{
 "body": "\nPart of the [MMLU](mmlu.md) family.\n\n## What it measures\n\nIntroductory computer science and programming concepts at high-school level. Like the rest of MMLU, each question gives four labelled options and the model is\ngraded on picking the single correct one, typically evaluated 5-shot. The benchmark's own\ncategorisation places this subject in the \"computer science\" subcategory, within the \"STEM\" group of\nMMLU's four broad areas (STEM, humanities, social sciences, and other).\n\n## Reading the numbers\n\nThe Hugging Face mirror of this subject holds 100 test questions (used for scoring), plus 9\nvalidation and 5 dev questions for few-shot prompting. With well under a thousand items, a\nhandful of questions can shift the reported percentage by several points, so treat small\ndifferences between models on this subject alone as noisy rather than meaningful. Read it against\na model's overall MMLU score and against other subjects in the \"STEM\" group rather than in\nisolation, and see\nthe [MMLU](mmlu.md) family page for the shared scoring protocol, saturation and contamination notes\nthat apply here too.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "mmlu_high_school_computer_science",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 70B",
   "model_id": "meta/meta-llama-3-70b",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 92.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 70B Instruct",
   "model_id": "meta/meta-llama-3-70b-instruct",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 92.0,
   "source": "llm-stats, open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 70B Instruct",
   "model_id": "nous-research/meta-llama-3-70b-instruct",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 92.0,
   "source": "llm-stats, open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 34B Chat",
   "model_id": "01-ai/yi-1-5-34b-chat",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 90.0,
   "source": "open-llm-leaderboard-v2, open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 34B Chat 16K",
   "model_id": "01-ai/yi-1-5-34b-chat-16k",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 90.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 9B Chat",
   "model_id": "01-ai/yi-1-5-9b-chat",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 87.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 9B Chat 16K",
   "model_id": "01-ai/yi-1-5-9b-chat-16k",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 87.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mixtral 8x22B Instruct v0.1",
   "model_id": "mistral/mixtral-8x22b-instruct-v0-1",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 86.0,
   "source": "open-llm-leaderboard-v2, llm-stats, open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Nous Hermes 2 Yi 34B",
   "model_id": "nous-research/nous-hermes-2-yi-34b",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 84.0,
   "source": "open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 34B 32K",
   "model_id": "01-ai/yi-1-5-34b-32k",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 84.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 34B",
   "model_id": "01-ai/yi-1-5-34b",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 83.0,
   "source": "open-llm-leaderboard-v2, open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 9B",
   "model_id": "01-ai/yi-1-5-9b",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 83.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 9B",
   "model_id": "01-ai/yi-9b",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 83.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 9B 32K",
   "model_id": "01-ai/yi-1-5-9b-32k",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 82.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 34B 200K",
   "model_id": "01-ai/yi-34b-200k",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 82.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 34B Chat",
   "model_id": "01-ai/yi-34b-chat",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 81.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mixtral 8x7B Instruct v0.1",
   "model_id": "mistral/mixtral-8x7b-instruct-v0-1",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 78.0,
   "source": "open-llm-leaderboard-v2, open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 8B Instruct",
   "model_id": "meta/meta-llama-3-8b-instruct",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 75.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 8B Instruct",
   "model_id": "nous-research/meta-llama-3-8b-instruct",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 75.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Nous Hermes 2 Mixtral 8x7B DPO",
   "model_id": "nous-research/nous-hermes-2-mixtral-8x7b-dpo",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 75.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 6B Chat",
   "model_id": "01-ai/yi-1-5-6b-chat",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 75.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Phi 3 mini 4K instruct",
   "model_id": "microsoft/phi-3-mini-4k-instruct",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 73.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mixtral 8x7B v0.1",
   "model_id": "mistral/mixtral-8x7b-v0-1",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 72.0,
   "source": "open-llm-leaderboard-v2, open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Nous Hermes 2 SOLAR 10.7B",
   "model_id": "nous-research/nous-hermes-2-solar-10-7b",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 71.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Hermes 2 Theta Llama 3 8B",
   "model_id": "nous-research/hermes-2-theta-llama-3-8b",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 70.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 1.5 6B",
   "model_id": "01-ai/yi-1-5-6b",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 69.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 8B",
   "model_id": "meta/meta-llama-3-8b",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 68.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Meta Llama 3 8B",
   "model_id": "nous-research/meta-llama-3-8b",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 68.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "gemma 7B it",
   "model_id": "google/gemma-7b-it",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 67.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Phi 3 mini 128K instruct",
   "model_id": "microsoft/phi-3-mini-128k-instruct",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 67.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Hermes 2 Pro Llama 3 8B",
   "model_id": "nous-research/hermes-2-pro-llama-3-8b",
