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

Install with pip install inspect-evals, or uv sync from a checkout of this repository.

Running evaluations

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)

Drop uv run if you manage dependencies yourself. Log viewing (inspect view) and default-model setup are documented in the Inspect Evals README.

Options

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

uv run inspect eval inspect_evals/gsm8k --limit 10 --sample-shuffle
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.

Changelog

[2-A] - 2026-02-16

• Migrate version to new scheme. See #907.

[1.0.1] - 2025-12-18

• Adds backoff policy for functions that connect to huggingface servers.