We evaluate seven large language models—four proprietary and three open-weight—on bidirectional Lakota–English translation using 200 sentence pairs from the New Lakota Dictionary. Each model is evaluated with and without extended reasoning, where the provider’s API permits. The best model (Gemini 3.1 Pro) achieves a mean chrF++ of 59.4 on Lakota→English and 42.6 on English→Lakota; the strongest open-weight model trails the proprietary leaders, and no model produces reliable translation in either direction. Two independent LLM judges from different model families agree substantially (Cohen’s κ=0.75) that semantic equivalence ranges from 6% (GPT-5.2) to 60% (Gemini), diverging substantially from chrF++ scores. For the open-weight models, enabling reasoning changes refusal behavior far more than translation quality: it surfaces the limitation rather than overcoming it. Diacritic-normalization analysis shows models produce roughly correct base characters but place diacritical marks inconsistently. All results and evaluation code are publicly available at https://github.com/robotson/lakota-translation-benchmark.