benchmark_id stringlengths 3 28 | benchmark_name stringlengths 3 56 | category stringclasses 59
values | metric stringclasses 27
values | num_problems float64 6 258k ⌀ | source_url stringlengths 19 115 ⌀ | canonical_setting_json stringlengths 122 1.53k | in_paper_matrix bool 2
classes |
|---|---|---|---|---|---|---|---|
infobench | InFoBench | Instruction following | null | 2,250 | https://github.com/qinyiwei/InfoBench | {"higher_is_better": true, "judge": "GPT-4-0314 judge for decomposed yes/no requirement questions", "metric_type": "pct", "multimodal_input": false, "notes": "Official InfoBench repo, arXiv 2401.03601, and HF kqsong/InFoBench define 500 instructions and 2,250 decomposed questions. The DRFR metric scores whether each de... | true |
c_eval | C-Eval (Chinese) | Knowledge | % | 12,342 | https://huggingface.co/datasets/ceval/ceval-exam | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "HF dataset card reports 13,948 total questions across splits; the test split has 12,342 scored multiple-choice questions across 52 subjects.", "range": [0, 100], "tools": "none", "version": "C-Eval (Chinese)"} | true |
chinese_simpleqa | Chinese-SimpleQA | Knowledge | % | 3,000 | https://huggingface.co/datasets/OpenStellarTeam/Chinese-SimpleQA | {"higher_is_better": true, "judge": "LLM grader", "metric_type": "pct", "multimodal_input": false, "notes": "Protocol audit: short Chinese factual QA. Each item asks a short-answer factual question; model output is judged for correctness against reference answers. HF dataset card reports 3,000 questions across 6 topics... | true |
cmmlu | CMMLU (Chinese) | Knowledge | % | null | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per DeepSeek V4-Pro model card.", "range": [0, 100], "tools": "none", "version": "CMMLU (Chinese)"} | false |
encyclo_k | Encyclo-K | Knowledge | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "Encyclo-K"} | false |
gdpval_aa_elo | GDPval (Artificial Analysis ELO) | Knowledge | score | 220 | https://huggingface.co/datasets/openai/gdpval | {"higher_is_better": true, "judge": "rubric-based grader / pairwise Elo aggregation in Artificial Analysis", "metric_type": "index", "multimodal_input": false, "notes": "GDPval evaluates AI model performance on real-world economically valuable tasks. The official OpenAI HF dataset reports 220 tasks across 44 occupation... | true |
global_mmlu_lite | Global MMLU Lite | Knowledge | null | 7,200 | https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Multilingual MMLU subset by Cohere. HF dataset card reports 18 languages with 400 test examples each. Distinct from MMLU (5-shot 14k EN) and MMMLU (multilingual MMLU full).", "range": [0, 100], "version": "Global MMLU Lite (multilingu... | false |
healthbench | HealthBench | Knowledge | % | 5,000 | https://huggingface.co/datasets/openai/healthbench | {"higher_is_better": true, "judge": "LLM judge over physician-written rubric criteria", "metric_type": "pct", "multimodal_input": false, "notes": "HealthBench evaluates model responses to health and medical conversation prompts. The official OpenAI HF repository points to the HealthBench eval and OpenAI simple-evals re... | true |
lpfqa | LPFQA | Knowledge | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "LPFQA"} | false |
mmlu | MMLU | Knowledge | % correct | 14,042 | https://arxiv.org/abs/2009.03300 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "MMLU (5-shot, 14042 questions)"} | false |
mmlu_pro | MMLU-Pro | Knowledge | % correct | 12,032 | https://arxiv.org/abs/2406.01574 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "MMLU-Pro"} | true |
mmlu_redux | MMLU-Redux | Knowledge | null | null | https://arxiv.org/abs/2406.04127 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Cleaned version of MMLU (Gema et al. 2024). 5-shot.", "range": [0, 100], "version": "MMLU-Redux (5-shot)"} | false |
mmmlu | MMMLU | Knowledge | % correct | 258,090 | https://huggingface.co/datasets/openai/MMMLU | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). HF dataset contains 14 translated MMLU test CSVs; row count across test CSVs is 258,090. If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false... | true |
