math benchmark
BIG-Bench Leaderboard
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 17, 2026
Models3
Model coverage3
MetricScore
EvidenceB
BIG-Bench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelGO | Score75.0% | Percentile100.0% | Participants3 | EvidenceB | Evaluated |
| Rank02 | ModelGO | Score74.9% | Percentile50.0% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelGO | Score68.2% | Percentile0.0% | Participants3 | EvidenceC | Evaluated |
BIG-Bench Highlights
The leading models and scores on this benchmark.
BIG-Bench Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for BIG-Bench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis big-bench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01GOGemini 1.0 ProGoogle
- Score
- 75.0%
- Speed
- Up to 120 tok/s via Google
Strengths
- Ranks #1 of 3 compared models
- 100th percentile on this benchmark
- B evidence result
Considerations
- This result measures BIG-Bench, not total model capability
- 02GOGemma 2 27BGoogle
- Score
- 74.9%
Strengths
- Ranks #2 of 3 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures BIG-Bench, not total model capability
- 03GOGemma 2 9BGoogle
- Score
- 68.2%
Strengths
- Ranks #3 of 3 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures BIG-Bench, not total model capability
Selection summary
Best AI Models for BIG-Bench
Gemini 1.0 Pro currently leads BIG-Bench with 75.0%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is BIG-Bench?
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.
- Family
- BIG-Bench
- Modality
- text
- Primary category
- math
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- big-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about BIG-Bench.
Which model scores highest on BIG-Bench?
Gemini 1.0 Pro is currently ranked first with 75.0%.
What does BIG-Bench measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.