How OpenAI, Claude and Gemini describe you differently

ChatGPT, Claude, and Gemini don’t share one database. They’re trained and updated separately, and they may retrieve different sources. So the same question can get different answers—and that can matter for how you’re perceived. Someone using ChatGPT might see one version of you; someone using Claude might see another. For founders, executives, and brands, that inconsistency can be as problematic as a single wrong answer.

This article explains why the big models diverge, how to measure the difference, and how to use that information.

Why answers differ

Models differ in training data, cut-off dates, and whether they use live search. One may have a clear answer; another may be vague or point to someone else. Consistency across models is useful to know.

Training data and cut-offs mean that one model may have learned about you from different sources or a different point in time. Retrieval (when enabled) pulls from different indexes and ranking logic. So you can’t assume that "what AI says" is one thing—it’s at least three (OpenAI, Anthropic, Google), and often more when you include other systems. Measuring across them is the only way to see the full picture.

What to measure

A Scan compares the first impression across OpenAI, Anthropic, and Google in one report. You see side-by-side how each describes the entity. A Snapshot adds more models and goes deeper into retrieval and web context.

The Scan is designed for comparability: the same style of query, the same point in time. That makes it easy to spot model-specific gaps—e.g. "Claude has me right, ChatGPT mixes me up." The Snapshot then explains why: which sources each model uses, how entity resolution differs, and whether the fix is model-specific or structural.

Using the results

If one model is wrong or mixed and others are fine, the issue may be model-specific. If all are inconsistent, the problem may be structural. The diagnostic helps you decide where to focus.

In practice, that might mean prioritizing one ecosystem (e.g. where your investors or customers mostly are) or working on the shared inputs—canonical description, sources, disambiguation—that can improve representation across multiple models. We don’t guarantee outcomes, but we give you the map so you can decide.