This paper evaluates the performance of Claude 3 Opus, a large language model (LLM) from Anthropic, in machine translation (MT) tasks. The study shows that Claude 3 Opus outperforms strong baselines like Google Translate and NLLB-54B on many language pairs, particularly in translating into English. However, it exhibits signs of data contamination on the FLORES-200 benchmark, suggesting that some of its performance may be due to exposure to training data during evaluation. The study also finds that Claude 3 Opus has remarkable resource efficiency, meaning its translation quality is less dependent on the resource level of the language pair. This suggests that Claude 3 Opus is more effective at translating low-resource languages compared to other LLMs. The paper also demonstrates that knowledge distillation techniques can be applied to LLMs to create compact neural machine translation (NMT) models that outperform the state-of-the-art. Using Claude 3 Opus to generate synthetic data, the authors show that distillation can be used to improve Yoruba-English translation, meeting or surpassing strong baselines like NLLB-54B and Google Translate. The study evaluates Claude 3 Opus on a variety of language pairs, including both high- and low-resource languages. It finds that Claude 3 Opus performs well on many language pairs, including low- and very low-resource languages. However, it still lags behind state-of-the-art NMT systems when translating from English into low-resource languages. The study also shows that Claude 3 Opus can be used to generate parallel corpora, which can then be used to fine-tune NMT models, leading to improved translation performance. The paper highlights the importance of developing machine translation benchmarks that use unseen source and target sentences to avoid data contamination. It also discusses the challenges of evaluating LLMs on public benchmarks, as these may contain data that the LLM has already seen during training. The study concludes that while Claude 3 Opus shows promise in machine translation, further research is needed to fully understand its capabilities and limitations.This paper evaluates the performance of Claude 3 Opus, a large language model (LLM) from Anthropic, in machine translation (MT) tasks. The study shows that Claude 3 Opus outperforms strong baselines like Google Translate and NLLB-54B on many language pairs, particularly in translating into English. However, it exhibits signs of data contamination on the FLORES-200 benchmark, suggesting that some of its performance may be due to exposure to training data during evaluation. The study also finds that Claude 3 Opus has remarkable resource efficiency, meaning its translation quality is less dependent on the resource level of the language pair. This suggests that Claude 3 Opus is more effective at translating low-resource languages compared to other LLMs. The paper also demonstrates that knowledge distillation techniques can be applied to LLMs to create compact neural machine translation (NMT) models that outperform the state-of-the-art. Using Claude 3 Opus to generate synthetic data, the authors show that distillation can be used to improve Yoruba-English translation, meeting or surpassing strong baselines like NLLB-54B and Google Translate. The study evaluates Claude 3 Opus on a variety of language pairs, including both high- and low-resource languages. It finds that Claude 3 Opus performs well on many language pairs, including low- and very low-resource languages. However, it still lags behind state-of-the-art NMT systems when translating from English into low-resource languages. The study also shows that Claude 3 Opus can be used to generate parallel corpora, which can then be used to fine-tune NMT models, leading to improved translation performance. The paper highlights the importance of developing machine translation benchmarks that use unseen source and target sentences to avoid data contamination. It also discusses the challenges of evaluating LLMs on public benchmarks, as these may contain data that the LLM has already seen during training. The study concludes that while Claude 3 Opus shows promise in machine translation, further research is needed to fully understand its capabilities and limitations.