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Benchmarks
63
Public problems
91,222
Benchmarks with full text
40

What you can work on

Public problems by field, counting a benchmark under each of its fields: Agents 470; Code 5,646; Instruction following 541; Knowledge 18,341; Long context 200; Mathematics 13,803; Multilingual 2,750; Multimodal 400; Reasoning 67,696; Safety 817; Science 4,983; Search 4,507. Compare formats, answer availability and reported scores.

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A training input from ARC-AGI-2. Compare it with the output and other examples in the full problem.

Explore a47bf94d
Input · 22 × 22
Input — numeric array
[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,3,3,3,8,8,8,8,8,8,0,0,8,8,0,0,0,0,0,0,0],[0,0,3,3,3,0,0,0,0,0,8,0,0,8,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,8,8,9,9,9,9,8,0,0,0,0,0,0,0,0],[0,0,0,4,0,0,0,8,0,9,9,9,9,0,0,4,0,4,0,0,0,0],[0,0,4,0,4,8,5,8,5,9,9,9,9,8,8,0,4,0,0,0,0,0],[0,0,0,4,0,0,0,8,0,9,9,9,9,0,0,4,0,4,0,0,0,0],[0,0,0,0,0,0,0,8,0,9,9,9,9,0,0,0,0,0,0,0,0,0],[0,0,2,2,2,0,0,8,0,0,8,0,0,0,0,0,0,0,0,0,0,0],[0,0,2,2,2,8,8,8,0,0,8,8,8,8,8,0,0,0,0,0,0,0],[0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,6,0,0,0,0],[0,0,0,0,0,8,8,8,8,8,8,8,8,8,8,0,6,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,6,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]

Choose a benchmark

  • Safety / Agents

    AgentHarm

    agentharm

    AgentHarm measures how far a tool-using agent will go in carrying out a harmful request. It pairs 110 harmful behaviours with 110 matched benign ones, so over-refusal is measured alongside compliance.

    Metric
    harm score and refusal rate, with the benign pairs measuring over-refusal
    Size
    110 harmful and 110 benign behaviours in the source; none imported.
    License
    other (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:2410.09024

  • Mathematics

    AIME 2024

    aime-2024

    The 30 problems of the 2024 American Invitational Mathematics Examination (AIME I and II). Every answer is an integer from 0 to 999, with no options and no partial credit. It is a frequent reference point for reasoning m…

    Full text30 tasks
    Size
    All 30 problems imported from the default / train selection, with a source-row limit of 200.
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    2
  • Mathematics

    AIME 2025 I

    aime-2025

    The 15 problems of AIME I 2025. As one of the newest competition sets available, it leaves the least room for training-data contamination, and reading it beside the 2024 set makes that effect visible.

    Full text15 tasks
    Size
    All 15 problems imported from the AIME2025-I / test selection, with a source-row limit of 200.
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    2
  • Mathematics

    AMC (AIMO validation set)

    amc

    83 AMC 12 problems assembled by Project Numina as a validation set for the AIMO competition. They sit a step below AIME in difficulty, which makes them a useful lower rung on the same scale.

    Full text83 tasks
    Size
    All 83 problems imported from the default / train selection, with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    2
  • Reasoning

    ARC-AGI-2

    arc-agi-2

    ARC-AGI-2 evaluates the ability to infer unfamiliar rules from a few examples and apply them to new grids. Training and evaluation are separate; this import covers the 120 public evaluation tasks.

    Full text120 tasks
    Size
    120 public evaluation tasks in this catalog selection; training is a separate split.
    License
    Apache-2.0 (declared in the GitHub repository)
    Reference links
    2

    Repository: Source repository

  • Reasoning / Agents

    ARC-AGI-3

    arc-agi-3

    ARC-AGI-3 is an interactive benchmark for exploring unfamiliar game environments, learning their rules, and planning actions. The official toolkit provides an environment interface, with game versions and trial records t…

    Reference links
    1
  • Reasoning / Science

    ARC-Challenge

    arc-challenge

    ARC-Challenge is the subset of grade-school science questions that both a retrieval baseline and a word co-occurrence baseline answered incorrectly. It tests reasoning with knowledge rather than recall alone.

