reasoning benchmark
AutoLogi
AutoLogi is an automated method for synthesizing open-ended logic puzzles to evaluate reasoning abilities of Large Language Models. The benchmark addresses limitations of existing multiple-choice reasoning evaluations by featuring program-based verification and controllable difficulty levels. It includes 1,575 English and 883 Chinese puzzles, enabling more reliable evaluation that better distinguishes models' reasoning capabilities across languages.
Updated Aug 7, 2026
Published models2
Registry coverage2
MetricScore
EvidenceB
AutoLogi leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | MA | 89.5% | 100.0% | 2 | C | |
| 2 | MA | 89.5% | 0.0% | 2 | C |
Score distribution
Top published rows on the benchmark's original scale.
AutoLogi highlights
The top published results on this benchmark's own scale.
What is AutoLogi?
Definition and scoring fields from the benchmark registry.
AutoLogi is an automated method for synthesizing open-ended logic puzzles to evaluate reasoning abilities of Large Language Models. The benchmark addresses limitations of existing multiple-choice reasoning evaluations by featuring program-based verification and controllable difficulty levels. It includes 1,575 English and 883 Chinese puzzles, enabling more reliable evaluation that better distinguishes models' reasoning capabilities across languages.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- AutoLogi
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- autologi|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
FAQ
Common questions about AutoLogi.
Which model scores highest on AutoLogi?
Kimi K2 Instruct is currently ranked first with 89.5%.
What does AutoLogi measure?
AutoLogi is an automated method for synthesizing open-ended logic puzzles to evaluate reasoning abilities of Large Language Models. The benchmark addresses limitations of existing multiple-choice reasoning evaluations by featuring program-based verification and controllable difficulty levels. It includes 1,575 English and 883 Chinese puzzles, enabling more reliable evaluation that better distinguishes models' reasoning capabilities across languages.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.