Standardized benchmark results for 20 leading AI models across 8 widely recognised evaluation suites. Scores are sourced from official model cards, research papers and reproducible third-party evaluations, last refreshed July 2026.
| Model | Provider | MMLU | HumanEval | MATH | GPQA | GSM8K | SWE-bench | MT-Bench | MMMU |
|---|---|---|---|---|---|---|---|---|---|
| Claude Fable 5 | Anthropic | ★ 94.6 | ★ 96.2 | 95.1 | 84.7 | 98.9 | ★ 79.4 | ★ 9.7 | 86.2 |
| Claude Opus 5 | Anthropic | 94.1 | 95.8 | 94.2 | 83.1 | 98.7 | 78.1 | 9.6 | 85.3 |
| GPT-5.6 Sol | OpenAI | 93.8 | 94.7 | ★ 97.8 | ★ 89.6 | ★ 99.3 | 74.6 | 9.4 | 83.1 |
| GPT-5.6 | OpenAI | 93.1 | 95.4 | 93.4 | 80.2 | 98.6 | 75.8 | 9.5 | 82.6 |
| Gemini 3.6 Pro | 92.4 | 92.8 | 92.1 | 78.4 | 97.9 | 70.3 | 9.4 | ★ 89.7 | |
| Claude Sonnet 5 | Anthropic | 92.2 | 94.1 | 90.8 | 76.3 | 97.4 | 72.6 | 9.4 | 82.1 |
| Claude Opus 4.8 | Anthropic | 92.8 | 93.6 | 92.3 | 77.1 | 97.6 | 71.2 | 9.4 | 83.4 |
| Gemini 3.6 Flash | 87.6 | 88.2 | 84.7 | 64.8 | 93.8 | 56.4 | 8.9 | 84.2 | |
| Grok 4 | xAI | 90.2 | 90.6 | 89.4 | 72.1 | 96.2 | 62.8 | 9.1 | 79.6 |
| Llama 5 Behemoth | Meta | 90.8 | 89.4 | 88.1 | 71.4 | 95.8 | 60.2 | 8.9 | 81.3 |
| Kimi K3.7 | Moonshot AI | 89.1 | 92.3 | 88.6 | 69.8 | 95.4 | 66.7 | 8.8 | 74.2 |
| GPT-5.6 Mini | OpenAI | 87.4 | 89.6 | 84.2 | 63.7 | 93.1 | 55.3 | 8.8 | 76.4 |
| DeepSeek V4.5 | DeepSeek | 88.7 | 90.8 | 87.3 | 68.2 | 95.1 | 58.6 | 8.9 | 72.8 |
| DeepSeek R2 | DeepSeek | 88.2 | 87.1 | 96.4 | 82.6 | 99.0 | 52.1 | 8.6 | 68.4 |
| Qwen 3.8 | Alibaba | 87.9 | 88.4 | 85.7 | 66.1 | 94.3 | 54.8 | 8.7 | 74.6 |
| Llama 5 Maverick | Meta | 86.4 | 85.7 | 82.1 | 62.8 | 93.2 | 50.4 | 8.6 | 79.1 |
| Claude Haiku 4.5 | Anthropic | 80.4 | 81.3 | 74.2 | 52.1 | 86.4 | 38.7 | 8.3 | 72.1 |
| GLM-5 | Zhipu AI | 85.8 | 86.9 | 82.6 | 61.4 | 92.7 | 49.7 | 8.5 | 70.2 |
| Mistral Large 3.1 | Mistral | 83.1 | 82.4 | 76.8 | 55.2 | 89.4 | 44.1 | 8.3 | 68.7 |
| Llama 5 Scout | Meta | 80.2 | 79.8 | 72.4 | 50.6 | 86.1 | 39.8 | 8.1 | 73.4 |
Tests breadth of knowledge. High scores indicate strong general academic understanding across STEM, humanities, and professional domains.
Evaluates ability to write correct code from docstrings. Directly relevant for coding assistants and software development use cases.
Measures mathematical reasoning from AMC to AIME difficulty. Correlates with broader reasoning capability.
Expert-level science questions that stump even PhD students. A tough frontier test of genuine understanding vs. pattern matching.
Tests multi-step arithmetic reasoning in plain language. Near-saturated for frontier models - now mainly a baseline check.
Measures real-world software engineering. The gold standard for agentic coding: resolve GitHub issues end-to-end.
LLM-judged multi-turn conversations. Reflects instruction-following quality in realistic chat and assistant scenarios.
The leading multimodal benchmark. Tests chart understanding, scientific diagrams, and cross-modal reasoning simultaneously.