{
 "body": "\nPart of the [MMLU](mmlu.md) family.\n\n## What it measures\n\nThis id is not one of MMLU's 57 dataset subjects. It is the \"computer science\" subcategory the\noriginal authors define in the benchmark repository's `categories.py`, which groups four subjects\nunder one STEM label: College Computer Science (algorithms and computability at undergraduate\nlevel), High School Computer Science (introductory programming concepts), Computer Security\n(cryptography and systems security) and Machine Learning. Some publishers report MMLU results at\nthis coarser grain instead of, or alongside, the four subjects individually, which is what this id\ncaptures.\n\n## Reading the numbers\n\nTreat a score reported under this id as covering all four pooled subjects, not any one alone, and\ncompare it to the individual subject pages (`mmlu_college_computer_science`,\n`mmlu_high_school_computer_science`, `mmlu_computer_security`, `mmlu_machine_learning`) when they\nare available for the same model. Combined, the four subjects' Hugging Face test splits hold 412\nquestions; how a publisher weights or averages them into this single number is not documented and\nis not established here, so a \"computer science\" score from one source and one from another may\nnot be computed the same way. See the [MMLU](mmlu.md) family page for the shared scoring protocol,\nsaturation and contamination notes.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "mmlu_computer_science",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [
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   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Opus 4",
   "model_id": "anthropic/claude-opus-4-20250514",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 88.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Claude Opus 4.6",
   "model_id": "anthropic/claude-opus-4-6",
   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 88.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, anthropic-system-card-mythos"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4.1",
   "model_id": "openai/gpt-4-1",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 88.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, domain-evals preference-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemini 2.5 Pro",
   "model_id": "google/gemini-2-5-pro",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 87.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o",
   "model_id": "openai/gpt-4o",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 87.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "GPT-4o (2024-05-13)",
   "model_id": "openai/gpt-4o-2024-05-13",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 87.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, domain-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
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   "display_name": "GPT-4o (2024-08-06)",
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  },
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   "as_of": "2026-04",
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   "as_of": "2026-04",
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  {
   "as_of": "2026-04",
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   "display_name": "Claude Sonnet 4.5 (latest)",
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   "provider_display": "Anthropic",
   "score": 86.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals"
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   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek R1",
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   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2, domain-evals"
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   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek R1 0528",
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   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 85.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2, domain-evals"
  },
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   "as_of": "2026-04",
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   "display_name": "DeepSeek R1 0528 NVFP4 v2",
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   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 85.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2"
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   "as_of": "2026-04",
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   "model_id": "deepseek/deepseek-r1-0528-qwen3-8b",
   "provider": "deepseek",
   "provider_display": "DeepSeek",
   "score": 85.5,
   "source": "bigcode-leaderboard, provider-reports, open-llm-leaderboard"
  },
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   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "DeepSeek R1 Distill Llama 70B",
   "model_id": "deepseek/deepseek-r1-distill-llama-70b",
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   "provider_display": "DeepSeek",
   "score": 85.5,
   "source": "lmarena.ai, provider-reports, open-llm-leaderboard-v2"
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   "as_of": "2026-04",
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   "as_of": "2026-04",
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   "display_name": "DeepSeek R1 Distill Qwen 32B",
   "model_id": "deepseek/deepseek-r1-distill-qwen-32b",
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   "provider_display": "DeepSeek",
   "score": 85.5,
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  },
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   "as_of": "2026-04",
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   "display_name": "DeepSeek R1 Distill Qwen 7B",
   "model_id": "deepseek/deepseek-r1-distill-qwen-7b",
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   "provider_display": "DeepSeek",
   "score": 85.5,
   "source": "lmarena.ai, provider-reports, open-llm-leaderboard-v2"
  },
  {
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   "display_name": "DeepSeek Reasoner",
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   "provider": "cerebras",
   "provider_display": "Cerebras",
   "score": 84.5,
   "source": "bigcode-leaderboard, provider-reports, preference-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
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   "model_id": "qwen/qwen3-235b-a22b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 84.5,
   "source": "lmarena.ai, provider-reports, preference-evals, llm-stats, domain-evals"
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   "display_name": "Gemma 4 31B",
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   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 82.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
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   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 82.5,
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   "model_id": "unsloth/gemma-4-31b-it-gguf",
