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language benchmark

BIG-Bench Hard

BIG-Bench Hard (BBH) is a subset of 23 challenging BIG-Bench tasks selected because prior language model evaluations did not outperform average human-rater performance. The benchmark contains 6,511 evaluation examples testing various forms of multi-step reasoning including arithmetic, logical reasoning (Boolean expressions, logical deduction), geometric reasoning, temporal reasoning, and language understanding. Tasks require capabilities such as causal judgment, object counting, navigation, pattern recognition, and complex problem solving.

Updated Aug 11, 2026

Models21
Model coverage21
MetricScore
EvidenceB

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BIG-Bench Hard Ranking

Higher score ranks better on this benchmark.

21 rows
Columns

Show columns

01ANClaude 3.5 SonnetAnthropic93.1%100.0%21CAug 11, 2026
02ANClaude 3.5 SonnetAnthropic93.1%95.0%21CAug 11, 2026
03GOGemini 1.5 ProGoogle89.2%90.0%21CAug 11, 2026
04GOGemma 3 27BGoogle87.6%85.0%21CAug 11, 2026
05ANClaude 3 OpusAnthropic86.8%80.0%21CAug 11, 2026
06GOGemma 3 12BGoogle85.7%75.0%21CAug 11, 2026
07GOGemini 1.5 FlashGoogle85.5%70.0%21CAug 11, 2026
08ANClaude 3 SonnetAnthropic82.9%65.0%21CAug 11, 2026
09MIPhi-3.5-MoE-instructMicrosoft79.1%60.0%21CAug 11, 2026
10ANClaude 3 HaikuAnthropic73.7%55.0%21CAug 11, 2026
11GOGemma 3 4BGoogle72.2%50.0%21CAug 11, 2026
12MIPhi 4 MiniMicrosoft70.4%45.0%21CAug 11, 2026
13IBGranite 3.3 8B BaseIBM69.1%40.0%21CAug 11, 2026
14IBGranite 3.3 8B InstructIBM69.1%35.0%21CAug 11, 2026
15MIPhi-3.5-mini-instructMicrosoft69.0%30.0%21CAug 11, 2026
16IBIBM Granite 4.0 Tiny PreviewIBM55.7%25.0%21CAug 11, 2026
17GOGemma 3n E4BGoogle52.9%20.0%21CAug 11, 2026
18GOGemma 3n E4B Instructed LiteRT PreviewGoogle52.9%15.0%21CAug 11, 2026
19GOGemma 3n E2BGoogle44.3%10.0%21CAug 11, 2026
20GOGemma 3n E2B Instructed LiteRT (Preview)Google44.3%5.0%21CAug 11, 2026
21GOGemma 3 1BGoogle39.1%0.0%21CAug 11, 2026

BIG-Bench Hard Score Distribution

A closer view of the leading scores on this benchmark.

BIG-Bench Hard

BIG-Bench Hard Highlights

The leading models and scores on this benchmark.

Rank #1Claude 3.5 Sonnet93.1%Rank #2Claude 3.5 Sonnet93.1%Rank #3Gemini 1.5 Pro89.2%Rank #4Gemma 3 27B87.6%

What is BIG-Bench Hard?

What BIG-Bench Hard measures and how its scores work.

BIG-Bench Hard (BBH) is a subset of 23 challenging BIG-Bench tasks selected because prior language model evaluations did not outperform average human-rater performance. The benchmark contains 6,511 evaluation examples testing various forms of multi-step reasoning including arithmetic, logical reasoning (Boolean expressions, logical deduction), geometric reasoning, temporal reasoning, and language understanding. Tasks require capabilities such as causal judgment, object counting, navigation, pattern recognition, and complex problem solving.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
BIG-Bench Hard
Modality
text
Primary category
language
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
big-bench-hard|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about BIG-Bench Hard.

Which model scores highest on BIG-Bench Hard?

Claude 3.5 Sonnet is currently ranked first with 93.1%.

What does BIG-Bench Hard measure?

BIG-Bench Hard (BBH) is a subset of 23 challenging BIG-Bench tasks selected because prior language model evaluations did not outperform average human-rater performance. The benchmark contains 6,511 evaluation examples testing various forms of multi-step reasoning including arithmetic, logical reasoning (Boolean expressions, logical deduction), geometric reasoning, temporal reasoning, and language understanding. Tasks require capabilities such as causal judgment, object counting, navigation, pattern recognition, and complex problem solving.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

21 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.