simpleqa | SimpleQA | Knowledge | % correct | 4,326 | https://openai.com/index/introducing-simpleqa/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "SimpleQA (OpenAI 4326 questions)"} | true |
simpleqa_verified | SimpleQA-Verified | Knowledge | % correct (pass@1) | null | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "SimpleQA-Verified"} | true |
aa_lcr | AA Long Context Reasoning | Long Context | % correct | 300 | https://artificialanalysis.ai/methodology/intelligence-benchmarking | {"higher_is_better": true, "judge": "LLM judge: Qwen3 235B A22B 2507 Non-Reasoning equality checker", "metric_type": "pct", "multimodal_input": false, "notes": "Official AA-LCR uses 100 hard text-based long-context questions over approximately 100k-token inputs, spanning seven document categories and about 230 document... | true |
browsecomp_long_context_128k | BrowseComp Long Context 128k | Long Context | % accuracy | 1,266 | https://openai.com/index/gpt-5-1-for-developers/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "OpenAI GPT-5.1 appendix reports BrowseComp Long Context 128k but does not publish a separate count. Use the official BrowseComp 1,266-row test set as the source-backed count unless a 128k-specific slice is found.", "range": [0, 100], ... | true |
browsecomp_long_context_256k | BrowseComp Long Context 256k | Long Context | null | null | null | {"judge": "rule-based", "notes": "Per OpenAI GPT-5 developer blog https://openai.com/index/introducing-gpt-5-for-developers/"} | false |
cl_bench | CL-Bench | Long Context | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "CL-Bench"} | false |
corpusqa_1m | CorpusQA 1M | Long Context | % | null | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per DeepSeek V4-Pro model card.", "range": [0, 100], "tools": "none", "version": "CorpusQA 1M"} | false |
flenqa_3k | FlenQA (3K-token) | Long Context | null | null | https://arxiv.org/abs/2402.14848 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Long-context QA at 3K tokens.", "range": [0, 100], "version": "FlenQA 3K-token subset"} | false |
graphwalks_bfs_0k_128k | GraphWalks BFS 0-128K | Long Context | % | 300 | https://huggingface.co/datasets/openai/graphwalks | {"higher_is_better": true, "judge": "deterministic set-overlap F1 against answer node list", "metric_type": "pct", "multimodal_input": false, "notes": "OpenAI GraphWalks is a multi-hop reasoning long-context benchmark. Each prompt gives a directed graph as an edge list and asks for either a BFS result set or a parent-n... | true |
graphwalks_bfs_128k_plus | GraphWalks BFS >128k | Long Context | % | null | https://openai.com/index/gpt-4-1/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per OpenAI GPT-4.1 blog.", "range": [0, 100], "tools": "none", "version": "GraphWalks BFS >128k"} | false |
graphwalks_bfs_256k_1m | GraphWalks BFS 256K-1M | Long Context | % f1 (avg 256K-1M) | null | https://openai.com/index/introducing-gpt-5-5/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "GraphWalks BFS 256K-1M"} | false |
graphwalks_parents_0k_128k | GraphWalks parents 0-128K | Long Context | % | 350 | https://huggingface.co/datasets/openai/graphwalks | {"higher_is_better": true, "judge": "deterministic set-overlap F1 against answer node list", "metric_type": "pct", "multimodal_input": false, "notes": "OpenAI GraphWalks is a multi-hop reasoning long-context benchmark. Each prompt gives a directed graph as an edge list and asks for either a BFS result set or a parent-n... | true |
graphwalks_parents_128k_plus | GraphWalks parents >128k | Long Context | % | null | https://openai.com/index/gpt-4-1/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per OpenAI GPT-4.1 blog.", "range": [0, 100], "tools": "none", "version": "GraphWalks parents >128k"} | false |
graphwalks_parents_256k_1m | GraphWalks parents 256K-1M | Long Context | % | null | https://openai.com/index/introducing-gpt-5-4/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per OpenAI GPT-5.4 blog.", "range": [0, 100], "tools": "none", "version": "GraphWalks parents 256K-1M"} | false |
