MMLU-Pro: Advanced Multitask Knowledge and Reasoning Evaluation

An advanced benchmark that tests both broad knowledge and reasoning capabilities across many subjects, featuring challenging questions and multiple-choice answers with increased difficulty and complexity.

Overview

MMLU-Pro is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models’ capabilities. This dataset contains 12K complex questions across 14 disciplines (including ‘Other’).

There are three major differences compared to original MMLU:

1. MMLU-Pro increases the number of options from 4 to 10, making the evaluation more realistic and challenging.
2. In this dataset, the creators increase the problem difficulty and integrate more reasoning-focused problems.
3. The benchmark is made more robust by increasing the number of distractor options.

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/mmlu_pro --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.mmlu_pro import mmlu_pro
eval(mmlu_pro)

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

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

Parameters

mmlu_pro

• subjects (str | list[str]): List of subjects to evaluate on. (default: [])
• fewshot (int): Number of few shot examples to use. (default: 0)
• shuffle (bool): Whether we shuffle the dataset for evaluation (default: True)

Dataset

Here is an example from the dataset:

Question: Approximately how far away is the Andromeda Galaxy?

Options: A) 5 million light years
B) 2.5 million light years
C) 2.1 million light years
D) 1.9 million light years
E) 3.2 million light years
F) 4 million light years
G) 1.7 million light years
H) 3.5 million light years
I) 1.2 million light years
J) 2.8 million light years

The model is tasked to answer the question and choose the appropriate option.

Evaluation

The prompts are based on EleutherAI’s lm-evaluation-harness and MultipleChoiceTemplate.SINGLE_ANSWER. The in-built choice scorer is used for evaluation.