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<title>LLM Stats Benchmarks</title>
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LLM Stats Benchmarks
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<p class="text-sm text-gray-500 mt-1">Comprehensive leaderboard of AI model performance across diverse tasks</p>
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<input type="text" id="searchInput" placeholder="Search benchmarks..."
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<p class="text-gray-500 text-lg">No benchmarks found matching your criteria.</p>
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Data sourced from <a href="https://llm-stats.com/benchmarks" class="text-indigo-600 hover:underline font-medium" target="_blank" rel="noopener">llm-stats.com/benchmarks</a>
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<script>
const benchmarks = [
{
name: "GPQA",
category: "physics",
description: "A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult.",
topModels: [
{ rank: 1, name: "GPT-5.6 Sol", score: 94.6 },
{ rank: 2, name: "Claude Mythos Preview", score: 94.6 },
{ rank: 3, name: "Gemini 3.1 Pro", score: 94.3 },
{ rank: 4, name: "Claude Opus 4.7", score: 94.2 },
{ rank: 5, name: "Claude Opus 4.8", score: 93.6 }
]
},
{
name: "MMLU-Pro",
category: "language",
description: "A robust multi-task language understanding benchmark extending MMLU with 10 options, eliminating trivial questions, and focusing on reasoning-intensive tasks.",
topModels: [
{ rank: 1, name: "Qwen3.7 Max", score: 89.6 },
{ rank: 2, name: "Qwen3.7-Plus", score: 88.5 },
{ rank: 3, name: "Qwen3.6 Plus", score: 88.5 },
{ rank: 4, name: "MiniMax M2.1", score: 88.0 },
{ rank: 5, name: "Qwen3.5-397B-A17B", score: 87.8 }
]
},
{
name: "AIME 2025",
category: "math",
description: "All 30 problems from the 2025 American Invitational Mathematics Examination, testing olympiad-level mathematical reasoning with integer answers.",
topModels: [
{ rank: 1, name: "GPT-5.2 Pro", score: 100.0 },
{ rank: 2, name: "GPT-5.2", score: 100.0 },
{ rank: 3, name: "Gemini 3 Pro", score: 100.0 },
{ rank: 4, name: "Kimi K2-Thinking-0905", score: 100.0 },
{ rank: 5, name: "Grok-4 Heavy", score: 100.0 }
]
},
{
name: "SWE-Bench Verified",
category: "reasoning",
description: "A verified subset of 500 software engineering problems from real GitHub issues, evaluating language models' ability to resolve real-world coding issues.",
topModels: [
{ rank: 1, name: "Claude Fable 5", score: 95.0 },
{ rank: 2, name: "Claude Mythos Preview", score: 93.9 },
{ rank: 3, name: "Claude Opus 4.8", score: 88.6 },
{ rank: 4, name: "Claude Opus 4.7", score: 87.6 },
{ rank: 5, name: "Claude Sonnet 5", score: 85.2 }
]
},
{
name: "MMLU",
category: "language",
description: "Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, and social sciences.",
topModels: [
{ rank: 1, name: "GPT-5", score: 92.5 },
{ rank: 2, name: "o1", score: 91.8 },
{ rank: 3, name: "GPT-4.5", score: 90.8 },
{ rank: 4, name: "o1-preview", score: 90.8 },
{ rank: 5, name: "Sarvam-105B", score: 90.6 }
]
},
{
name: "Humanity's Last Exam",
category: "math",
description: "A multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge.",
topModels: [
{ rank: 1, name: "Claude Mythos Preview", score: 64.7 },
{ rank: 2, name: "Claude Fable 5", score: 64.5 },
{ rank: 3, name: "Muse Spark 1.1", score: 62.1 },
{ rank: 4, name: "Muse Spark", score: 58.4 },
{ rank: 5, name: "Claude Opus 4.8", score: 57.9 }
]
},
{
name: "LiveCodeBench",
category: "reasoning",
description: "A holistic and contamination-free evaluation benchmark for LLMs for code, continuously collecting new problems from programming contests (LeetCode, AtCoder, CodeForces).",
topModels: [
{ rank: 1, name: "DeepSeek-V4-Pro-Max", score: 93.5 },
{ rank: 2, name: "DeepSeek-V4-Flash-Max", score: 91.6 },
{ rank: 3, name: "DeepSeek-V3.2", score: 83.3 },
{ rank: 4, name: "DeepSeek-V3.2 (Thinking)", score: 83.3 },
{ rank: 5, name: "MiniMax M2", score: 83.0 }
]
},
{
name: "MATH",
category: "math",
