GSM8K: Grade School Math Word Problems

Measures how effectively language models solve realistic, linguistically rich math word problems suitable for grade-school-level mathematics.

Overview

GSM8K is a dataset consisting of diverse grade school math word problems.

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/gsm8k --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.gsm8k import gsm8k
eval(gsm8k)

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/gsm8k --limit 10
uv run inspect eval inspect_evals/gsm8k --max-connections 10
uv run inspect eval inspect_evals/gsm8k --temperature 0.5

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

Parameters

gsm8k

• fewshot (int): The number of few shots to include (default: 10)
• fewshot_seed (int): The seed for generating few shots (default: 42)
• shuffle_fewshot (bool): Whether we take the first N samples of the dataset as the fewshot examples, or randomly sample to get the examples. (default: True)

Dataset

Here is an example prompt from the dataset (after it has been further processed by Inspect):

Solve the following math problem step by step. The last line of your response should be of the form “ANSWER: $ANSWER” (without quotes) where $ANSWER is the answer to the problem.

Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers’ market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers’ market?

Remember to put your answer on its own line at the end in the form “ANSWER: $ANSWER” (without quotes) where $ANSWER is the answer to the problem, and you do not need to use a \boxed command.

Reasoning:

The model is then expected to generate reasoning steps and provide a final answer.

Scoring

An accuracy is calculated over the datapoints. The correctness is based on an exact-match criterion and whether the model’s final answer is correct.