AI Model Benchmarks 2026
Compare 32 frontier AI models across 7 benchmarks. Real scores from independent evaluations — not marketing claims.
Microsoft
Live Leaderboard
| Rank | Model | Company | MMLU | HumanEval | MATH | GPQA | Arena ELO | Overall | Cost/1M | Context |
|---|---|---|---|---|---|---|---|---|---|---|
1 | Claude Opus 4.6 | Anthropic | 94.2 | 95.1 | 97.6 | 90.5 | 1476 | 94.9 | $5.00 | 200K |
22 | Gemini 3.1 Pro | 94.3 | 93.2 | 98.1 | 94.1 | 1222 | 94.2 | $2.50 | 1M | |
33 | GPT-5.4 | OpenAI | 93.8 | 96.3 | 97.9 | 92.8 | 1146 | 93.8 | $2.50 | 1M |
41 | GPT-5.2 | OpenAI | 92.1 | 94.8 | 98.0 | 91.4 | 1172 | 92.5 | $1.75 | 400K |
51 | Gemini 3 Pro | 93.5 | 92.0 | 98.5 | 91.9 | 1045 | 92.1 | $2.00 | 1M | |
61 | GLM-5? | Zhipu AI | 91.2 | 90.5 | 95.3 | 77.8 | 1179 | 89.8 | $1.00 | 200K |
72 | Claude Opus 4.5 | Anthropic | 92.8 | 94.2 | 96.1 | 87.0 | 1345 | 90.4 | $5.00 | 200K |
8 | Claude Sonnet 4.6 | Anthropic | 91.8 | 93.8 | 97.8 | 89.9 | 941 | 89.5 | $3.00 | 200K |
92 | GPT-5 | OpenAI | 91.5 | 92.1 | 97.9 | 88.1 | 1037 | 89.3 | $1.25 | 400K |
104 | Kimi K2.5? | Moonshot AI | 90.4 | 89.3 | 96.8 | 87.6 | 988 | 87.5 | $0.60 | 262K |
11 | Gemini 3 Flash | 90.8 | 89.6 | 95.2 | 90.4 | 1172 | 88.5 | $0.50 | 1M | |
125 | GLM-5.1? | Zhipu AI | 89.7 | 88.1 | 94.5 | 86.2 | 1329 | 86.3 | $1.40 | 200K |
132 | Qwen 3.5 397B397B (MoE A17B) | Alibaba | 89.5 | 88.9 | 95.6 | 88.4 | 1067 | 87.4 | $0.60 | 262K |
143 | GPT-4.1 | OpenAI | 88.2 | 91.5 | 93.8 | 80.5 | 1360 | 85.8 | $2.00 | 1M |
152 | Claude Sonnet 4.5 | Anthropic | 89.1 | 90.8 | 97.7 | 83.4 | 1294 | 86.3 | $3.00 | 200K |
163 | DeepSeek V4? | DeepSeek | 88.5 | 89.2 | 94.1 | 82.3 | 1080 | 85.4 | $0.27 | 128K |
172 | Grok 4 | xAI | 88.0 | 87.5 | 93.2 | 81.8 | 1050 | 84.3 | $3.00 | 256K |
181 | Llama 4 Maverick400B (MoE 17B active) | Meta | 87.2 | 86.1 | 91.5 | 78.2 | 1180 | 82.7 | $0.20 | 10M |
194 | Gemini 2.5 Pro | 87.8 | 88.4 | 92.3 | 79.8 | 1350 | 84.1 | $1.25 | 1M | |
203 | DeepSeek V3671B (MoE 37B active) | DeepSeek | 85.1 | 84.2 | 88.5 | 72.4 | 1150 | 79.4 | $0.27 | 128K |
212 | GPT-4o | OpenAI | 83.5 | 86.5 | 85.2 | 68.4 | 1280 | 77.3 | $2.50 | 128K |
223 | Claude 3.5 Sonnet | Anthropic | 82.1 | 85.8 | 84.1 | 65.3 | 1200 | 75.8 | $3.00 | 200K |
23 | Llama 4 Scout109B (MoE 17B active) | Meta | 82.5 | 82.3 | 86.1 | 70.5 | 980 | 79.1 | $0.11 | 10M |
24 | Qwen 3.5 122B122B (MoE A10B) | Alibaba | 84.2 | 84.8 | 90.1 | 86.6 | 1134 | 83.8 | $0.40 | 262K |
252 | MiniMax M2.5? | MiniMax | 83.8 | 83.5 | 89.2 | 74.8 | 1020 | 81.5 | $0.20 | 1M |
264 | Mistral Large | Mistral AI | 81.2 | 80.1 | 82.5 | 62.1 | 1120 | 73.2 | $2.00 | 128K |
273 | Gemma 4 31B31B | 80.5 | 79.8 | 83.8 | 68.5 | 950 | 76.2 | $0.10 | 128K | |
282 | Phi-414B | Microsoft | 78.5 | 82.5 | 80.8 | 61.5 | 1080 | 71.9 | $0.07 | 16K |
295 | Grok 3 | xAI | 84.5 | 83.5 | 87.8 | 72.1 | 1380 | 80.3 | $3.00 | 131K |
303 | Command R+104B | Cohere | 75.2 | 75.0 | 72.8 | 55.2 | 1060 | 66.5 | $2.50 | 128K |
316 | Llama 3.1 405B405B | Meta | 79.8 | 79.2 | 78.5 | 60.8 | 1150 | 71.3 | $3.00 | 128K |
324 | Mixtral 8x22B141B (MoE 39B active) | Mistral AI | 72.8 | 71.5 | 68.2 | 48.5 | 1030 | 62.3 | $0.65 | 65K |
32 models · Sorted by Rank (ascending)
Compare Models
Select up to 3 models to see side-by-side benchmark comparisons.
