Accuracy on MMLU's miscellaneous questions, one of 57 subject tests of academic and professional knowledge.
unassessed
| Category | knowledge |
|---|---|
| Subcategory | Other |
| Page status | active |
| Metric | accuracy |
| Direction | higher_is_better |
| Unit | % |
| Dataset size | 783 |
| Dataset licence | MIT |
| Publisher | UC Berkeley |
General-knowledge questions that did not fit any of MMLU's other 56 named subjects -- broad factual trivia spanning many domains -- which is also why this is one of the largest MMLU subject tests. Framed as four-option multiple-choice questions and scored zero-shot or few-shot by exact match against the labelled option, as one of the 57 subject subsets that make up the MMLU benchmark.
Four-option multiple-choice question answering (A-D), one correct answer, zero-shot or few-shot.
| Model | Provider | Score | Card as of |
|---|---|---|---|
| Meta Llama 3 70B | Meta | 91.8 | 2024-07 |
| Meta Llama 3 70B Instruct | Meta | 91.8 | 2026-04 |
| Meta Llama 3 70B Instruct | Nous Research | 91.8 | 2026-04 |
| Nous Hermes 2 Yi 34B | Nous Research | 91.1 | 2024-07 |
| Yi 1.5 34B 32K | 01.AI | 90.5 | 2024-07 |
| Yi 34B 200K | 01.AI | 90.3 | 2024-07 |
| Yi 1.5 34B | 01.AI | 90.2 | 2026-04 |
| Mixtral 8x22B Instruct v0.1 | Mistral AI | 89.9 | 2026-04 |
| Yi 1.5 34B Chat | 01.AI | 89.9 | 2026-04 |
| Yi 1.5 34B Chat 16K | 01.AI | 89.9 | 2024-07 |
| Yi 34B Chat | 01.AI | 89.9 | 2024-07 |
| Mixtral 8x7B Instruct v0.1 | Mistral AI | 88.0 | 2026-04 |
| Nous Hermes 2 Mixtral 8x7B DPO | Nous Research | 88.0 | 2024-07 |
| Mixtral 8x7B v0.1 | Mistral AI | 87.5 | 2026-04 |
| Hermes 2 Theta Llama 3 8B | Nous Research | 84.4 | 2024-07 |
| Yi 1.5 9B 32K | 01.AI | 84.4 | 2024-07 |
| Yi 1.5 9B Chat 16K | 01.AI | 84.2 | 2024-07 |
| Yi 9B | 01.AI | 83.9 | 2024-07 |
| gemma 7B it | Google DeepMind | 83.8 | 2024-07 |
| Yi 1.5 9B Chat | 01.AI | 83.4 | 2024-07 |
| Hermes 2 Pro Llama 3 8B | Nous Research | 83.1 | 2024-07 |
| Meta Llama 3 8B | Meta | 83.0 | 2024-07 |
| Meta Llama 3 8B | Nous Research | 83.0 | 2024-07 |
| Yi 1.5 9B | 01.AI | 83.0 | 2024-07 |
| Nous Hermes 2 SOLAR 10.7B | Nous Research | 82.8 | 2024-07 |
| Phi 3 mini 4K instruct | Microsoft | 82.8 | 2024-07 |
| Phi 3 mini 128K instruct | Microsoft | 81.6 | 2024-07 |
| Yi 6B | 01.AI | 80.7 | 2024-07 |
| Yi 6B Chat | 01.AI | 80.7 | 2024-07 |
| Yi 1.5 6B | 01.AI | 80.6 | 2024-07 |
| Meta Llama 3 8B Instruct | Meta | 79.8 | 2024-07 |
| Meta Llama 3 8B Instruct | Nous Research | 79.8 | 2024-07 |
| Mistral 7B v0.3 | Mistral AI | 79.8 | 2024-07 |
| mistral 7B v0.3 bnb 4bit | Unsloth | 79.8 | 2024-07 |
| Yi 1.5 6B Chat | 01.AI | 79.7 | 2024-07 |
| Mistral 7B Instruct v0.2 | Mistral AI | 78.0 | 2024-07 |
| falcon 40B | TII | 75.9 | 2024-07 |
| deepseek llm 7B base | DeepSeek | 73.1 | 2024-07 |
| deepseek llm 7B chat | DeepSeek | 73.1 | 2024-07 |
| Qwen2 1.5B Instruct | Alibaba / Qwen Team | 72.0 | 2024-07 |
| phi 2 | Microsoft | 69.2 | 2024-07 |
| chatglm2 6B | Zhipu AI | 59.6 | 2024-07 |
| gemma 2B | Google DeepMind | 54.7 | 2024-07 |
| Qwen2 0.5B Instruct | Alibaba / Qwen Team | 52.1 | 2024-07 |
| gemma 2B it | Google DeepMind | 46.2 | 2024-07 |
| deepseek coder 6.7B instruct | DeepSeek | 43.7 | 2024-07 |
| deepseek coder 6.7B base | DeepSeek | 40.2 | 2024-07 |
| deepseek coder 1.3B base | DeepSeek | 29.2 | 2024-07 |
| deepseek coder 1.3B instruct | DeepSeek | 29.2 | 2024-07 |
| OLMo 1B hf | Allen AI | 29.0 | 2024-07 |