Last updated 07-26-2026
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BIG-bench
BIG-bench measures how well large language models handle reasoning, math, bias, and multilingual tasks across more than 200 community-written evaluation challenges. Google hosts the open source repository on GitHub, where researchers contributed tasks through pull requests and published comparative model scores on the leaderboard. Each task scores models through text generation or log-probability queries, using metrics like BLEU, BLEURT, and exact string match.
Unlike fixed benchmarks such as GLUE or SuperGLUE, BIG-bench grew through community pull requests, so task authors could submit challenges designed to exceed what existing models could solve. The suite also ships BIG-bench Lite, a 24-task subset that gives a cheaper canonical score across the full collection of 200+ tasks. Programmatic tasks support multi-turn model interaction, while JSON tasks work through a simpler task.json format with built-in scoring rules.
ML researchers use BIG-bench to compare model scaling trends and publish leaderboard results. Model developers run evaluations locally with HuggingFace models or through Docker scripts, then submit score files via pull request. The benchmark is archived and read-only as of April 2026, but the tasks, code, and published TMLR 2023 analysis paper remain available for reproducible research.
More than 200 benchmark tasks across JSON and programmatic formats, contributed via open pull requests
BIG-bench Lite packs 24 diverse tasks for a cheaper canonical model comparison score
Built-in metrics include BLEU, BLEURT, ROUGE, exact string match, and multiple-choice grading
SeqIO integration loads JSON tasks with 0-shot through 3-shot evaluation presets
Python 3.5 through 3.8 required; install with pip install -e . from the GitHub repository
200+ community-contributed tasks cover reasoning, math, bias, and multilingual evaluation in one suite.
BIG-bench Lite provides a 24-task shortcut for comparable model scores without running the full benchmark.
Open source under Apache 2.0 with local Docker scripts and HuggingFace model support for self-hosted evaluation.
Repository archived in April 2026, so new task submissions via pull request are no longer accepted.
SeqIO bridge covers JSON tasks only; programmatic tasks need the native BIG-bench Python API.
Requires Python 3.5 through 3.8, which excludes newer Python versions without manual compatibility work.
What is BIG-bench?
BIG-bench is the Beyond the Imitation Game Benchmark, a collaborative suite of more than 200 tasks for measuring large language model capabilities. Researchers contributed tasks via GitHub pull requests, and results are published on the repository leaderboard.
Is BIG-bench free to use?
Yes, BIG-bench is free and open source under the Apache 2.0 license on GitHub. You can clone the repository, install it with pip install -e ., and run evaluations locally without paying for access.
How many tasks does BIG-bench include?
BIG-bench includes more than 200 tasks spanning JSON and programmatic formats. The README also defines BIG-bench Lite, a 24-task subset designed for cheaper canonical model comparisons.
What is BIG-bench Lite?
BIG-bench Lite is a 24-task subset of BIG-bench chosen for diversity and lower evaluation cost. It appears on the repository leaderboard as a canonical score without running the full 200+ task collection.
How do you install BIG-bench?
BIG-bench installs from GitHub with git clone and pip install -e . after running python setup.py sdist. The README lists Python 3.5 through 3.8 and pytest as requirements for running the benchmark locally.
Can you still contribute to BIG-bench?
The BIG-bench GitHub repository was archived in April 2026 and is now read-only, so new task pull requests are no longer accepted. Existing tasks, evaluation scripts, and submitted model scores remain available for research use.