    Full text1,172 tasks
    Size
    1,172 problems imported from the ARC-Challenge / test selection (the full test split).
    License
    cc-by-sa-4.0 (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:1803.05457

  • Reasoning / Science

    ARC-Easy

    arc-easy

    ARC-Easy is the subset of grade-school science questions that a retrieval baseline or a word co-occurrence baseline answered correctly. It is the easier counterpart of ARC-Challenge and serves as the baseline measure of …

    Full text2,376 tasks
    Size
    2,376 problems imported from the ARC-Easy / test selection (the full test split).
    License
    cc-by-sa-4.0 (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:1803.05457

  • Instruction following

    Arena-Hard-Auto

    arena-hard

    Arena-Hard-Auto selects 500 prompts from 200,000 real LMArena user conversations. Answers are scored automatically with a strong model as judge, which is reported to correlate closely with human preference results.

    Size
    500 prompts in the source; none imported.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Agents / Search

    AssistantBench

    assistantbench

    AssistantBench collects realistic, time-consuming tasks that can only be answered by working through the live web, written so that each has one determinable answer. It measures whether a web agent is actually useful.

    Full text181 tasks
    Metric
    accuracy and answer rate over the held-out test set, scored by the official submission service
    Size
    All 181 tasks imported from the default / test selection, with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2407.15711

  • Reasoning

    BIG-Bench Extra Hard (BBEH)

    bbeh

    BIG-Bench Extra Hard evaluates language-model reasoning through challenging task categories. It offers full and mini distributions; this import contains the full distribution: 4,520 questions across all 23 task categorie…

    Full text4,513 tasks
    Size
    Catalog selection: the full distribution, 4,513 unique questions across all 23 task directories; 7 source rows repeat an earlier prompt and are recorded as variants. The mini distribution repeats a subset and is not imported separately.
    License
    Apache-2.0 (declared in the GitHub repository)
    Reference links
    2

    Repository: Source repository

  • Reasoning

    BIG-Bench Hard

    bbh

    BIG-Bench Hard is the set of 23 BIG-Bench tasks on which models of the time scored below the average human rater. It is best known for showing how much chain-of-thought prompting changes the result.

    Full text6,507 tasks
    Size
    6,507 unique problems imported from the 27 task files of the official repository (6,511 rows; 4 rows repeat an earlier prompt and are recorded as variants).
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    2

    Repository: Official repository

  • Agents / Tools

    Berkeley Function Calling Leaderboard

    bfcl

    The Berkeley Function Calling Leaderboard evaluates tool use. It works up from a single call to parallel calls, multi-turn dialogue, and recognising when no function should be called at all.

    Size
    Several thousand evaluation cases across the categories; none imported.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    3
  • Code

    BigCodeBench

    bigcodebench

    BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.

    Full text1,139 tasks
    Metric
    Pass@1 (the official runner also reports Pass@5 and Pass@10)
    Size
    1,139 unique problems imported from the default / v0.1.0_hf selection of 1,140 rows; 1 row repeats an earlier prompt and is recorded as a variant. Later version splits repeat the same task IDs and are not imported separately.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2406.15877

  • Reasoning

    BoolQ

    boolq

    BoolQ pairs naturally occurring yes/no questions from search queries with a passage that decides them. The format is simple, but the inference it needs is consistently heavier than it looks.

    Full text3,270 tasks
    Metric
    accuracy over the 3,270 validation questions
    Size
    3,270 problems imported from the default / validation selection (the full validation split).
    License
    cc-by-sa-3.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Agents / Search

    BrowseComp

    browsecomp

    BrowseComp evaluates web browsing and the ability to identify answers from multiple clues. This catalog supports introductions and discussion of search methods without republishing decrypted questions or answers.

