reasoning benchmark
ZebraLogic is an evaluation framework for assessing large language models' logical reasoning capabilities through logic grid puzzles derived from constraint satisfaction problems (CSPs). The benchmark consists of 1,000 programmatically generated puzzles with controllable and quantifiable complexity, revealing a 'curse of complexity' where model accuracy declines significantly as problem complexity grows.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 97.3% | 100.0% | 8 | C | |
| 02 | ME | 95.5% | 85.7% | 8 | C | |
| 03 | AC | 95.0% | 71.4% | 8 | C | |
| 04 | ME | 89.3% | 57.1% | 8 | C | |
| 05 | MA | 89.0% | 42.9% | 8 | C | |
| 06 | MA | 89.0% | 28.6% | 8 | C | |
| 07 | MI | 86.8% | 14.3% | 8 | C | |
| 08 | MI | 80.1% | 0.0% | 8 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What ZebraLogic measures and how its scores work.
ZebraLogic is an evaluation framework for assessing large language models' logical reasoning capabilities through logic grid puzzles derived from constraint satisfaction problems (CSPs). The benchmark consists of 1,000 programmatically generated puzzles with controllable and quantifiable complexity, revealing a 'curse of complexity' where model accuracy declines significantly as problem complexity grows.
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 ZebraLogic.
Qwen3 VL 235B A22B Thinking is currently ranked first with 97.3%.
ZebraLogic is an evaluation framework for assessing large language models' logical reasoning capabilities through logic grid puzzles derived from constraint satisfaction problems (CSPs). The benchmark consists of 1,000 programmatically generated puzzles with controllable and quantifiable complexity, revealing a 'curse of complexity' where model accuracy declines significantly as problem complexity grows.
Yes. Higher values rank better for this benchmark.
8 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.