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Training data coverage affects what a model encounters, while tokenization affects how efficiently text is represented. A passage can require different token counts across languages even when it expresses similar information. This changes practical context limits and serving costs. Cross-lingual transfer can help a model apply patterns learned from one language to another. However, transfer is a capability to measure, not a guarantee that specialized terminology, idioms, or culturally situated questions will be handled correctly. Build an evaluation set for each important language and task. Include natural local examples, mixed-language messages, named entities, and longer documents. Translating an English benchmark alone can introduce unnatural wording or errors that confound the measurement. Review the complete user experience: output language, fonts, text direction, locale formats, citations, and fallback behavior. If the system cannot confidently perform a task in a requested language, communicate that limitation and preserve access to the source. Track regression results by language rather than hiding them in one global average.