Parity

Parity: Statistical testing tool that compares performance between two AI models using gold labels and prediction files to determine if one is inferior.

Parity is a command-line tool designed for testing machine learning models before deploying them into production environments. It requires JSONL files containing gold labels and model predictions to calculate performance metrics. The software performs a non-inferiority test by running a paired bootstrap on model scores, producing a 95% confidence interval for the difference between a candidate model and a baseline. It generates reports in Markdown and JSON formats, providing accuracy, Macro-F1 scores, and invalid output counts. This tool helps teams decide if they can transition to smaller, less expensive models without sacrificing performance. It uses NumPy as its only dependency and requires no GPU or API keys.

Parity at a glance

Best for: Software engineer
Runs on: CLI
Pricing: Free
Released: October 2026
Category: AI coding tool
Website: github.com/illate/parity

Key features

Paired bootstrap resampling for accurate difference testing
Non-inferiority testing with 95% confidence intervals
JSONL input support for labels and predictions
Automatic generation of Markdown and JSON reports
Exit codes for integration into CI pipelines
Error reporting for invalid model outputs
Performance analysis by individual classes

Use it for

Validating smaller, cheaper models before replacing existing ones
Comparing performance between two different model architectures
Testing if candidate models stay within an agreed margin of current performance
Generating performance reports for fine-tuned APIs or local models

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