    License
    MIT (declared in the GitHub repository)
    Reference links
    3

    Repository: Source repository

  • Agents / Search

    BrowseComp-Plus

    browsecomp-plus

    BrowseComp-Plus supports evaluation of retrieval methods using BrowseComp tasks and a search corpus. This catalog links to the official distribution without republishing plaintext questions or corpus content.

    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:2508.06600

  • Code

    CodeContests

    code-contests

    CodeContests is the competitive-programming dataset built for AlphaCode, drawing problems from Codeforces and similar sites. Each problem ships with both correct and incorrect submissions and a large generated test suite…

    Size
    Roughly 13,500 problems in the source; none imported.
    License
    cc-by-4.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Science

    CritPt

    critpt

    CritPt evaluates scientific understanding and multi-step reasoning and computation on research-level physics problems. Its public dataset contains 70 challenges with problem descriptions and code templates.

    Full text70 tasks
    Metric
    accuracy (README: average accuracy over 5 runs × 70 challenges)
    Size
    70 public challenges; catalog selection expects 70 task records from train, with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2509.26574

  • Code / Reasoning

    CRUXEval

    cruxeval

    CRUXEval takes 800 short Python functions and asks either for the output given an input (CRUXEval-O) or for an input that produces a given output (CRUXEval-I). It measures the ability to execute code mentally rather than…

    Full text800 tasks
    Metric
    pass@1 on output prediction and on input prediction, reported separately
    Size
    800 problems imported from the default / test selection (the full split).
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2401.03065

  • Reasoning

    DROP

    drop

    DROP is a reading-comprehension benchmark whose questions require discrete operations over the paragraph — addition, subtraction, comparison, counting, sorting. It is built so that extracting a span cannot answer them.

    Full text9,535 tasks
    Metric
    exact match and macro-averaged F1 over the answer spans
    Size
    9,535 problems imported from the default / validation selection (the full validation split).
    License
    cc-by-sa-4.0 (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:1903.00161

  • Code / Science

    DS-1000

    ds-1000

    DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve …

    Full text1,000 tasks
    Metric
    accuracy (the source paper reports the best public system at 43.3%)
    Size
    1,000 problems imported from the default / test selection (the full split).
    License
    cc-by-sa-4.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2211.11501

  • Mathematics

    FrontierMath

    frontiermath

    FrontierMath evaluates advanced mathematical reasoning using problems written by experts. This catalog links to the official introduction and public examples while excluding private evaluation problems.

    Reference links
    1
  • Science

    FrontierScience

    frontierscience

    FrontierScience evaluates the ability to solve expert-level scientific tasks. Its public data separates olympiad and research problems so that competition and research tasks can be examined independently.

    Full text100 tasks
    Size
    Catalog selection: olympiad / test, expected 100 task records, source-row limit 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    2
  • Agents / Multimodal

    GAIA

    gaia

    GAIA evaluates general AI assistants on tasks involving information gathering and tool use. Some tasks include attachments; this catalog provides an introduction in view of access and resharing restrictions.

    Reference links
    3

    Paper: arXiv:2311.12983

  • Science

    GPQA

    gpqa

    GPQA is a set of graduate-level questions in biology, physics and chemistry written so that non-experts with web access still fail them. The Diamond subset is the most-used slice.

    Size
    Not imported here; see the official distribution for the current size.
    License
    cc-by-4.0 (declared on the source)
    Reference links
    3

    Paper: arXiv:2311.12022

  • Mathematics

    GSM8K

    gsm8k

    GSM8K is a set of grade-school word problems that each need several arithmetic steps. It remains a standard reference point for multi-step reasoning.

