Model Accuracy vs Pass@3
100%
75%
50%
25%
0%
Gemini-3-Pro-Preview
GPT-5.2 (high)
Claude-Opus-4.5
Grok-4.1-fast
GPT-5.1 (high)
Deepseek-V3.2
Solar-Open-100B
K-EXAONE-236B-A23B
K-EXAONE-236B-A23B
Kanana-2-30B-Thinking-2601
Solar-Pro-2 (31B)(high)
Kanana-2-30B-Thinking
HCX-007(high)
EXAONE-4.0.1-32B (high)
A.X-4.0 (72B)
Llama-VARCO-8B-Instruct
Accuracy
Pass@3
Avg Token Usage (Per Problem)
112K
84K
56K
28K
0
K-EXAONE-236B-A23B
K-EXAONE-236B-A23B
Grok-4.1-fast
Solar-Open-100B
Kanana-2-30B-Thinking-2601
Kanana-2-30B-Thinking
Deepseek-V3.2
Gemini-3-Pro-Preview
Claude-Opus-4.5
Solar-Pro-2 (31B)(high)
GPT-5.1 (high)
EXAONE-4.0.1-32B (high)
GPT-5.2 (high)
Llama-VARCO-8B-Instruct
HCX-007(high)
A.X-4.0 (72B)
Avg Tokens / Problem
EntropyMath is an evolutionary multi-agent system and benchmark that generates high-entropy math problems designed to systematically break current LLMs. The EntropyMath_SAT_50 dataset challenges models with SAT-style problems derived from the Korean, Indian, and Japanese College Scholastic Ability Test (CSAT). These problems demand not only high-precision calculation but also deep conceptual understanding and logical inference, representing a significant challenge even for advanced LLMs.
Results are reported using Pass@3 metrics to account for generation variance. Detailed execution traces are available for transparency.
Performance Legend
Mastery (100%)
3/3
Strong (66%)
2/3
Weak (33%)
1/3
Fail (0%)
0/3
Leaderboard / SAT · 50 problems
Change benchmark ↑Scroll horizontally to see all problems. Select a result cell to view the problem and recorded response.


