{
 "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 \"chemistry\" subcategory the original\nauthors define in the benchmark repository's `categories.py`, which groups the College Chemistry\nand High School Chemistry subjects under one STEM label. College Chemistry covers general and\norganic chemistry at undergraduate level; High School Chemistry covers standard secondary-school\nchemistry. Some publishers report MMLU results at this coarser grain instead of, or alongside, the\ntwo subjects individually, which is what this id captures.\n\n## Reading the numbers\n\nTreat a score reported under this id as covering both College Chemistry and High School Chemistry,\nnot either one alone, and compare it to the two individual subject pages\n(`mmlu_college_chemistry`, `mmlu_high_school_chemistry`) when both are available for the same\nmodel. Combined, the two subjects' Hugging Face test splits hold 303 questions; how a publisher\nweights or averages the two subjects into this single number is not documented and is not\nestablished here, so a \"chemistry\" score from one source and one from another may not be computed\nthe same way. See the [MMLU](mmlu.md) family page for the shared scoring protocol, saturation and\ncontamination notes.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "mmlu_chemistry",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [
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   "attribution": "unverified-legacy",
   "display_name": "Claude Opus 4",
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   "provider": "anthropic",
   "provider_display": "Anthropic",
   "score": 82.5,
   "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": 82.5,
   "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": 81.8,
   "source": "lmarena.ai, provider-reports, multimodal-evals, safety-evals, domain-evals preference-evals, llm-stats, intlpull"
  },
  {
   "as_of": "2026-04",
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   "display_name": "Gemini 2.5 Pro",
   "model_id": "google/gemini-2-5-pro",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 81.5,
   "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",
   "model_id": "openai/gpt-4o",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 80.2,
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   "display_name": "GPT-4o (2024-05-13)",
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   "provider_display": "OpenAI",
   "score": 80.2,
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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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   "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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   "display_name": "DeepSeek R1 0528",
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   "score": 78.5,
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   "score": 78.5,
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   "score": 78.5,
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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",
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   "as_of": "2026-04",
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   "provider_display": "DeepSeek",
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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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   "source": "lmarena.ai, provider-reports, open-llm-leaderboard-v2"
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   "display_name": "DeepSeek Reasoner",
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   "score": 77.2,
   "source": "bigcode-leaderboard, provider-reports, preference-evals"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
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   "score": 77.2,
   "source": "lmarena.ai, provider-reports, preference-evals, llm-stats, domain-evals"
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   "display_name": "Mistral Large (latest)",
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   "display_name": "Gemma 4 31B",
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  },
  {
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   "model_id": "nvidia/gemma-4-31b-it-nvfp4",
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   "provider_display": "NVIDIA",
   "score": 74.5,
   "source": "lmarena.ai, provider-reports, multimodal-evals, open-llm-leaderboard-v2"
  },
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   "display_name": "Gemma 4 26B",
   "model_id": "google/gemma-4-26b",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 73.2,
   "source": "lmarena.ai, provider-reports, open-llm-leaderboard-v2"
  },
  {
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   "display_name": "Llama 3.3 70B Instruct NVFP4",
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   "score": 71.2,
   "source": "lmarena.ai, provider-reports, safety-evals, domain-evals, open-llm-leaderboard-v2"
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   "provider": "microsoft",
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   "score": 70.5,
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 ],
 "page": {
  "category": "knowledge",
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 303,
   "size_note": "Not a single dataset split. Sum of the two pooled subjects' test splits in the Hugging Face parquet mirror of cais/mmlu: College Chemistry (100 test, 8 validation, 5 dev) plus High School Chemistry (203 test, 22 validation, 5 dev) = 303 test questions combined. The two subjects are separately downloadable configs; there is no single \"chemistry\" config in the dataset itself.\n",
   "splits": "College Chemistry (dev 5, validation 8, test 100) + High School Chemistry (dev 5, validation 22, test 203)",
   "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 College Chemistry and High School Chemistry as separate tasks (mmlu_college_chemistry, mmlu_high_school_chemistry). This id reflects publisher-reported subcategory scores, not a harness task name."
  },
  "id": "mmlu_chemistry",
  "leaderboard_url": "https://github.com/hendrycks/test",
  "lineage": {
   "family": "mmlu",
   "variants": [
    "mmlu_college_chemistry",
    "mmlu_high_school_chemistry"
   ]
  },
  "measures": "This id does not correspond to a single dataset config in the Hugging Face mirror of MMLU. It corresponds to the \"chemistry\" subcategory the benchmark's authors define in the repository's categories.py, which pools the College Chemistry and High School Chemistry subjects -- general and organic chemistry at an undergraduate level, and standard high-school chemistry respectively. 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: Chemistry (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": "chemistry",
  "summary": "The chemistry subcategory of MMLU: a rollup of the College Chemistry and High School Chemistry 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 two underlying MMLU subjects (College Chemistry, High School Chemistry), graded on the single correct labelled option. How a given publisher averages the two 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"
 }
}