description: "Contains 12,500 challenging competition mathematics problems from AMC 10, AMC 12, AIME, and other mathematics competitions with full step-by-step solutions.",
topModels: [
{ rank: 1, name: "o3-mini", score: 97.9 },
{ rank: 2, name: "o1", score: 96.4 },
{ rank: 3, name: "MiniStral 3 (14B)", score: 90.4 },
{ rank: 4, name: "Mistral Large 3", score: 90.4 },
{ rank: 5, name: "Gemini 2.0 Flash", score: 89.7 }
]
},
{
name: "HumanEval",
category: "reasoning",
description: "A benchmark that measures functional correctness for synthesizing programs from docstrings, consisting of 164 original programming problems.",
topModels: [
{ rank: 1, name: "MiniCPM-SALA", score: 95.1 },
{ rank: 2, name: "Kimi K2 0905", score: 94.5 },
{ rank: 3, name: "Claude 3.5 Sonnet", score: 93.7 },
{ rank: 4, name: "GPT-5", score: 93.4 },
{ rank: 5, name: "Kimi K2 Instruct", score: 93.3 }
]
},
{
name: "IFEval",
category: "instruction following",
description: "Instruction-Following Evaluation benchmark for large language models, focusing on verifiable instructions with 25 types of instructions and around 500 prompts.",
topModels: [
{ rank: 1, name: "Qwen3.5-27B", score: 95.0 },
{ rank: 2, name: "Qwen3.7-Plus", score: 94.6 },
{ rank: 3, name: "Qwen3.7 Max", score: 94.3 },
{ rank: 4, name: "Qwen3.6 Plus", score: 94.3 },
{ rank: 5, name: "o3-mini", score: 93.9 }
]
},
{
name: "MMMU-Pro",
category: "multimodal",
description: "A robust multi-discipline multimodal understanding benchmark that enhances MMMU through filtering text-only answerable questions and introducing vision-only input settings.",
topModels: [
{ rank: 1, name: "Gemini 3.5 Flash", score: 83.6 },
{ rank: 2, name: "GPT-5.5", score: 83.2 },
{ rank: 3, name: "GPT-5.6 Sol", score: 83.0 },
{ rank: 4, name: "Seed 2.1 Pro", score: 82.7 },
{ rank: 5, name: "Seed 2.1 Turbo", score: 82.2 }
]
},
{
name: "MMMU",
category: "multimodal",
description: "Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning.",
topModels: [
{ rank: 1, name: "Qwen3.6 Plus", score: 86.0 },
{ rank: 2, name: "GPT-5.1", score: 85.4 },
{ rank: 3, name: "GPT-5.1 Instant", score: 85.4 },
{ rank: 4, name: "GPT-5.1 Thinking", score: 85.4 },
{ rank: 5, name: "GPT-5", score: 84.2 }
]
},
{
name: "BrowseComp",
category: "reasoning",
description: "A benchmark comprising 1,266 questions that challenge AI agents to persistently navigate the internet in search of hard-to-find, entangled information.",
topModels: [
{ rank: 1, name: "Kimi K3", score: 91.2 },
{ rank: 2, name: "GPT-5.6 Sol", score: 90.4 },
{ rank: 3, name: "GPT-5.5 Pro", score: 90.1 },
{ rank: 4, name: "GPT-5.6 Terra", score: 87.5 },
{ rank: 5, name: "Claude Mythos Preview", score: 86.9 }
]
},
{
name: "AIME 2024",
category: "math",
description: "American Invitational Mathematics Examination 2024, consisting of 30 challenging mathematical reasoning problems from AIME I and AIME II competitions.",
topModels: [
{ rank: 1, name: "Grok-3 Mini", score: 95.8 },
{ rank: 2, name: "o4-mini", score: 93.4 },
{ rank: 3, name: "LongCat-Flash-Thinking", score: 93.3 },
{ rank: 4, name: "Grok-3", score: 93.3 },
{ rank: 5, name: "Gemini 2.5 Pro", score: 92.0 }
]
},
{
name: "GSM8k",
category: "math",
description: "Grade School Math 8K, a dataset of 8.5K high-quality linguistically diverse grade school math word problems requiring multi-step reasoning.",
topModels: [
{ rank: 1, name: "MiMo-V2.5-Pro", score: 99.6 },
{ rank: 2, name: "Kimi K2 Instruct", score: 97.3 },
{ rank: 3, name: "o1", score: 97.1 },
{ rank: 4, name: "GPT-4.5", score: 97.0 },
{ rank: 5, name: "Llama 3.1 405B Instruct", score: 96.8 }
]
}
];
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"math": "bg-blue-100 text-blue-800 border-blue-200",
"reasoning": "bg-purple-100 text-purple-800 border-purple-200",
"language": "bg-green-100 text-green-800 border-green-200",
"multimodal": "bg-pink-100 text-pink-800 border-pink-200",
"physics": "bg-indigo-100 text-indigo-800 border-indigo-200",
"instruction following": "bg-yellow-100 text-yellow-800 border-yellow-200",
