language benchmark
Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark consisting of 204+ tasks designed to probe large language models and extrapolate their future capabilities. It covers diverse domains including linguistics, mathematics, common-sense reasoning, biology, physics, social bias, software development, and more. The benchmark focuses on tasks believed to be beyond current language model capabilities and includes both English and non-English tasks across multiple languages.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | GO | 75.0% | 100.0% | 3 | B | |
| 02 | GO | 74.9% | 50.0% | 3 | C | |
| 03 | GO | 68.2% | 0.0% | 3 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What BIG-Bench measures and how its scores work.
Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark consisting of 204+ tasks designed to probe large language models and extrapolate their future capabilities. It covers diverse domains including linguistics, mathematics, common-sense reasoning, biology, physics, social bias, software development, and more. The benchmark focuses on tasks believed to be beyond current language model capabilities and includes both English and non-English tasks across multiple languages.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about BIG-Bench.
Gemini 1.0 Pro is currently ranked first with 75.0%.
Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark consisting of 204+ tasks designed to probe large language models and extrapolate their future capabilities. It covers diverse domains including linguistics, mathematics, common-sense reasoning, biology, physics, social bias, software development, and more. The benchmark focuses on tasks believed to be beyond current language model capabilities and includes both English and non-English tasks across multiple languages.
Yes. Higher values rank better for this benchmark.
3 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.