AI engines say about my brand?
Run the same prompt through ChatGPT, Perplexity, Gemini, Claude, and DeepSeek, then log four things per engine: is your brand mentioned, where does it rank, what is the sentiment, and which source got cited. The five will disagree, and that is normal. Compare them side by side and the gaps show you exactly where to fix your visibility.
Why the same brand gets described five different ways
Each engine builds its answer from a different mix of sources, so the same brand lands differently. Four factors drive the split:
- Training data vs live retrieval. ChatGPT and Claude answer largely from what they learned during training, so they favor brands that were well documented when the model was built. Perplexity and Gemini run a live web search first, so they favor whatever ranks and reads well today.
- Freshness. A brand that launched or rebranded in the last year often shows up in the search-grounded engines months before it reaches the training-heavy ones.
- Source weighting. Perplexity leans on listicles and review sites. Gemini leans on Google's index. DeepSeek and Claude weight their training corpus. The cited source under each answer tells you which lever moved it.
- Prompt phrasing. Even one engine varies run to run. Small wording changes shift which brands surface.
A method for comparing engines side by side
You do not need a tool to start. Open all five engines, paste the identical prompt into each, and record the result in a simple grid. Track four columns per engine:
- Mentioned? Yes or no, and the rank if it is a list.
- Sentiment. Positive, neutral, or negative framing of your brand.
- Cited source. The URL the engine leaned on, if it shows one.
- Context. One line on how you were described, right or wrong.
Use the exact same prompt string in every engine, or the comparison is worthless. Run it two or three times per engine, since answers drift between runs. Do this monthly at minimum: AI answers shift as models retrain and as the pages they cite change, so a single snapshot goes stale within weeks.
One prompt, five engines: a worked example
Here is an illustrative run for a password manager tracking the brand 1Password against the prompt below.
What is the best password manager for a small team?
| Engine | Mentioned? | Sentiment | Cited source |
|---|---|---|---|
| ChatGPT | Yes, #1 | Positive | None (training data) |
| Perplexity | Yes, #3 | Neutral | PCMag listicle |
| Gemini | Yes, #2 | Positive | Google-indexed review |
| Claude | Yes, #1 | Positive | None (training data) |
| DeepSeek | No | - | - |
The same brand is the top pick in ChatGPT and Claude, a mid-list option in Perplexity, and absent in DeepSeek. That spread is the whole point. The training-heavy engines already know the brand, Perplexity ranks it by whatever listicle it pulled, and DeepSeek missed it entirely. The fix for the DeepSeek gap (get cited in the sources it reads) is different from the fix for the Perplexity rank (get into higher-authority listicles).
Why you track all five, not your favorite one
Inconsistency across engines is the norm, not a glitch. Checking only ChatGPT tells you nothing about the buyer who asks Perplexity, and the two often disagree on which brand wins. A weak spot in one engine is invisible if you only look at another.
Tracking all five also separates the two failure modes. If you are absent everywhere, that is an authority problem: the web does not talk about you enough. If you are strong in the training-based engines but weak in the search-grounded ones, that is a freshness or ranking problem: your recent content is not getting cited. The pattern across engines tells you which one you have.
Sentiment matters as much as presence. An engine can mention you and still frame you as the expensive option or the legacy pick. Reading the actual wording, not just the yes-or-no, is where you catch a description that is quietly costing you deals.
Doing this by hand across five engines and dozens of prompts gets old fast. avisibli runs the same prompt across ChatGPT, Perplexity, Gemini, Claude, and DeepSeek on a schedule and lays the mentions, sentiment, and citations in one cross-engine view, so you can spot the gaps without the copy-paste.
avisibli is the GEO platform that publishes this answer library. Self-references are limited to topics where a tool-based answer is genuinely useful to readers.