How does the inference throughput-accuracy trade-off differ between o1-preview and DeepSeek-R1 under constrain

Abstract

Abstract: Recent advances in test-time scaling of large language models (LLMs), exemplified by DeepSeek-R1 and OpenAI's o1, show that extending the chain of thought during inference can significantly improve general reasoning performance. However, the impact of this paradigm on legal reasoning remains insufficiently explored. To address this gap, we present the first systematic evaluation of 12 LLMs, including both reasoning-focused and general-purpose models, across 17 Chinese and English legal tasks spanning statutory and case-law traditions. In addition, we curate a bilingual chain-of-thought dataset

Research Question

How does the inference throughput-accuracy trade-off differ between o1-preview and DeepSeek-R1 under constrained token budgets when performing abductive reasoning on legal case summaries, as measured by F1 score and latency per query?

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