    Full text1,319 tasks
    Size
    1,319 problems imported from the main / test selection (the full test split).
    License
    mit (declared on the source)
    Reference links
    3

    Paper: arXiv:2110.14168

  • Safety

    HarmBench

    harmbench

    HarmBench is a standardized framework for comparing automated red-teaming attacks and refusal robustness on the same footing. It pits attack methods against defences and measures the success rate against harmful behaviou…

    Metric
    attack success rate (ASR) per attack and defence pair
    Size
    510 behaviours in the full benchmark, of which the linked mirror carries 400 textual ones; none imported.
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:2402.04249

  • Reasoning

    HellaSwag

    hellaswag

    HellaSwag asks which of four continuations naturally follows a sentence or a video caption. The wrong options are produced by adversarial filtering, so items that are nearly trivial for people remain hard for models.

    Full text10,042 tasks
    Metric
    accuracy over the 10,042 validation items
    Size
    10,042 problems imported from the default / validation selection (the full validation split).
    License
    mit (declared in the dataset card’s Licensing Information section, which points at the project’s MIT LICENSE)
    Reference links
    4

    Paper: arXiv:1905.07830

  • Code

    Humanity's Last Code Exam

    hlce

    Humanity's Last Code Exam evaluates problem solving and code generation using ICPC World Finals and IOI problems. The project describes 235 problems; this import selects its public ICPC dataset of 146 problems.

    Full text146 tasks
    Size
    235 problems described in the catalog introduction; this selection expects 146 ICPC problem records from ICPC-World-Finals / examples, with a source-row limit of 200.
    Reference links
    3

    Repository: Source repository

  • Reasoning / Multimodal

    Humanity's Last Exam

    hle

    Humanity's Last Exam evaluates AI knowledge and reasoning across academic disciplines. It includes text and image questions; this catalog provides an introduction and official links in accordance with the publisher's req…

    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    3
  • Code

    HumanEval

    humaneval

    HumanEval asks for a Python function body from a docstring and signature, checked by hidden unit tests. It is the original functional-correctness benchmark for code generation.

    Full text164 tasks
    Metric
    pass@1 (unbiased estimator over sampled completions)
    Size
    164 problems imported from the openai_humaneval / test selection, with a source-row limit of 200.
    License
    mit (declared on the source)
    Reference links
    3

    Paper: arXiv:2107.03374

  • Code

    HumanEval+

    humaneval-plus

    HumanEval+ keeps the 164 original HumanEval problems and multiplies their tests by roughly 80×. It exists because the original suite was loose enough to pass implementations that were actually wrong.

    Full text164 tasks
    Metric
    pass@1 on the expanded tests
    Size
    All 164 problems imported from the default / test selection, with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Reasoning / Instruction following

    IFEval

    ifeval

    IFEval measures whether a model follows verifiable instructions -- word counts, formats, forbidden words -- rather than whether an answer is good. Every constraint is checked programmatically.

    Full text541 tasks
    Size
    541 problems imported from the default / train selection (the full split; the source names its only split train).
    License
    apache-2.0 (declared on the source)
    Reference links
    3

    Paper: arXiv:2311.07911

  • Safety

    JailbreakBench

    jailbreakbench

    JailbreakBench is an open robustness benchmark for jailbreaking attacks, defining 100 harmful behaviours alongside 100 matched benign ones. This catalogue deliberately introduces and links it rather than reproducing any …

    Metric
    attack success rate, with the benign pairs used to measure over-refusal
    Size
    100 harmful and 100 benign behaviours in the source; none imported.
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2404.01318

  • Code

    LiveCodeBench

    livecodebench

    LiveCodeBench continuously collects new competitive-programming problems to evaluate coding capabilities. Its initial release_v1 contains 400 problems, with source platform, contest date, and dataset version tracked expl…

    Full text100 tasks
    Size
    400 problems in initial release_v1 described in the catalog introduction; this release_v1 / test selection expects 100 task records, limited to the first 100 source rows.
    License
    cc (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2403.07974

