How does the token-efficiency trade-off (accuracy per inference cost) vary between DeepSeek-R1 and o1-preview

Abstract

Abstract: Despite increasing discussions on open-source Artificial Intelligence (AI), existing research lacks a discussion on the transparency and accessibility of state-of-the-art (SoTA) Large Language Models (LLMs). The Open Source Initiative (OSI) has recently released its first formal definition of open-source software. This definition, when combined with standard dictionary definitions and the sparse published literature, provide an initial framework to support broader accessibility to AI models such as LLMs, but more work is essential to capture the unique dynamics of openness in AI. In addition,

Research Question

How does the token-efficiency trade-off (accuracy per inference cost) vary between DeepSeek-R1 and o1-preview when processing multilingual legal document classification tasks?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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Provenance