Choosing Reliable AI Models for Production Data Analysis

Selecting an AI model for exploratory data analysis requires evidence of repeatable performance, not just a high average score. The benchmark compares eight models across 10 synthetic EDA tasks and five runs per task, separating mean analytical quality from a reliability-adjusted score. Its results show that rankings can change materially once run-to-run variation is penalised, exposing models that perform well occasionally but inconsistently. Technology leaders should evaluate stability alongside accuracy before embedding agentic analysis into production workflows.