Compare top models across the benchmark suite that best represents knowledge performance. Use this page as the fastest way to inspect the relevant tests, then jump into the full matrix when you want broader context.
3
Benchmarks in category
46
Models with coverage
1
Benchmarks with human baseline
1
Saturated benchmarks
The current benchmark set in this category, with context on what each test captures.
Shows how well a model has absorbed factual knowledge during training. Saturating above 90%, so less useful for differentiating frontier models.
Better at differentiating top models since scores are 16-33% lower than standard MMLU. Tests reasoning in addition to knowledge.
GPT-4o scores below 40%, making it surprisingly challenging. Tests honesty and factual reliability, not just knowledge breadth.
Tests broad knowledge across 57 academic subjects (STEM, humanities, social sciences) with 16,000 multiple-choice questions. The most widely-cited LLM benchmark.
Why it matters
Shows how well a model has absorbed factual knowledge during training. Saturating above 90%, so less useful for differentiating frontier models.
| # | Model | Score |
|---|---|---|
| 1 | 🥇GPT-5.4 | 94.0% |
| 2 | 🥈GPT-5.2 | 93.5% |
| 3 | 🥉GPT-5 | 93.0% |
| 4 | Gemini 3 Pro | 92.5% |
| 5 | o3 | 92.3% |
| 6 | Claude Opus 4.6 | 92.1% |
| 7 | o1 | 91.8% |
| 8 | DeepSeek R1-0528 | 91.5% |
| 9 | Grok 4 | 91.5% |
| 10 | Claude Opus 4.5 | 91.4% |
| 11 | Claude Sonnet 4.6 | 91.2% |
| 12 | Claude Opus 4 | 91.0% |
| 13 | Gemini 2.5 Pro | 90.8% |
| 14 | DeepSeek R1 | 90.8% |
| 15 | Claude Sonnet 4.5 | 90.8% |
| 16 | Claude 3.7 Sonnet | 90.2% |
| 17 | Claude Sonnet 4 | 89.5% |
| 18 | GPT-4.1 | 89.2% |
| 19 | DeepSeek V3 (March 2025) | 89.2% |
| 20 | GPT-4o | 88.7% |
| 21 | Claude 3.5 Sonnet | 88.7% |
| 22 | Llama 3.1 405B | 88.6% |
| 23 | DeepSeek V3 | 88.5% |
| 24 | Grok 3 | 88.5% |
| 25 | Llama 4 Maverick | 88.0% |
| 26 | Gemini 3 Flash | 88.0% |
| 27 | Grok 2 | 87.5% |
| 28 | o3-mini | 86.9% |
| 29 | Claude 3 Opus | 86.8% |
| 30 | GPT-4 Turbo | 86.5% |
| 31 | Llama 3.3 70B | 86.3% |
| 32 | Qwen 2.5 72B | 86.1% |
| 33 | Llama 3.1 70B | 86.0% |
| 34 | Gemini 1.5 Pro | 85.9% |
| 35 | Gemini 2.5 Flash | 85.8% |
| 36 | o1-mini | 85.2% |
| 37 | Phi-4 | 84.8% |
| 38 | Mistral Large 2 | 84.7% |
| 39 | Claude Haiku 4.5 | 84.5% |
| 40 | Mistral Large 2 | 84.0% |
| 41 | GPT-4o mini | 82.0% |
| 42 | Claude 3.5 Haiku | 80.9% |
| 43 | Mixtral 8x22B | 77.3% |
| 44 | Gemini 2.0 Flash | 76.4% |
| 45 | Command R+ | 75.7% |
| 46 | Gemma 2 27B | 75.2% |
Performance Tiers
Model Types
Saturated benchmarks have top models clustered above 90%, making them less useful for comparison.
Scores sourced from official model cards, technical reports, and third-party evaluations (Artificial Analysis, LMSYS Arena). Last updated: 2026-03-07. Some scores are approximate.
AI benchmarks are grouped into categories like coding, math, reasoning, knowledge, and safety. Each category contains multiple standardized tests that measure specific aspects of model performance. This page focuses on one category so you can compare models within a specific skill area.
Each benchmark has its own scoring method - accuracy percentage, pass rate, Elo rating, or normalized score. We display raw scores from official evaluations and community-run tests. Scores are updated hourly as new evaluation results become available.
A saturated benchmark is one where top models score near the maximum (typically above 95%). This means the benchmark no longer effectively differentiates between the best models, and newer, harder benchmarks are needed to measure progress.