Data product
Model Intelligence Maintainer
A workbook and static guide for comparing model quality, price, and provider coverage.
What it is
This project pulls together model metadata and benchmark signals from multiple sources, then turns them into a workbook and static guide for a very practical question: which model should I actually use?
A Python pipeline refreshes and normalizes OpenRouter, Artificial Analysis, Vals, and LiveBench data into deterministic datasets, a workbook, and a deployed static site. The repo keeps provenance, cohort rules, and mapping diagnostics out in the open instead of pretending that part is easy.