loft_128k | LOFT (128k) | Long Context | % | null | https://x.ai/news/grok-3 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per xAI Grok 3 blog.", "range": [0, 100], "tools": "none", "version": "LOFT (128k)"} | false |
longbench_v2 | LongBench-V2 | Long Context | % | 503 | https://huggingface.co/datasets/THUDM/LongBench-v2 | {"higher_is_better": true, "judge": "exact multiple-choice answer matching", "metric_type": "pct", "multimodal_input": false, "notes": "Official LongBench v2 sources are the THUDM LongBench repo, HF dataset, and arXiv:2412.15204, not the prior DeepSeek model card. The paper/README/HF card state 503 challenging multiple... | true |
mrcr_v2 | MRCR v2 | Long Context | % correct | 2,400 | https://huggingface.co/datasets/openai/mrcr | {"higher_is_better": true, "judge": "difflib SequenceMatcher string-similarity ratio with required hash prefix", "metric_type": "pct", "multimodal_input": false, "notes": "Official source is the openai/mrcr HuggingFace dataset. OpenAI MRCR v2 expands Gemini MRCR into an open long-context multiple-needle benchmark with ... | true |
mrcr_v2_2needle_128k | OpenAI MRCR v2 (2 needle, 128k) | Long Context | % | 500 | https://huggingface.co/datasets/openai/mrcr | {"higher_is_better": true, "judge": "difflib SequenceMatcher string-similarity ratio with required hash prefix", "metric_type": "pct", "multimodal_input": false, "notes": "Official source is the openai/mrcr HuggingFace dataset, with results reported in the OpenAI GPT-4.1 blog. This row is the 2-needle 128k slice: one n... | true |
mrcr_v2_2needle_1m | OpenAI MRCR v2 (2 needle, 1M) | Long Context | % | null | https://openai.com/index/gpt-4-1/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per OpenAI GPT-4.1 blog.", "range": [0, 100], "tools": "none", "version": "OpenAI MRCR v2 (2 needle, 1M)"} | false |
mrcr_v2_2needle_256k | OpenAI MRCR v2 (2-needle, 256k) | Long Context | null | null | null | {"judge": "rule-based", "notes": "Per OpenAI GPT-5 developer blog https://openai.com/index/introducing-gpt-5-for-developers/"} | false |
mrcr_v2_8needle | OpenAI MRCR v2 (8-needle) | Long Context | % | 800 | https://huggingface.co/datasets/openai/mrcr | {"higher_is_better": true, "judge": "difflib SequenceMatcher string-similarity ratio with required hash prefix", "metric_type": "pct", "multimodal_input": false, "notes": "Official source is the openai/mrcr HuggingFace dataset. This row is the 8-needle slice: one needle setting, eight token-length bins from 4k through ... | true |
ruler_128k | RULER 128K | Long Context | % | null | https://mistral.ai/news/mistral-medium-3 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Mistral Medium 3 blog: RULER long-context benchmark @ 128K tokens.", "range": [0, 100], "tools": "none", "version": "RULER 128K"} | false |
ruler_32k | RULER 32K | Long Context | % | null | https://mistral.ai/news/mistral-medium-3 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Mistral Medium 3 blog: RULER long-context benchmark @ 32K tokens.", "range": [0, 100], "tools": "none", "version": "RULER 32K"} | false |
mrcr_v1 | MRCR v1 | Long-context | null | 2,000 | https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf | {"higher_is_better": true, "judge": "difflib SequenceMatcher string-similarity ratio", "metric_type": "pct", "multimodal_input": false, "notes": "Official source is the Gemini 1.5 technical report. MRCR presents a long user-model conversation with two adversarially similar writing requests and asks the model to reprodu... | true |
aime_2024 | AIME 2024 | Math | % correct (pass@1) | 30 | https://artofproblemsolving.com/wiki/index.php/2024_AIME | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "AIME-2024-I+II (30 problems)"} | true |
aime_2025 | AIME 2025 | Math | % correct (pass@1) | 30 | https://artofproblemsolving.com/wiki/index.php/2025_AIME | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "AIME-2025-I+II (30 problems)"} | true |
aime_2026 | AIME 2026 | Math | % correct (pass@1) | 30 | https://huggingface.co/datasets/MathArena/aime_2026_I | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "AIME-2026-I+II (30 problems)"} | true |
