This study combines two evaluation methods: three automatic evaluation metrics (BLEU, chrF, and COMET) and LLM-as-a-judge to evaluate translation quality for the low-resource language pair Thai-English. Moreover, this study also investigates translation performance by comparing with the high-resource language pair Swedish-English. Within the scope of the study, four large language model (LLM) systems (Gemini 2.5 Flash, GPT-5.4 mini, Qwen2-72B, Llama 3.3-70B) and one neural machine translation (NMT) system (Google Translate) were used to translate 32 texts drawn from the official governmental and diplomatic domain with a focus on terminological accuracy and semantic preservation. The results show that Gemini 2.5 Flash outperforms Google Translate and all other LLM- based translation systems on Thai-English translation across all three automatic metrics and LLM-as-a-judge. In contrast, Google Translate outperforms Gemini 2.5 Flash across BLEU and chrF on Swedish-English translation. These findings suggest that LLM systems are better suited for translation in low-resource languages and the combination of the evaluation methods provides a complementary evaluation of translation quality.