Strategic Insights in Korean-English Translation: Cost, Latency, and Quality Assessed through Large Language Model

We evaluates machine translation models (GPT-3.5-turbo, GPT-4-turbo, Google Translator API, DeepL API, Papago API), focusing on cost, latency, and translation quality, alongside a novel application of LLMs for translation evaluation. Utilizing the dataset of the Korean-English and English-Korean pairs, the study reveals distinct performance attributes: GPT-3.5-turho as the most cost-efficient, Google Translator API as the fastest, and GPT-4-turbo as superior in quality despite higher costs and latency. This approach highlights the critical need for strategic model selection based on specific project requirements and paves the way for future research in LLM-enhanced evaluation.