AI Model Comparison

By running one fixed prompt set against every engine on the same schedule and reading the result per engine before averaging. Each model has its own retrieval pipeline and its own training cutoff, so the same brand can be named in most answers on one and absent from another. A single blended score hides exactly the gap worth acting on.

The one where the gap between you and the brands named alongside you is largest, which is a different answer for every brand. That is why the comparison is run before the work is chosen rather than after: focusing on the engine you personally use is the most common way to spend a quarter on the surface you were already winning.

Because they weight different signals. Gemini and AI Overviews lean on the same authority signals as Google Search. Perplexity favours pages it can cite confidently and reads them live. Claude rewards substantive third-party coverage and is conservative about brands it cannot disambiguate. Copilot leans on Bing and on documented Microsoft ecosystem fit. Grok weights recent conversation on X.

Track both from the same prompt set in the same run, then read share of voice per engine rather than pooled. Comparing two engines is only meaningful when the questions and the timing are identical; otherwise you are measuring two different prompt sets and calling the difference a finding.

The table on this page compares all eight engines Strajist tracks by what each one weights most heavily. For a specific pairing, the head-to-head pages below go deeper on two engines at a time, and each model has its own page covering the prompts and signals particular to it.

Only if your buyers use one, which is rarely true. Tracking a single engine gives a number that moves for reasons you cannot see, because you have no comparison to tell whether a change is yours or the engine's.