Natural Language to Structured Query Language (NL2SQL) systems have advanced with large language models (LLMs), yet evaluations remain centered on English, leaving open how these systems behave across languages and language varieties. We aim to contribute to this by analyzing two state-of-the-art LLMs (LLaMA 3.3 70B and Qwen 3.6 35B, both zero-shot) on the WikiSQL benchmark in English, Brazilian Portuguese (PT-BR), and European Portuguese (PT-PT), keeping database schemas and inference configuration fixed. Translations were validated through distance metrics (cosine similarity 0.966), contrastive linguistic analysis confirming varietal features (e.g., cleft constructions 36.8× more frequent in PT-PT), and multi-model judgment (85% / 81% semantic equivalence, 95.5% PT-BR and 96.0% PT-PT inter-judge agreement). Results show a marked drop from English (47.25% EX) to Portuguese (PT-BR: 40.03%; PT-PT: 39.34%), with the PT-BR vs. PT-PT difference reaching statistical significance (p = 0.019). English-only successes outnumber Portuguese-only successes nearly 10:1. A replication on a second model (Qwen 3.6 35B) confirms the English-Portuguese gap but suggests that intralinguistic effects may be model-dependent. Stratified analyses show semantic complexity and quantification amplify degradation, particularly for COUNT queries. These findings point to persistent English bias in NL2SQL and measurable effects of intralinguistic variation, even between closely related varieties.