Estimating model performance without labeled data is important for deploying NLP systems across diverse domains. Prior work has proposed proxy measures based on distribution shift or predicted correctness, but it remains unclear whether these methods reliably preserve the ranking of domains, which is critical for model selection and risk triage. We study this problem from an ordinal perspective using a two-step framework that pairs a base classifier with an error predictor to estimate instance-level failures and disparities and aggregate them into label-free performance estimates. Across multiple sample sizes from the GeoOLID and Amazon Reviews datasets, large language model–based error predictors substantially outperform drift-based baselines in 𝜌 and 𝜏 rank correlations. We further show that lower absolute estimation error (RMSE) does not necessarily imply better ranking, highlighting the importance of evaluating ordinal consistency directly.

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