ChatGPT, Perplexity, and Gemini were not trained equally on all languages. For high-resource languages like English, French, and Mandarin, all three engines perform reliably. For lower-resource languages — regional dialects, minority languages, or newer national languages — accuracy drops and varies considerably by engine.
Omniscient AI's three-engine comparison creates an empirical record of engine performance across languages used in a newsroom. Editors who run verification checks consistently can identify patterns: which engine tends to hallucinate on Arabic political terms, which one is more reliable for Portuguese statistics, which one struggles with Swahili proper nouns.
This knowledge becomes a practical editorial protocol. Editors can designate primary and backup verification sources per language — making multi-language newsrooms systematically more accurate rather than relying on individual reporter instincts about AI reliability.