Eastern Min Chinese (Fuzhou dialect, ISO 639-3 ‘cdo‘) has roughly ten million speakers yet no dedicated paper at any major NLP venue. Four properties make Fuzhou structurally distinct from Mandarin and challenging for current NLP: (1) a tone-sandhi system conditioned on the following syllable’s citation tone, (2) initial-consonant assimilation in non-final positions, (3) systematic literary-colloquial diglossia (45% of frequent characters carry ≥ 2 distinct segmental readings), and (4) substantial lexical divergence (everyday concepts use entirely different morphemes from Mandarin). We release **FuzhouBench**, the first public NLP benchmark for Eastern Min, built on a community-maintained open dictionary: 22,890 word entries with 51,505 standardized Yngping romanizations, 11,276 explicit tone-sandhi annotations, 5,160 explanations, and 3.06 hours of paired dictionary-style audio from two native speakers. We define three tasks (grapheme-to-phoneme, sandhi prediction, definition retrieval) with full baselines. Frontier LLMs score under 3% word-exact on few-shot Yngping G2P, while a LoRA fine-tune on the released pairs reaches 17.2%: learnable, but far from saturated. A symbolic sandhi rule learned from the training pool beats every LLM by up to 18 points; and BM25 collapses from MRR 0.799 to 0.372 when queries shift from dictionary-extracted to colloquial Mandarin, partially recoverable via a two-stage hybrid + cross-encoder pipeline (MRR 0.716). Data, evaluation splits, and code are available at https://github.com/Nang-muoi-sing/FuzhouBench.