apex_shortlist | Apex Shortlist | Math | % correct (pass@1) | null | https://matharena.ai/apex/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "Apex shortlist"} | false |
beyond_aime | Beyond AIME | Math | % | 100 | https://huggingface.co/datasets/ByteDance-Seed/BeyondAIME | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "HF dataset card reports one test split with 100 problems; answers are positive integers with automated exact verification. Per Seed-Thinking-v1.5 paper.", "range": [0, 100], "tools": "none", "version": "Beyond AIME"} | true |
brumo_2025 | BRUMO 2025 | Math | % correct (pass@1) | 30 | https://huggingface.co/datasets/MathArena/brumo_2025 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "sampling": "samples=4", "tools": "none", "version": "BRUMO 2025"} | true |
cmimc_2025 | CMIMC 2025 | Math | % correct (pass@1) | 40 | https://huggingface.co/datasets/MathArena/cmimc_2025 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "sampling": "samples=4", "tools": "none", "version": "CMIMC 2025"} | true |
cnmo_2024 | CNMO 2024 | Math | % | 6 | https://www.cms.org.cn/Home/comp/comp_details/id/1253.html | {"higher_is_better": true, "judge": "rule-based", "metric_type": "pct", "multimodal_input": false, "notes": "Protocol audit: Chinese National High School Mathematics Olympiad 2024 finals, pure text olympiad math. The official CMS page identifies the 2024 national final / 40th winter camp; the standard CMO format is two... | true |
dynamath | DynaMath | Math | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "DynaMath"} | false |
frontiermath | FrontierMath | Math | % correct T1-3 | 300 | https://epoch.ai/benchmarks/frontiermath | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "FrontierMath Tier 1-3 (300)"} | true |
frontiermath_tier4 | FrontierMath Tier 4 | Math | % | 48 | https://epoch.ai/benchmarks/frontiermath | {"higher_is_better": true, "judge": "rule-based answer-function grader", "metric_type": "pct", "multimodal_input": false, "notes": "FrontierMath Tier 4 is the hardest tier of FrontierMath. Epoch’s official FrontierMath page documents the current Inspect-based evaluation: each question asks the model to solve a challeng... | true |
gsm8k | GSM8K | Math | % correct | 1,319 | https://arxiv.org/abs/2110.14168 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "GSM8K (test, 1319 problems)"} | true |
hiddenmath | HiddenMath | Math | null | null | https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-2-0-Flash-Model-Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Google internal held-out math benchmark, AIME/AMC-style, not leaked online.", "range": [0, 100], "version": "HiddenMath (held-out AIME/AMC-like, Google internal)"} | false |
hmmt_feb_2025 | HMMT Feb 2025 | Math | % | 30 | https://huggingface.co/datasets/MathArena/hmmt_feb_2025 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "MathArena dataset has 30 questions; MathArena evaluates each model 4 times per problem.", "range": [0, 100], "sampling": "samples=4", "tools": "none", "version": "HMMT Feb 2025"} | true |
hmmt_feb_2026 | HMMT Feb 2026 | Math | % correct (pass@1) | 33 | https://huggingface.co/datasets/MathArena/hmmt_feb_2026 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "sampling": "samples=4", "tools": "none", "version": "HMMT Feb 202... | true |
hmmt_nov_2025 | HMMT Nov 2025 | Math | % correct | 30 | https://huggingface.co/datasets/MathArena/hmmt_nov_2025 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "sampling": "samples=4", "tools": "none", "version": "HMMT Nov 202... | true |
imo_2025 | IMO 2025 | Math | % of 42 points | 6 | https://matharena.ai/imo/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "IMO 2025"} | false |
imo_answerbench | IMO-AnswerBench | Math | null | 400 | https://imobench.github.io/ | {"higher_is_better": true, "judge": "Gemini 2.5 Pro AnswerAutoGrader", "metric_type": "pct", "multimodal_input": false, "notes": "IMO-Bench official site and arXiv 2511.01846 define IMO-AnswerBench as 400 Olympiad short-answer problems. The paper uses AnswerAutoGrader, built with Gemini 2.5 Pro, to extract final answer... | true |