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   "score": 82.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
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   "model_id": "nvidia/gemma-4-31b-it-nvfp4",
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   "score": 82.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
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   "display_name": "Mistral Large (latest)",
   "model_id": "mistral/mistral-large-latest",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 82.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, open-llm-leaderboard-v2, llm-stats"
  },
  {
   "as_of": "2026-04",
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   "display_name": "Mistral Large 2.1",
   "model_id": "mistral/mistral-large-2411",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 82.2,
   "source": "lmarena.ai, provider-reports, safety-evals, preference-evals,, domain-evals open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mistral Large 3",
   "model_id": "mistral/mistral-large-2512",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 82.2,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, preference-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemma 4 26B",
   "model_id": "google/gemma-4-26b",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 81.2,
   "source": "lmarena.ai, provider-reports, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama 3.3 70B Instruct NVFP4",
   "model_id": "nvidia/llama-3-3-70b-instruct-nvfp4",
   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 80.5,
   "source": "lmarena.ai, provider-reports, safety-evals, preference-evals, open-llm-leaderboard-v2, llm-stats"
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   "as_of": "2026-04",
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   "display_name": "Llama-3.3-70B-Instruct",
   "model_id": "meta/llama-3-3-70b-instruct",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 80.5,
   "source": "lmarena.ai, provider-reports, safety-evals, preference-evals, open-llm-leaderboard-v2, llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama 3.1 70B",
   "model_id": "meta/llama-3-1-70b",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 79.8,
   "source": "lmarena.ai, provider-reports, safety-evals, domain-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Llama 3.1 70B Instruct",
   "model_id": "meta/llama-3-1-70b-instruct",
   "provider": "meta",
   "provider_display": "Meta",
   "score": 79.8,
   "source": "lmarena.ai, provider-reports, safety-evals, domain-evals, open-llm-leaderboard-v2"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "phi 4",
   "model_id": "microsoft/phi-4",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 78.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2,, domain-evals llm-stats"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Phi 4 mini instruct",
   "model_id": "microsoft/phi-4-mini-instruct",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 78.5,
   "source": "lmarena.ai, provider-reports, preference-evals, llm-stats, open-llm-leaderboard-v2"
  },
  {
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   "display_name": "Phi 4 multimodal instruct",
   "model_id": "microsoft/phi-4-multimodal-instruct",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 78.5,
   "source": "lmarena.ai, provider-reports, preference-evals, open-llm-leaderboard-v2"
  }
 ],
 "page": {
  "category": "knowledge",
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 412,
   "size_note": "Not a single dataset split. Sum of the four pooled subjects' test splits in the Hugging Face parquet mirror of cais/mmlu: College Computer Science (100 test, 11 validation, 5 dev), High School Computer Science (100 test, 9 validation, 5 dev), Computer Security (100 test, 11 validation, 5 dev) and Machine Learning (112 test, 11 validation, 5 dev) = 412 test questions combined. The four subjects are separately downloadable configs; there is no single \"computer science\" config in the dataset itself.\n",
   "splits": "College CS (5/11/100) + High School CS (5/9/100) + Computer Security (5/11/100) + Machine Learning (5/11/112) [dev/validation/test]",
   "url": "https://huggingface.co/datasets/cais/mmlu"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice A"
  },
  "harness": {
   "other": "Not a distinct lm-evaluation-harness, HELM or OpenCompass task; those harnesses run the four pooled subjects separately (mmlu_college_computer_science, mmlu_high_school_computer_science, mmlu_computer_security, mmlu_machine_learning). This id reflects publisher-reported subcategory scores, not a harness task name."
  },
  "id": "mmlu_computer_science",
  "leaderboard_url": "https://github.com/hendrycks/test",
  "lineage": {
   "family": "mmlu",
   "variants": [
    "mmlu_college_computer_science",
    "mmlu_high_school_computer_science",
    "mmlu_computer_security",
    "mmlu_machine_learning"
   ]
  },
  "measures": "This id does not correspond to a single dataset config in the Hugging Face mirror of MMLU. It corresponds to the \"computer science\" subcategory the benchmark's authors define in the repository's categories.py, which pools four subjects: College Computer Science (algorithms and computability at undergraduate level), High School Computer Science (introductory programming concepts), Computer Security (cryptography and systems security) and Machine Learning. Some publishers report MMLU broken down by this kind of subcategory rather than by all 57 individual subjects; this id captures scores reported at that grain.\n",
  "metric": {
   "baseline_note": "25% is the four-option random-guess rate for each underlying question. No dedicated human baseline exists for this subcategory grouping; see the mmlu family page for the benchmark-wide human baselines.\n",
   "direction": "higher_is_better",
   "max_score": 100,
   "name": "accuracy",
   "random_baseline": 25,
   "unit": "%"
  },
  "name": "MMLU: Computer Science (subcategory)",
  "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": "categories.py: MMLU subject-to-subcategory mapping, hendrycks/test repository",
    "url": "https://github.com/hendrycks/test/blob/master/categories.py"
   },
   {
    "accessed": "2026-09-07",
    "title": "cais/mmlu dataset card, Hugging Face",
    "url": "https://huggingface.co/datasets/cais/mmlu"
   }
  ],
  "status": "active",
  "subcategory": "computer science",
  "summary": "The computer science subcategory of MMLU: a rollup of four subjects, used by publishers that report MMLU at a coarser grain than all 57 subjects.",
  "tags": [
   "knowledge",
   "multiple-choice",
   "mmlu-subset",
   "stem",
   "rollup"
  ],
  "task_format": "Four-option multiple-choice questions pooled from four underlying MMLU subjects, graded on the single correct labelled option. How a given publisher averages the four subjects into one number -- an unweighted mean of per-subject accuracy, or a single accuracy over the pooled question set -- is not documented and not established here.\n"
 }
}