When multiple normative frameworks yield equally valid answers, which framework should a language model select, and can it answer correctly within it? We formalize this as normative pluralism and study it in Islamic finance with a four-choice taxonomy that decomposes framework selection from within-framework correctness. The decomposition exposes a failure mode invisible to two-choice instruments, the stereotype trap: a model leans toward the culturally appropriate framework but produces factually wrong answers within it. Across twelve models, two languages, and fifty demographic signals, cultural cues redirect framework selection at severe cost: redirection and correctness are inversely coupled (r=−0.89 Arabic, r=−0.81 English). Under the strongest signal, large open-weight models reach 97% Islamic-frame selection, a result two-choice instruments would report as near-perfect alignment, yet 57-66% of those responses misquote the Islamic rule. We argue that cultural default bias is competence-conditioned routing: models default to the framework where they are most accurate, and the cues prescribed as mitigation expose the competence gap they were meant to correct.

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. Evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.