Recently, Test-Time Scaling Large Language Models (LLMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated exceptional capabilities across various domains and tasks, particularly in reasoning. While these models have shown impressive performance on general language tasks, their effectiveness in specialized fields like legal remains unclear. To address this, we present a preliminary evaluation of LLMs in various legal scenarios, covering both Chinese and English legal tasks. Our analysis includes 9 LLMs and 17 legal tasks, with a focus on newly published and more complex challenges such as multi-defendant legal judgments and legal argument reasoning. Our findings indicate that, despite DeepSeek-R1 and OpenAI o1 being among the most powerful models, their legal reasoning capabilities are still lacking. Specifically, these models score below 80\% on seven Chinese legal reasoning tasks and below 80\% on two English legal reasoning tasks. This suggests that, even among the most advanced reasoning models, legal reasoning abilities remain underdeveloped.

翻译：近期，测试时扩展大语言模型（如DeepSeek-R1和OpenAI o1）在多个领域和任务中展现出卓越能力，尤其在推理方面表现突出。尽管这些模型在通用语言任务上已显示出令人印象深刻的性能，但其在法律等专业领域的有效性仍不明确。为此，我们对大语言模型在多种法律场景下的表现进行了初步评估，涵盖中英文法律任务。我们的分析涉及9个大语言模型和17项法律任务，重点关注新近发布且更具挑战性的复杂问题，例如多被告法律判决和法律论证推理。研究结果表明，尽管DeepSeek-R1和OpenAI o1属于最强大的模型之列，但其法律推理能力仍有不足。具体而言，这些模型在七项中文法律推理任务中的得分低于80%，在两项英文法律推理任务中的得分也低于80%。这表明，即使在最先进的推理模型中，法律推理能力仍有待提升。