Searchable comparison of the most important LLM evaluation benchmarks 2024-2025
Benchmarks are standardized tests for measuring LLM capabilities. From AIME (mathematics) to ELAIPBench (enterprise) - each benchmark measures different dimensions of model intelligence.
Research Explorer for navigating current research.
Without benchmark understanding, model comparison is impossible. Choosing the right benchmark depends on the use case - reasoning benchmarks differ from coding benchmarks.
| Model | Score | Release | Parameters | Type |
|---|
American Invitational Mathematics Exam. 30 problems per year. Olympiad-level difficulty. Best indicator for true reasoning capability. GPT-4: 94%, Claude 4.5: 96%, DeepSeek-R1: 98%.
New Enterprise LLM Evaluation Platform. Focus: Production readiness, not just raw performance. Evaluates: Reliability, Safety, Cost-Efficiency. More realistic scores than toy benchmarks.
2025 benchmark specifically for test-time reasoning. Measures: Quality, Speed, Efficiency of Chain-of-Thought. Designed for GRPO/RL-trained models. Claude 4.5, GPT-5.1, DeepSeek-R1 optimized.
Pure logical puzzles without domain knowledge. Tests raw reasoning without memorized knowledge. Also a benchmark for consistency & self-correction across iterations.
Data leakage is a problem: Models trained on benchmark data. Therefore: new benchmarks 2024-2025 (ThinkBench, ELAIPBench) with fresh data. Old benchmarks (ARC, MMLU) less informative now.
Trend: Companies create custom benchmarks for their use cases (e.g., Legal Reasoning, Medical Diagnosis). Generic benchmarks less relevant. Shift to domain-specific evaluation.