MedQA: Medical exam Q&A benchmark

Knowledge

A Q&A benchmark with questions collected from professional medical board exams. Only includes the English subset of the dataset (which also contains Mandarin Chinese and Taiwanese questions).

Contributed By

@bunny-baxter

@JasonBenn

Code

src/inspect_evals/medqa

Paper

https://arxiv.org/abs/2009.13081

Overview

MedQA is an evaluation dataset of medical knowledge questions collected from medical board exams. The full dataset has questions in English, Mandarin Chinese, and Taiwanese, but this evaluation only includes the English subset.

This evaluation is likely close to saturation. According to a result from 2023, GPT-4 was already scoring 86.1% at that time. GPT-4.1 does not currently score much better. A quick examination of its incorrect answers revealed some possibly incorrect or arguable questions, so it is not clear how much higher a realistic score might be.

Usage

Installation

There are two ways of using Inspect Evals, from pypi as a dependency of your own project and as a standalone checked out GitHub repository.

If you are using it from pypi, install the package and its dependencies via:

pip install inspect-evals

If you are using Inspect Evals in its repository, start by installing the necessary dependencies with:

uv sync

Running evaluations

Now you can start evaluating models. For simplicity’s sake, this section assumes you are using Inspect Evals from the standalone repo. If that’s not the case and you are not using uv to manage dependencies in your own project, you can use the same commands with uv run dropped.

uv run inspect eval inspect_evals/medqa --model openai/gpt-5-nano

You can also import tasks as normal Python objects and run them from python:

from inspect_ai import eval
from inspect_evals.medqa import medqa
eval(medqa)

After running evaluations, you can view their logs using the inspect view command:

uv run inspect view

For VS Code, you can also download Inspect AI extension for viewing logs.

If you don’t want to specify the --model each time you run an evaluation, create a .env configuration file in your working directory that defines the INSPECT_EVAL_MODEL environment variable along with your API key. For example:

INSPECT_EVAL_MODEL=anthropic/claude-opus-4-1-20250805
ANTHROPIC_API_KEY=<anthropic-api-key>

Options

You can control a variety of options from the command line. For example:

uv run inspect eval inspect_evals/medqa --limit 10
uv run inspect eval inspect_evals/medqa --max-connections 10
uv run inspect eval inspect_evals/medqa --temperature 0.5

See uv run inspect eval --help for all available options.

Dataset

All questions are multiple choice, with 5 options and 1 correct answer. For humans, a passing score is 60%, and the 95th percentile score is 87%.

Example question:

  • Question: A scientist is studying the properties of myosin-actin interactions in a sample of human muscle tissue. She has identified a drug that selectively inhibits phosphate release by the myosin head. If she gives this drug to a sample of human muscle tissue under physiologic conditions, which of the following steps in cross-bridge cycling will most likely be blocked?
  • Choices:
      1. Myosin head release from actin
      1. Myosin head cocking
      1. Exposure of myosin-binding sites on actin
      1. Myosin head binding to actin
      1. Power stroke
  • Answer: E

Scoring

Simple accuracy over multiple choice answers.

Evaluation Report

Results from July 3, 2025, running on all 1,273 samples:

Model Accuracy
gpt-4o-mini 68.5%
gpt-4.1 87.4%