"coding": "bg-red-100 text-red-800 border-red-200",
"long context": "bg-teal-100 text-teal-800 border-teal-200",
"legal": "bg-gray-100 text-gray-800 border-gray-200",
"spatial reasoning": "bg-orange-100 text-orange-800 border-orange-200",
"general": "bg-slate-100 text-slate-800 border-slate-200",
"image to text": "bg-cyan-100 text-cyan-800 border-cyan-200"
};
const categoryDisplay = {
"math": "Math",
"reasoning": "Reasoning",
"language": "Language",
"multimodal": "Multimodal",
"physics": "Physics",
"instruction following": "Instruction Following",
"coding": "Coding",
"long context": "Long Context",
"legal": "Legal",
"spatial reasoning": "Spatial Reasoning",
"general": "General",
"image to text": "Image to Text"
};
// Populate category filter
const categoryFilter = document.getElementById('categoryFilter');
const uniqueCategories = [...new Set(benchmarks.map(b => b.category))].sort();
uniqueCategories.forEach(cat => {
const option = document.createElement('option');
option.value = cat;
option.textContent = categoryDisplay[cat] || cat.charAt(0).toUpperCase() + cat.slice(1);
categoryFilter.appendChild(option);
});
function renderBenchmarks(data) {
const grid = document.getElementById('benchmarkGrid');
const noResults = document.getElementById('noResults');
grid.innerHTML = '';
if (data.length === 0) {
noResults.classList.remove('hidden');
return;
}
noResults.classList.add('hidden');
data.forEach(benchmark => {
const colorClass = categoryColors[benchmark.category] || "bg-gray-100 text-gray-800 border-gray-200";
const categoryName = categoryDisplay[benchmark.category] || benchmark.category.charAt(0).toUpperCase() + benchmark.category.slice(1);
const maxScore = Math.max(...benchmark.topModels.map(m => m.score));
const card = document.createElement('div');
card.className = 'bg-white rounded-xl border border-gray-200 p-6 card-hover fade-in flex flex-col';
card.innerHTML = `
<div class="flex items-start justify-between mb-3">
<span class="inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium border ${colorClass}">
${categoryName}
</span>
</div>
<h3 class="text-xl font-bold text-gray-900 mb-2">${benchmark.name}</h3>
<p class="text-sm text-gray-600 mb-6 flex-grow leading-relaxed">${benchmark.description}</p>
<div class="space-y-3 mt-auto">
<h4 class="text-xs font-semibold text-gray-500 uppercase tracking-wider">Top Performers</h4>
${benchmark.topModels.map((model, index) => `
<div class="flex items-center gap-3">
<span class="flex-shrink-0 w-5 h-5 flex items-center justify-center rounded-full text-xs font-bold ${index === 0 ? 'bg-yellow-100 text-yellow-700' : 'bg-gray-100 text-gray-600'}">
${model.rank}
</span>
<div class="flex-grow min-w-0">
<div class="flex justify-between items-center mb-1">
<span class="text-sm font-medium text-gray-900 truncate">${model.name}</span>
<span class="text-sm font-bold text-indigo-600">${model.score}%</span>
</div>
<div class="w-full bg-gray-100 rounded-full h-1.5">
<div class="score-bar bg-indigo-500 h-1.5 rounded-full" style="width: ${(model.score / 100) * 100}%"></div>
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</div>
</div>
`).join('')}
</div>
`;
grid.appendChild(card);
});
}
function filterBenchmarks() {
const searchTerm = document.getElementById('searchInput').value.toLowerCase();
const selectedCategory = document.getElementById('categoryFilter').value;
const filtered = benchmarks.filter(benchmark => {
const matchesSearch = benchmark.name.toLowerCase().includes(searchTerm) ||
benchmark.description.toLowerCase().includes(searchTerm) ||
benchmark.topModels.some(m => m.name.toLowerCase().includes(searchTerm));
const matchesCategory = selectedCategory === 'all' || benchmark.category === selectedCategory;
return matchesSearch && matchesCategory;
});
renderBenchmarks(filtered);
}
document.getElementById('searchInput').addEventListener('input', filterBenchmarks);
document.getElementById('categoryFilter').addEventListener('change', filterBenchmarks);
// Initial render
renderBenchmarks(benchmarks);
</script>
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