In industrial LLM development, evaluating large language models (LLMs) is critical for tasks like benchmarking internal models and detecting regressions during fine-tuning, but existing benchmark aggregation methods, such as Elo-based systems, can be resource-intensive, public facing, and time-consuming. Here, we describe Chatbot Arena Estimate (CAE), a practical framework for aggregating performance across diverse benchmarks. The framework, developed and widely adopted within our organization, addresses the need for quick, accurate, and cost-efficient evaluations of LLMs. CAE generates two primary metrics: a “Goodness” score (answer accuracy) and a “Fastness” score (cost or queries per second, QPS). These metrics allow for model ranking both overall and within specific subdomains, enabling informed decisions during model iteration and deployment. We demonstrate CAE’s effectiveness by comparing it with existing benchmarks, including the full Chatbot Arena and the MMLU leaderboard. Notably, our approach achieves higher Pearson correlation with Chatbot Arena Elo scores than MMLU’s correlation with Chatbot Arena Elo scores, validating its reliability for real-world LLM evaluation.