What it is
This page provides guidance on how to evaluate the performance of AI models using the OpenAI API, including best practices and methodologies for assessment.
Gabriel’s notes
OpenAI Documentation on how to evaluate AI performance.
Good fit if you want to:
- build, test, or ship software faster (APIs, dev tooling, code assistance).
Pricing snapshot (auto-enriched): Free tier available; usage-based pricing charged per 1M tokens for input, output, and cached input; additional costs apply for tool calls and storage with some free allowances.
Work-use / compliance snapshot (auto-enriched): The OpenAI API and related tools are suitable for workplace use, offering enterprise-grade data ownership and control with no default training on customer data, configurable data retention, SSO via SAML, and compliance with SOC 2, HIPAA (via BAA), GDPR, and other major privacy standards.
Alternatives (auto-enriched): Alternative: Hugging Face Evaluate | Comparison: Hugging Face Evaluate offers a broad open-source evaluation framework with extensive community support, whereas OpenAI Evals focuses on standardized benchmarking and custom evaluations within the OpenAI ecosystem.
Reading tip: skim headings first, then focus on the sections that match your current project or question.
Note: pricing and policy details can change—verify on the official site before making decisions.