math | MATH | Math | null | 12,500 | https://github.com/hendrycks/math | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Full MATH benchmark (algebra, geometry, pre-calculus, etc). Distinct from MATH-500 subset.", "range": [0, 100], "version": "MATH (Hendrycks et al. 2021, full set)"} | true |
math_500 | MATH-500 | Math | % correct | 500 | https://arxiv.org/abs/2103.03874 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "MATH-500 subset (Hendrycks)"} | true |
matharena_apex_2025 | MathArena Apex 2025 | Math | % correct | 12 | https://matharena.ai/apex/ | {"higher_is_better": true, "judge": "exact final-answer auto-verification", "metric_type": "pct", "multimodal_input": false, "notes": "Official MathArena Apex page defines the current benchmark as 12 hard final-answer problems selected from 2025 competitions. Problem filtering used 4 attempts with frontier models, but ... | true |
mathcanvas | MathCanvas | Math | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "MathCanvas"} | false |
mathkangaroo | MathKangaroo | Math | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "MathKangaroo"} | false |
mathvision | MathVision | Math | % correct | 3,040 | https://huggingface.co/datasets/MathLLMs/MathVision | {"higher_is_better": true, "judge": "rule-based answer extraction and symbolic/option matching", "metric_type": "pct", "multimodal_input": true, "notes": "Official MATH-Vision sources are the mathllm/MATH-V repo, MathLLMs/MathVision HF dataset, project page, and arXiv:2402.14804. The paper/project page define MATH-Visi... | true |
mgsm | MGSM | Math | exact match (%) | 2,500 | https://github.com/google-research/url-nlp/tree/main/mgsm | {"higher_is_better": true, "judge": "rule-based exact match on numeric answer", "metric_type": "pct", "multimodal_input": false, "notes": "Official source is google-research/url-nlp MGSM, introduced by arXiv:2210.03057. The benchmark manually translates the same 250 GSM8K test problems into 10 languages (Spanish, Frenc... | true |
mt_aime_2024 | MT-AIME2024 | Math | % | 1,650 | https://huggingface.co/datasets/amphora/MCLM | {"higher_is_better": true, "judge": "rule-based verifier", "metric_type": "pct", "multimodal_input": false, "notes": "Official source is the MCLM dataset and Son et al. (arXiv:2502.17407). MT-AIME2024 translates the full AIME 2024 set into 55 languages; the dataset stores 30 rows with 55 language columns, so the canoni... | true |
omnimath | OmniMath | Math | null | null | https://arxiv.org/abs/2410.07985 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Olympiad-level math benchmark.", "range": [0, 100], "version": "OmniMath"} | false |
smt_2025 | SMT 2025 | Math | % correct (pass@1) | 53 | https://huggingface.co/datasets/MathArena/smt_2025 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "sampling": "samples=4", "tools": "none", "version": "SMT 2025"} | true |
usamo_2025 | USAMO 2025 | Math | % of 42 points | 6 | https://huggingface.co/datasets/MathArena/usamo_2025 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "USAMO 2025"} | true |
usamo_2026 | USAMO 2026 | Math | % of 42 points | 6 | https://matharena.ai/usamo/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "USAMO 2026"} | false |
mathvista | MathVista | Math/Vision | % | 1,000 | https://huggingface.co/datasets/AI4Math/MathVista | {"higher_is_better": true, "judge": "rule-based answer extraction and answer-key matching", "metric_type": "pct", "multimodal_input": true, "notes": "Official MathVista sources are the project page, AI4Math/MathVista HF dataset, lupantech/MathVista repo, and arXiv:2310.02255, not the prior OpenAI GPT-4.1 model blog. Th... | true |
medxpertqa_mm | MedXpertQA MM | Medical | null | null | https://huggingface.co/blog/gemma4 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Medical visual QA.", "range": [0, 100], "version": "MedXpertQA Multimodal"} | false |
