Machine translation (MT) evaluation is central in guiding researchers on how to improve a model’s performance. Current automatic evaluation practices fail to provide reliable insights into the specific translation errors that occur, especially for low-resource languages. This paper introduces the Lux-MT-Test-Suite, enabling a linguistically motivated and fine-grained analysis of Luxembourgish–English (LB-EN) MT based on 896 test items covering 12 linguistic categories and 36 linguistic phenomena. We compare a baseline local LLM (Gemma 3), its fine-tuned counterpart (LuxMT), and a proprietary state-of-the-art LLM (GPT-5) to analyse what local LLMs learn through fine-tuning in a low-resource setting and to assess performance differences between local and proprietary systems. The findings identify specific performance gains through fine-tuning, minor degradations, a difference in translation strategies, performance gaps between local and proprietary models, and remaining challenges.