This white paper presents an evaluation of integrating large language models into post signature contract review workflows, comparing their performance against traditional manual review and established machine learning based contract analytics models. Using a controlled testing framework applied across representative contract types and review scopes, the analysis examines three dimensions critical to large scale review projects: accuracy, efficiency and cost.

The testing reflects the performance of models as available at the time of evaluation. While all model families evaluated have continued to evolve, the objective of this study is to compare relative performance across approaches using a consistent, controlled methodology, rather than benchmark any single model as a permanent or definitive solution.

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