medxpertqa_text | MedXpertQA (Text) | Medical | null | null | null | {"higher_is_better": true, "judge": "gpt-oss-120b", "metric_type": "pct", "multimodal_input": false, "notes": "Per Meta MSL eval methodology: 2,450 prompts spanning medical specialties; 10 answer choices; graded with gpt-oss-120b. Source: https://ai.meta.com/blog/introducing-muse-spark-msl/", "range": [0, 100], "versio... | false |
disco_x | Disco-X | Multilingual | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "Disco-X"} | false |
multilingual_mmlu | Multilingual MMLU | Multilingual | null | null | https://huggingface.co/microsoft/Phi-4-mini-instruct | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "5-shot MMLU across multiple languages.", "range": [0, 100], "version": "Multilingual MMLU (5-shot)"} | false |
ntrex | NTREX | Multilingual | null | null | https://cohere.com/research/papers/command-a-technical-report.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Machine translation COMET-20 score.", "range": [0, 100], "version": "NTREX (COMET-20)"} | false |
ai2d | AI2D | Multimodal | % | null | https://mistral.ai/news/mistral-medium-3 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Mistral Medium 3 blog: AI2 Diagram understanding benchmark, 0-shot.", "range": [0, 100], "tools": "none", "version": "AI2D"} | false |
babyvision | BabyVision | Multimodal | % accuracy | 388 | https://huggingface.co/datasets/UnipatAI/BabyVision | {"higher_is_better": true, "judge": "LLM judge compares model output to ground truth answer", "metric_type": "pct", "multimodal_input": true, "notes": "Official BabyVision MLLM evaluation has 388 visual reasoning tasks; BabyVision-Gen is a separate generation-track benchmark.", "range": [0, 100], "sampling": "pass@1", ... | true |
chartqa | ChartQA | Multimodal | % | null | https://mistral.ai/news/mistral-medium-3 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Mistral Medium 3 blog: Chart visual question answering, 0-shot.", "range": [0, 100], "tools": "none", "version": "ChartQA"} | false |
charxiv_reasoning | CharXiv Reasoning | Multimodal | % accuracy | 1,000 | https://charxiv.github.io/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Official leaderboard validation set has 1,000 charts and 5,000 questions; HF schema has one reasoning question per chart, so reasoning evaluation is 1,000 model answers.", "range": [0, 100], "sampling": "pass@1", "tools": "none", "vers... | true |
docvqa | DocVQA | Multimodal | % | null | https://mistral.ai/news/mistral-medium-3 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Mistral Medium 3 blog: Document visual question answering, 0-shot.", "range": [0, 100], "tools": "none", "version": "DocVQA"} | false |
mmmu | MMMU | Multimodal | % correct | 900 | https://mmmu-benchmark.github.io/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "MMMU validation (900 questions)"} | true |
mmmu_pro | MMMU-Pro | Multimodal | % correct | 3,460 | https://huggingface.co/datasets/MMMU/MMMU_Pro | {"higher_is_better": true, "judge": "rule-based multiple-choice accuracy", "metric_type": "pct", "multimodal_input": true, "notes": "Official sources are arXiv:2409.02813 and the MMMU/MMMU_Pro HuggingFace dataset. The paper filters MMMU to 1,730 questions, augments them to the standard 10-option setting, and creates a ... | true |
ocrbench | OCRBench | Multimodal | null | null | https://huggingface.co/moonshotai/Kimi-K2.5 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "OCR benchmark.", "range": [0, 100], "version": "OCRBench"} | false |
screenspot_pro | ScreenSpot-Pro | Multimodal | null | 1,581 | https://github.com/likaixin2000/ScreenSpot-Pro-GUI-Grounding | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "ScreenSpot-Pro contains 1,581 GUI grounding targets: 604 icon targets and 977 text targets. Count one model grounding response per target.", "range": [0, 100], "tools": "none", "version": "ScreenSpot-Pro full benchmark"} | true |
vibe_eval | Vibe-Eval | Multimodal | null | 269 | https://github.com/reka-ai/reka-vibe-eval | {"higher_is_better": true, "judge": "Reka Core evaluator scores each response on a 1-5 scale", "metric_type": "pct", "multimodal_input": true, "notes": "Official paper and HF dataset report 269 visual-understanding prompts, including 100 hard prompts. Count model generations as one response per example_id; evaluator ca... | true |
