BIG-Bench Hard
BIG-Bench Hard (BBH) - a subset of 23 challenging BIG-Bench tasks. few-shot.
Category
Reasoning
Score type
percent
Scale
100%
higher is better
Models tested
8
What is BIG-Bench Hard?
BIG-Bench Hard (BBH) is a subset of 23 challenging tasks from the crowd-sourced BIG-Bench collection on which earlier language models still clearly trailed the human average. Today BBH is one of the most established reasoning baselines.
What does it test?
BIG-Bench Hard measures general reasoning and comprehension. In detail, it checks:
- 23 very different reasoning task types
- logical, linguistic and spatial reasoning
- multi-step thinking with chain-of-thought
- comparability as an established baseline
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the BIG-Bench Hard benchmark?
BIG-Bench Hard (BBH) is a subset of 23 challenging tasks from the crowd-sourced BIG-Bench collection on which earlier language models still clearly trailed the human average. Today BBH is one of the most established reasoning baselines.
What does BIG-Bench Hard test?
BIG-Bench Hard mainly measures general reasoning and comprehension. Concretely, it checks: 23 very different reasoning task types; logical, linguistic and spatial reasoning; multi-step thinking with chain-of-thought.
How is the BIG-Bench Hard score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get BIG-Bench Hard data and scores?
The official source is https://paperswithcode.com/dataset/bbh. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/big-bench-hard/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 8 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
GLM-5.3-Flash
Zhipu AI
|
86.6%
|
27.08.2026 | Report | Verified |
| 2 |
Gemma 3 27B PT
Google DeepMind
|
77.7%
|
27.02.2025 | Report | Verified |
| 3 |
ERNIE-5.5-Flash-Base
Baidu
|
77.1%
|
— | HF model card baidu/ERNIE-5.5-Flash-Base (BBH, EM direct, 3-shot, Unified Pipeline) | Unverified |
| 4 |
Clef
Cloudflare
|
73.7%
|
— | Report | Verified |
| 5 |
Gemma 3 12B PT
Google DeepMind
|
72.6%
|
27.02.2025 | Report | Verified |
| 6 |
Clef-Flash
Cloudflare
|
68.9%
|
— | Report | Verified |
| 7 |
Gemma 3 4B PT
Google DeepMind
|
50.9%
|
27.02.2025 | Report | Verified |
| 8 |
Gemma 3 1B PT
Google DeepMind
|
28.4%
|
27.02.2025 | Report | Verified |