  • Reasoning / Long context

    LongBench v2

    longbench-v2

    LongBench v2 evaluates deep understanding and reasoning over long contexts through multiple-choice questions. Its official description lists 503 questions spanning tasks such as single-document and multi-document QA and …

    Full text200 tasks
    Metric
    accuracy over multiple-choice questions (README: 503 multiple-choice questions)
    Size
    503 questions described in the catalog introduction; this train selection expects 200 task records with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:2412.15204

  • Mathematics

    MATH

    math

    MATH organises 12,500 high-school competition problems into seven subjects and five difficulty levels, each with a full worked solution. It underlies much of the later mathematics evaluation work; this catalogue imports …

    Full text5,000 tasks
    Metric
    accuracy on the final boxed answer
    Size
    5,000 problems imported from the test split across all seven subjects.
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Mathematics / Multimodal

    MathVista

    mathvista

    MathVista gathers 6,141 mathematical problems that come with figures, charts or geometric diagrams, of which 1,000 form the public testmini split. It measures whether visual information can be carried into mathematical r…

    Metric
    accuracy over testmini and the full test set
    Size
    6,141 problems in the source, 1,000 in testmini; none imported.
    License
    cc-by-sa-4.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2310.02255

  • Code

    MBPP

    mbpp

    Mostly Basic Python Problems asks for a short Python function from a one-sentence description, checked by assertions. The sanitized split is the hand-verified subset.

    Full text1,401 tasks
    Metric
    pass@1 on the assertion tests
    Size
    1,401 problems imported: the full config across its four splits (974 records) and the hand-verified sanitized config across its four splits (427 records). The two configs overlap by upstream task_id and are kept as separate variant pages.
    License
    cc-by-4.0 (declared on the source)
    Reference links
    3

    Paper: arXiv:2108.07732

  • Code

    MBPP+

    mbpp-plus

    MBPP+ takes the 378 problems of the sanitized MBPP set and multiplies their tests by roughly 35 times. It removes the same test-weakness inflation that HumanEval+ addresses.

    Full text378 tasks
    Metric
    pass@1 on the expanded tests
    Size
    378 problems imported from the default / test selection (the full split).
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Mathematics / Multilingual

    MGSM

    mgsm

    MGSM is a human translation of 250 GSM8K problems into eleven languages, measuring whether reasoning survives the change of language. The gap against English is the multilingual signal.

    Full text2,750 tasks
    Size
    2,750 problems imported from the original TSVs: the same 250 grade-school problems in each of the eleven languages, kept as per-language pages.
    License
    cc-by-4.0 (declared in the original repository mgsm/LICENSE)
    Reference links
    4

    Repository: Official repository

  • Reasoning / Knowledge

    MMLU

    mmlu

    MMLU asks four-way multiple-choice questions across 57 subjects, from elementary material to professional-level examinations. Since 2020 it has been the most widely cited single reference point for general capability.

    Full text14,015 tasks
    Metric
    accuracy (fraction of questions answered correctly, usually reported as a macro average over the 57 subjects)
    Size
    14,015 unique problems imported from the all / test selection of 14,042 rows; 27 source rows repeat an earlier question exactly and are recorded as variants. The 57 subject configurations overlap this selection and are not imported separately.
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Reasoning

    MMLU-Pro

    mmlu-pro

    MMLU-Pro rebuilds MMLU with ten answer options instead of four and removes items that no longer separate models. Questions span fourteen academic and professional subjects.

    Full text12,032 tasks
    Size
    12,032 problems imported from the default / test selection (the full test split).
    License
    mit (declared on the source)
    Reference links
    3

    Paper: arXiv:2406.01574

  • Multimodal

    MMMU

    mmmu

    MMMU collects 11,500 college-level questions across six disciplines and thirty subjects, each carrying figures, diagrams or images that have to be read as well as reasoned over. MMMU-Pro is its tightened revision.

