#benchmarks + #openai

Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.

If you are building with LLMs, creating high quality evals is one of the most impactful things you can do. Without evals, it can be very difficult and time intensive to understand how different model versions might affect your use case.

July 2025: simple-evals will no longer be updated for new models or benchmark results. The repo will continue to host reference implementations for HealthBench, BrowseComp, and SimpleQA.

Evals are sensitive to prompting, and there's significant variation in the formulations used in recent publications and libraries. Some use few-shot prompts or role playing prompts ("You are an expert software programmer..."). These approaches are carryovers from evaluating base models (rather than instruction/chat-tuned models) and from models that were worse at following instructions.

For this library, we are emphasizing the zero-shot, chain-of-thought setting, with simple instructions like "Solve the following multiple choice problem". We believe that this prompting technique is a better reflection of the models' performance in realistic usage.

We will not be actively maintaining this repository and monitoring PRs and Issues. In particular, we're not accepting new evals. Here are the changes we might accept.

Bug fixes (hopefully not needed!)
Adding adapters for new models
Adding new rows to the table below with eval results, given new models and new system prompts.

This repository is NOT intended as a replacement for https://github.com/openai/evals, which is designed to be a comprehensive collection of a large number of evals.