AIME 2024: Problems from the American Invitational Mathematics Examination

A benchmark for evaluating AI’s ability to solve challenging mathematics problems from the 2024 AIME - a prestigious high school mathematics competition.

Overview

The AIME 2024 benchmark is based on problems from the American Invitational Mathematics Examination, a prestigious high school mathematics competition.

This benchmark tests a model’s ability to solve challenging mathematics problems by generating step-by-step solutions and providing the correct final answer.

The benchmark uses the Maxwell-Jia/AIME_2024 dataset from Hugging Face.

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/aime2024 --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.aime2024 import aime2024
eval(aime2024)

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/aime2024 --limit 10
uv run inspect eval inspect_evals/aime2024 --max-connections 10
uv run inspect eval inspect_evals/aime2024 --temperature 0.5

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

Dataset

There are 30 problems in the dataset, and each problem includes:

• A mathematics problem from the AIME 2024
• The correct answer
• A step-by-step solution

Problems are structured:

{
  "ID": "2024-I-1",
  "Problem": "Problem statement...",
  "Solution": "Detailed solution...",
  "Answer": "Numerical answer"
}

The model is instructed to solve the problem step by step and provide the final answer in the format “ANSWER: $ANSWER”.

Scoring

The benchmark uses exact match scoring to determine if the model’s answer matches the expected answer.

Results

Model	Accuracy	Std Error
openai/gpt-4o	0.1000	0.0557
openai/gpt-4o-mini	0.0667	0.0463
anthropic/claude-3-5-sonnet-20240620	0.1000	0.0557
anthropic/claude-3-7-sonnet-20250219	0.2000	0.0743