Select up to 3 models to compare:
| Metric | Claude Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro |
|---|---|---|---|
| Overall Score | 94.9 | 93.8 | 94.2 |
| Cost/1M In | $5 | $2.5 | $2.5 |
| Cost/1M Out | $25 | $15 | $15 |
| Context Window | 200K | 1M | 1M |
| Category | proprietary | proprietary | proprietary |
Methodology
Last updated: April 7, 2026Our benchmark scores are compiled from independently-run evaluations by Epoch AI, Scale AI, LMSYS Chatbot Arena, and official model technical reports. We prioritize independently verified scores over self-reported numbers where available. All scores represent the best reported result for each model on each benchmark.
MMLU
Massive Multitask Language Understanding. Tests knowledge across 57 subjects including STEM, humanities, and social sciences. 14,000+ multiple-choice questions.
MMLU-Pro
Harder version of MMLU with 10 choices per question (vs 4) and more complex reasoning required. Better at differentiating top models.
HumanEval
Coding benchmark with 164 Python programming challenges. Tests function implementation from docstrings. Reports pass@1 rate.
MATH
Competition-style mathematics problems across algebra, geometry, number theory, and more. Level 5 (hardest) used for frontier model comparison.
GPQA Diamond
Graduate-level science questions in physics, chemistry, and biology. PhD experts validate questions; random guessing yields ~25%.
Arena ELO
Chatbot Arena rating from LMSYS. Real humans vote on model outputs in blind head-to-head comparisons. Measures conversational quality.
SimpleQA
Factual accuracy benchmark testing whether models give correct, concise answers to straightforward questions. Measures hallucination rate.
How We Calculate Overall Score
The overall score is a weighted composite of normalized benchmark results:
- • MMLU — 15% weight (breadth of knowledge)
- • MMLU-Pro — 15% weight (harder reasoning)
- • HumanEval — 20% weight (coding ability)
- • MATH — 15% weight (mathematical reasoning)
- • GPQA Diamond — 15% weight (scientific reasoning)
- • Arena ELO — 10% weight (real-world preference)
- • SimpleQA — 10% weight (factual accuracy)
Data Sources
About These Benchmarks
Benchmarks measure specific capabilities under controlled conditions and may not fully represent real-world performance. Different prompting strategies, system prompts, and tool use can significantly change results. We recommend trying models on your specific tasks using our tool finder or the AI tools directory.
AI Model Benchmarks: What You Need to Know in 2026
The AI landscape in 2026 is dominated by a handful of frontier models that push the boundaries of reasoning, coding, and scientific knowledge. Our LLM leaderboard tracks the latest benchmark scores from independent evaluations, giving you an objective way to compare models like GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro.
Whether you're choosing a model for coding assistance, research, or general chatbot use, benchmarks like MMLU (knowledge), HumanEval (coding),GPQA Diamond (science), and Chatbot Arena ELO (human preference) provide concrete data points for comparison.
Key Takeaways for April 2026
- Claude Opus 4.6 leads in agentic coding (SWE-bench) and visual reasoning
- Gemini 3.1 Pro tops MMLU, GPQA, and Humanity's Last Exam
- GPT-5.4 excels in factual accuracy and overall reasoning
- Open-source models like Qwen 3.5 and Llama 4 offer strong performance at a fraction of the cost
- DeepSeek V4 provides the best value proposition for cost-sensitive applications
Explore our full AI tools directory for detailed reviews of each model, or use the AI Tool Finder to discover which model best fits your needs.