   "provider": "nous-research",
   "provider_display": "Nous Research",
   "score": 66.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Mistral 7B Instruct v0.2",
   "model_id": "mistral/mistral-7b-instruct-v0-2",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 65.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Mistral 7B v0.3",
   "model_id": "mistral/mistral-7b-v0-3",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 65.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "mistral 7B v0.3 bnb 4bit",
   "model_id": "unsloth/mistral-7b-v0-3-bnb-4bit",
   "provider": "unsloth",
   "provider_display": "Unsloth",
   "score": 65.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 6B",
   "model_id": "01-ai/yi-6b",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 65.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Yi 6B Chat",
   "model_id": "01-ai/yi-6b-chat",
   "provider": "01-ai",
   "provider_display": "01.AI",
   "score": 65.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "phi 2",
   "model_id": "microsoft/phi-2",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 64.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "falcon 40B",
   "model_id": "tii/falcon-40b",
   "provider": "tii",
   "provider_display": "TII",
   "score": 62.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "deepseek coder 6.7B instruct",
   "model_id": "deepseek/deepseek-coder-6-7b-instruct",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 56.0,
   "source": "open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2 1.5B Instruct",
   "model_id": "qwen/qwen2-1-5b-instruct",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 56.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "deepseek coder 6.7B base",
   "model_id": "deepseek/deepseek-coder-6-7b-base",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 52.0,
   "source": "open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "deepseek llm 7B base",
   "model_id": "deepseek/deepseek-llm-7b-base",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 47.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "deepseek llm 7B chat",
   "model_id": "deepseek/deepseek-llm-7b-chat",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 47.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "Qwen2 0.5B Instruct",
   "model_id": "qwen/qwen2-0-5b-instruct",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 47.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "chatglm2 6B",
   "model_id": "zhipu/chatglm2-6b",
   "provider": "zhipu",
   "provider_display": "Zhipu AI",
   "score": 42.0,
   "source": "open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "gemma 2B",
   "model_id": "google/gemma-2b",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 42.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "gemma 2B it",
   "model_id": "google/gemma-2b-it",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 37.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "deepseek coder 1.3B base",
   "model_id": "deepseek/deepseek-coder-1-3b-base",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 33.0,
   "source": "open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "deepseek coder 1.3B instruct",
   "model_id": "deepseek/deepseek-coder-1-3b-instruct",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 33.0,
   "source": "open-llm-leaderboard-v1"
  },
  {
   "as_of": "2024-07",
   "attribution": "unverified-legacy",
   "display_name": "OLMo 1B hf",
   "model_id": "allen-ai/olmo-1b-hf",
   "provider": "allen-ai",
   "provider_display": "Allen AI",
   "score": 31.0,
   "source": "open-llm-leaderboard-v1, open-llm-leaderboard-v2"
  }
 ],
 "page": {
  "category": "knowledge",
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 100,
   "size_note": "100 test questions (used for scoring), plus 9 validation and 5 dev (few-shot prompt) questions, per the Hugging Face parquet mirror of cais/mmlu, config 'high_school_computer_science'.",
   "splits": "dev (5), validation (9), test (100)",
   "url": "https://huggingface.co/datasets/cais/mmlu"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice A"
  },
  "harness": {
   "helm": "mmlu:subject=high_school_computer_science",
   "lm_eval": "mmlu_high_school_computer_science",
   "other": "hendrycksTest-high_school_computer_science in the pre-2024 Open LLM Leaderboard v1 harness fork"
  },
  "id": "mmlu_high_school_computer_science",
  "leaderboard_url": "https://github.com/hendrycks/test",
  "lineage": {
   "family": "mmlu"
  },
  "measures": "Introductory computer science and programming concepts at high-school level. Questions are four-option multiple-choice, drawn from the MMLU test set's \"computer science\" subcategory within the benchmark's \"STEM\" top-level group, and are graded on the single correct labelled option.",
  "metric": {
   "baseline_note": "25% is the four-option random-guess rate. No subject-specific human baseline is given by the paper for this subject; see the mmlu family page for the benchmark-wide human baselines.",
   "direction": "higher_is_better",
   "max_score": 100,
   "name": "accuracy",
   "random_baseline": 25,
   "unit": "%"
  },
  "name": "MMLU: High School Computer Science",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2009.03300",
   "title": "Measuring Massive Multitask Language Understanding",
   "url": "https://arxiv.org/abs/2009.03300",
   "year": 2021
  },
  "publisher": {
   "authors": [
    "Dan Hendrycks",
    "Collin Burns",
    "Steven Basart",
    "Andy Zou",
    "Mantas Mazeika",
    "Dawn Song",
    "Jacob Steinhardt"
   ],
   "org": "UC Berkeley (original); Center for AI Safety (current host)",
   "url": "https://github.com/hendrycks/test"
  },
  "released": "2020-09",
  "repo_url": "https://github.com/hendrycks/test",
  "sources": [
   {
    "accessed": "2026-09-07",
    "title": "Measuring Massive Multitask Language Understanding (Hendrycks et al., arXiv:2009.03300)",
    "url": "https://arxiv.org/abs/2009.03300"
   },
   {
    "accessed": "2026-09-07",
    "title": "hendrycks/test GitHub repository (MMLU reference implementation)",
    "url": "https://github.com/hendrycks/test"
   },
   {
    "accessed": "2026-09-07",
    "title": "cais/mmlu dataset card, Hugging Face",
    "url": "https://huggingface.co/datasets/cais/mmlu"
   }
  ],
  "status": "active",
  "subcategory": "computer science",
  "summary": "MMLU subject subset: Introductory computer science and programming concepts at high-school level.",
  "tags": [
   "knowledge",
   "multiple-choice",
   "mmlu-subset",
   "stem"
  ],
  "task_format": "Four-option multiple-choice questions, graded on the single correct labelled option; commonly evaluated 5-shot, consistent with the rest of MMLU."
 }
}