video_mme | Video-MME | Multimodal | null | 2,700 | https://video-mme.github.io/ | {"higher_is_better": true, "metric": "multiple-choice QA accuracy", "metric_type": "pct", "multimodal_input": true, "notes": "Official paper reports 900 videos with 2,700 QA pairs; count actual model generations as one response per QA pair. Distinct from Video-MMMU.", "range": [0, 100], "sampling": "single response per... | true |
mathvista_mini | MathVista (mini) | Multimodal Math | null | null | https://huggingface.co/moonshotai/Kimi-K2.5 | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "avg@3.", "range": [0, 100], "version": "MathVista mini"} | false |
gdpval_oai_woe | GDPval (OpenAI wins-or-ties) | Office | % | null | https://openai.com/index/introducing-gpt-5-5/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "OpenAI GDPval head-to-head wins or ties %, baseline against reference. Single-source: OpenAI GPT-5.5 blog.", "range": [0, 100], "tools": "agentic", "version": "GDPval (OpenAI wins-or-ties)"} | false |
officeqa | OfficeQA | Office | % | null | https://openai.com/index/introducing-gpt-5-4/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "OfficeQA productivity benchmark (different from OfficeQA Pro). Per OpenAI GPT-5.4 blog.", "range": [0, 100], "tools": "agentic", "version": "OfficeQA"} | false |
officeqa_pro | OfficeQA Pro | Office | % | null | https://openai.com/index/introducing-gpt-5-5/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Office productivity QA benchmark.", "range": [0, 100], "tools": "agentic", "version": "OfficeQA Pro"} | false |
phybench | Phybench | Physics | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "Phybench"} | false |
popqa | PopQA | QA | null | 14,267 | https://huggingface.co/datasets/akariasai/PopQA | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Official PopQA HuggingFace dataset contains 14,267 test rows. Count one factual QA generation per row; do not use rounded 14k marketing count.", "range": [0, 100], "tools": "none", "version": "PopQA test set"} | true |
tob_complex_workflows | ToB-ComplexWorkflows | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "ToB-ComplexWorkflows"} | false |
tob_compositional_tasks | ToB-CompositionalTasks | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "ToB-CompositionalTasks"} | false |
tob_information_extraction | ToB-InformationExtraction | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "ToB-InformationExtraction"} | false |
tob_k12_education | ToB-K12Education | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "ToB-K12Education"} | false |
tob_referenceqa | ToB-ReferenceQ&A | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "ToB-ReferenceQ&A"} | false |
tob_text_classification | ToB-TextClassification | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "ToB-TextClassification"} | false |
world_travel_text | WorldTravel (TEXT) | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "WorldTravel (TEXT)"} | false |
world_travel_vlm | WorldTravel (VLM) | Real-world | % | null | https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed2/0214/Seed2.0%20Model%20Card.pdf | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": true, "notes": "Per Doubao Seed 2.0 Pro model card.", "range": [0, 100], "tools": "none", "version": "WorldTravel (VLM)"} | false |
arc_agi_1 | ARC-AGI-1 | Reasoning | % correct | 400 | https://arcprize.org/arc-agi/1/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "ARC-AGI-1 (semi-private 400)"} | true |
arc_agi_2 | ARC-AGI-2 | Reasoning | % correct | 400 | https://arcprize.org/arc-agi/2/ | {"higher_is_better": true, "metric_type": "pct", "multimodal_input": false, "notes": "tools=none preferred (pure-reasoning eval). If only with-tool scores (python/web/RAG) are available, accept and mark cell matches_canonical=false.", "range": [0, 100], "tools": "none", "version": "ARC-AGI-2 (semi-private 400)"} | true |
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