ChemBench: Are large language models superhuman chemists?

ChemBench is designed to reveal limitations of current frontier models for use in the chemical sciences. It consists of 2786 question-answer pairs compiled from diverse sources. Our corpus measures reasoning, knowledge and intuition across a large fraction of the topics taught in undergraduate and graduate chemistry curricula. It can be used to evaluate any system that can return text (i.e., including tool-augmented systems).

Overview

ChemBench is designed to reveal limitations of current frontier models for use in the chemical sciences. It consists of 2786 question-answer pairs compiled from diverse sources. Our corpus measures reasoning, knowledge and intuition across a large fraction of the topics taught in undergraduate and graduate chemistry curricula. It can be used to evaluate any system that can return text (i.e., including tool-augmented systems).

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/chembench --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.chembench import chembench
eval(chembench)

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

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

Parameters

chembench

• task_name (str | None): (default: None)
• cot (bool): (default: False)
• shuffle (bool): (default: True)

You can run the eval on one of the subsets by specifying its name via -T task_name=<name> command-line argument, for example:

uv run inspect eval inspect_evals/chembench --model openai/gpt-4o -T task_name=inorganic_chemistry

The valid task are: “analytical_chemistry”, “chemical_preference”, “general_chemistry”, “inorganic_chemistry”, “materials_science”, “organic_chemistry”, “physical_chemistry”, “technical_chemistry”, “toxicity_and_safety”.

An optional cot parameter can be provided with True to enable Chain-of-Thought:

inspect eval src/inspect_evals/chembench -T task_name=inorganic_chemistry -T cot=True

Dataset

ChenBench includes 9 tasks consisting of both multiple-choice and open-ended questions, totaling 2,786 samples. (Note: The original paper reports 2,788 samples, but the version on Hugging Face has two duplicates removed. See this issue for details.)

You can download the dataset from Hugging Face.

Scoring

pattern scorers with customized regexes are used to match the answer. Accuracy and stderr are calculated over each task and all samples.