    Size
    11,500 questions in the source; none imported.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2311.16502

  • Multimodal

    MMMU-Pro

    mmmu-pro

    MMMU-Pro evaluates multimodal understanding across academic disciplines using images and text. The official card lists 1,730 questions per setting, separating four-option and ten-option standard settings from the vision …

    Full text200 tasks
    Metric
    accuracy over multiple-choice questions (dataset card)
    Size
    1,730 questions per setting described in the catalog introduction; this standard (10 options) / test selection expects 200 task records with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2409.02813

  • Instruction following

    MT-Bench

    mt-bench

    MT-Bench is a set of 80 two-turn questions across eight categories, published with the study that established LLM-as-a-judge evaluation. It looks at multi-turn consistency and instruction following.

    Metric
    average judge score over the eight categories, reported per turn
    Size
    80 questions in the source; none imported.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official evaluation code

  • Reasoning

    MuSR

    musr

    MuSR poses multistep soft reasoning problems over algorithmically generated narratives. It has three domains — murder mysteries, object placements and team allocation — and this catalogue imports all three domains, 756 p…

    Full text756 tasks
    Metric
    accuracy over the 250 problems in each domain
    Size
    756 problems imported across the three domain files (murder mysteries, object placements and team allocation).
    License
    cc-by-4.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Paper: arXiv:2310.16049

  • Mathematics / Science / Multimodal

    OlympiadBench

    olympiadbench

    OlympiadBench evaluates scientific reasoning on Olympiad-level mathematics and physics problems. Its official description lists 8,476 English and Chinese problems with separate text-only and multimodal settings.

    Full text200 tasks
    Size
    8,476 English and Chinese problems described across settings in the catalog introduction; this OE_TO_maths_en_COMP / train selection expects 200 task records with a source-row limit of 200.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Mathematics

    Omni-MATH

    omni-math

    Omni-MATH evaluates mathematical reasoning on Olympiad-level problems. Its official dataset contains 4,428 problems accompanied by domain and difficulty information.

    Full text4,406 tasks
    Size
    4,406 unique problems imported from the test selection of 4,428 rows; 22 rows repeat an earlier problem and are recorded as variants.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:2410.07985

  • Agents / Multimodal

    OSWorld

    osworld

    OSWorld runs an agent inside a real desktop operating system. It scores whether the requested task was actually accomplished, across applications and file-system state.

    Size
    Not imported here; see the official distribution for the current size.
    License
    Apache-2.0 (declared on the source)
    Reference links
    2

    Repository: Official repository

  • Reasoning / Long context

    RULER

    ruler

    RULER is a synthetic benchmark for the context length a model can actually use. It generates retrieval, multi-hop tracing, aggregation and question-answering tasks at any length, exposing the gap against the advertised w…

    Metric
    accuracy per task at each context length, and the effective length at which performance falls below a threshold
    Size
    Generated per run; nothing fixed to import.
    License
    apache-2.0 (declared by the LICENSE file in the official NVIDIA/RULER repository)
    Reference links
    2

    Repository: Official repository

  • Science / Code

    SciCode

    scicode

    SciCode evaluates the ability to solve scientific research problems through code. Problems are decomposed into subproblems; this dev import preserves the relationships between 15 parent problems and 50 subproblems.

    Full text65 tasks
    Size
    Catalog dev selection: 15 parent problems and 50 subproblems, totaling 65 task records.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    3

    Paper: arXiv:2407.13168

  • Knowledge / Search

    SimpleQA

    simpleqa

    SimpleQA collects short fact-seeking questions that have exactly one indisputable answer, measuring both how often a model is right and how often it declines. The format is deliberately narrow so that grading is not itse…

    Full text4,326 tasks
    Metric
    correct, incorrect and not-attempted rates, and the F-score that combines them
    Size
    4,326 problems imported from the default / test selection (the full split).
    License
    mit (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official evaluation code

  • Code / Agents

    SWE-bench Lite

    swe-bench-lite

    SWE-bench Lite is a 300-instance subset of SWE-bench in which the task is to write a patch that resolves a real GitHub issue. It is cheap enough to evaluate repeatedly during development.

