This study evaluates how LLMs interpret Words of Estimative Probability (WEPs) in Brazilian Portuguese compared to English. We translated an English benchmark and compared multilingual models (GPT-5.1, Gemini 3 Flash) against a region-specific model (Sabiá 4). Our findings reveal a “tail smoothing” phenomenon, where models systematically compress extreme probabilities. Notably, while Gemini 3 Flash demonstrated remarkable cross-lingual stability, GPT-5.1 exhibited significant calibration degradation. Counterintuitively, when measured against the English human baseline, Sabiá 4 displayed the most aggressive distribution compression. These results suggest a complex dynamic: either linguistic fine-tuning does not ensure alignment, or it actively captures a culturally specific pragmatic ambiguity in Brazilian Portuguese. This exposes the vulnerabilities and nuances of deploying LLMs in nuanced semantic tasks in Brazilian Portuguese.