math 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 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | GO | 75.0% | 100.0% | 3 | B | |
| 2 | GO | 74.9% | 50.0% | 3 | C | |
| 3 | GO | 68.2% | 0.0% | 3 | C |
Top published rows on the benchmark's original scale.
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
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. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
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 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.