    Size
    300 instances in the source; none imported.
    License
    Not declared on the source.
    Reference links
    4

    Paper: arXiv:2310.06770

  • Code / Agents

    SWE-Bench Pro

    swe-bench-pro

    SWE-Bench Pro evaluates agents on long-horizon software engineering tasks in real repositories. The public card describes 731 tasks containing issue descriptions, repository identifiers, and base commits.

    Full text200 tasks
    Size
    731 public tasks described in the catalog introduction; this test selection expects 200 task records with a source-row limit of 200.
    Reference links
    2
  • Code / Agents

    SWE-bench Verified

    swe-bench-verified

    SWE-bench Verified is the 500-issue subset of SWE-bench that human annotators confirmed to be solvable and correctly specified. Each task is a real GitHub issue in a Python repository.

    Size
    Not imported here; see the official distribution for the current size.
    Reference links
    2
  • Agents

    tau-bench

    tau-bench

    tau-bench places an agent in a customer-service conversation with a simulated user and a domain API. It scores the resulting database state against the rules the agent was given, not the transcript.

    Size
    Not imported here; see the official distribution for the current size.
    License
    MIT (declared on the source)
    Reference links
    2

    Repository: Official repository

  • Agents / Code

    Terminal-Bench 2.1

    terminal-bench-2-1

    Terminal-Bench 2.1 evaluates agents performing tasks in terminal environments. Each task supplies instructions and environment configuration, and version 2.1 is tracked separately from 2.0.

    Full text89 tasks
    Size
    Catalog selection for version 2.1: tasks split, expected 89 task records, source-row limit 200.
    License
    Apache-2.0 (declared in the GitHub repository)
    Reference links
    2

    Repository: Source repository

  • Reasoning / Safety

    TruthfulQA

    truthfulqa

    TruthfulQA measures whether a model repeats falsehoods that people commonly believe. Its 817 questions span 38 categories including health, law, finance and politics, and each one is written to invite a plausible wrong a…

    Full text817 tasks
    Metric
    truthfulness and informativeness, judged per answer; the multiple-choice configuration reports MC1/MC2 accuracy
    Size
    817 problems imported from the generation / validation selection (the full validation split). The multiple-choice configuration covers the same questions and is not imported separately.
    License
    apache-2.0 (declared on the Hugging Face dataset card)
    Reference links
    4

    Repository: Official repository

  • Agents / Search

    WebArena

    webarena

    WebArena gives an agent self-hosted clones of a shopping site, a forum, a wiki and a code host. It scores whether a natural-language goal was achieved on the live sites.

    Size
    Not imported here; see the official distribution for the current size.
    License
    Apache-2.0 (declared on the source)
    Reference links
    2

    Repository: Official repository

  • Reasoning

    WinoGrande

    winogrande

    WinoGrande scales the Winograd Schema Challenge to 44,000 problems in which a pronoun must be resolved between two sentences that differ by one word. It was built through a procedure that strips dataset-side bias.

    Metric
    accuracy, reported across training-set sizes from 160 to 40,000 examples
    Size
    About 44,000 problems in the source; none imported.
    License
    The dataset card has a Licensing Information section but names no licence.
    Reference links
    4

    Repository: Official repository

  • Reasoning

    ZebraLogicBench

    zebralogicbench

    ZebraLogicBench evaluates reasoning on logic puzzles that require satisfying clues to determine attribute assignments. The official card lists 1,000 grid-mode puzzles and a separate multiple-choice setting.

    Full text1,000 tasks
    Size
    1,000 grid-mode puzzles imported from the grid_mode / test selection (the full split).
    Reference links
    2

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