Dataset Viewer
Auto-converted to Parquet Duplicate
scenario
stringclasses
1 value
published_split
stringclasses
1 value
source_split
stringclasses
1 value
evaluation_type
stringclasses
1 value
query_id
stringlengths
15
102
base_query_id
stringclasses
0 values
is_validation
bool
1 class
domain
stringclasses
18 values
source_dataset
stringclasses
18 values
source_sample_id
stringlengths
15
102
variant_type
stringclasses
1 value
query_text
large_stringlengths
7
39k
input_json
large_stringlengths
18
39.7k
media_json
large_stringclasses
1 value
provenance_json
large_stringclasses
24 values
embedding_model
stringclasses
1 value
embedding
list
model_ids
listlengths
10
10
performance
listlengths
10
10
cost_cny
listlengths
10
10
latency_ms
listlengths
10
10
chinese
train
train
train
AlignBench-generation_open-100
null
false
AlignBench
AlignBench
AlignBench-generation_open-100
original
一条宽度为10m,深度为2m的矩形河道中,水以2m/s的速度流动。假设水的密度ρ为1000kg/m³,请给出解答:  a) 河道中水的流量,b) 河道中水的动能,c) 河道中水的总动能。
{"text":"一条宽度为10m,深度为2m的矩形河道中,水以2m/s的速度流动。假设水的密度ρ为1000kg/m³,请给出解答:\n a) 河道中水的流量,b) 河道中水的动能,c) 河道中水的总动能。"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ 0.0034384168684482574, 0.037582479417324066, -0.05768416076898575, 0.0062284269370138645, 0.007879799231886864, -0.018526552245020866, -0.044909022748470306, -0.020905958488583565, 0.007211013697087765, -0.007465955335646868, -0.01676052249968052, -0.011413704603910446, 0.008750895038247108,...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 0.699999988079071, 0.800000011920929, 0.800000011920929, 0.699999988079071, 0.800000011920929, 0.6000000238418579, 0.8999999761581421, 1, 1, 1 ]
[ 0.042561, 0.179253, 0.0306957, 0.1261512, 0.17584, 0.11856, 0.029896, 0.17112, 0.12412, 0.286576 ]
[ 1420413.646, 1038896.002, 4411102.973, 458766.092, 4403567.378, 3302731.031, 4892089.8889999995, 2981129.141, 5731107.698, 15529674.615 ]
chinese
train
train
train
AlignBench-generation_open-101
null
false
AlignBench
AlignBench
AlignBench-generation_open-101
original
下面是一道多选题,它包含多个正确选项,请仔细查看选项并直接给出所有正确的答案。  哪些因素会影响物体在水中的浮力?  A. 物体的质量  B. 物体的体积  C. 水的密度
{"text":"下面是一道多选题,它包含多个正确选项,请仔细查看选项并直接给出所有正确的答案。\n 哪些因素会影响物体在水中的浮力?\n A. 物体的质量\n B. 物体的体积\n C. 水的密度"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ 0.01193390041589737, 0.04081503674387932, -0.06803224980831146, -0.00573762459680438, -0.010950729250907898, -0.03551159426569939, -0.03350245580077171, 0.031539786607027054, 0.012936996296048164, -0.00961801502853632, -0.02431526966392994, 0.009463091380894184, -0.008843454532325268, -0.0...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 1, 0.8999999761581421, 1, 0.8999999761581421, 0.699999988079071, 0.699999988079071, 1, 0.699999988079071, 0.699999988079071, 0.8999999761581421 ]
[ 0.003372, 0.008091, 0.0030093, 0.0067872, 0.02404, 0.042012, 0.015686, 0.009504, 0.033332, 0.045204 ]
[ 1313172.115, 310968.62700000004, 4144054.6969999997, 367965.716, 4131023.421, 3140027.25, 4722989.0770000005, 2710936.975, 5557699.068, 14839562.978 ]
chinese
train
train
train
AlignBench-generation_open-102
null
false
AlignBench
AlignBench
AlignBench-generation_open-102
original
世界最大的海港是哪个海港?
{"text":"世界最大的海港是哪个海港?"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ 0.020801996812224388, 0.02039501816034317, -0.026009758934378624, -0.008262568153440952, -0.016737347468733788, 0.010994532145559788, -0.037320587784051895, -0.04788618162274361, 0.02067515254020691, -0.0007475424208678305, 0.014601526781916618, 0.026821594685316086, -0.0033523410093039274, ...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 1, 1, 0.5, 1, 1, 0.8999999761581421, 1, 0.8999999761581421, 1, 1 ]
[ 0.003207, 0.015021, 0.0130074, 0.0065058, 0.06512, 0.055848, 0.009016, 0.011064, 0.029624, 0.021196 ]
[ 1401770.131, 4014746.378, 4529121.095000001, 445400.78599999996, 4551499.664, 3388445.228, 4949277.829, 3008104.0429999996, 5780302.337, 1126739.1230000001 ]
chinese
train
train
train
AlignBench-generation_open-104
null
false
AlignBench
AlignBench
AlignBench-generation_open-104
original
下面是一道多选题,它包含多个正确选项,请仔细查看选项并直接给出所有正确的答案。  下面哪些是海水淡化方法()  A、蒸馏法  B、反渗透法  C、水合物法  D、冰冻法
{"text":"下面是一道多选题,它包含多个正确选项,请仔细查看选项并直接给出所有正确的答案。\n 下面哪些是海水淡化方法()\n A、蒸馏法\n B、反渗透法\n C、水合物法\n D、冰冻法"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ 0.012650148943066597, 0.027920974418520927, -0.03380533680319786, -0.006213318090885878, -0.013992627151310444, -0.018807945773005486, 0.023878643289208412, 0.014611367136240005, 0.013833132572472095, 0.024382293224334717, -0.017722269520163536, -0.013114726170897484, 0.02784876711666584, ...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 0.5, 0.4000000059604645, 0.30000001192092896, 0.5, 0.5, 0.5, 0.6000000238418579, 0.4000000059604645, 0.4000000059604645, 0.4000000059604645 ]
[ 0.001341, 0.003159, 0.006006, 0.0058338, 0.06698, 0.0162, 0.004756, 0.006336, 0.009932, 0.018024 ]
[ 1299540.303, 3731790.282, 4132454.758, 361997.387, 4121603.852, 3115158.132, 4680565.549, 2690109.809, 5525870.468, 14707116.908 ]
chinese
train
train
train
AlignBench-generation_open-105
null
false
AlignBench
AlignBench
AlignBench-generation_open-105
original
下面是一道多选题,它包含多个正确选项,请仔细查看选项并直接给出所有正确的答案。  第一次和第二次世界大战爆发的导火线事件是?  A. 萨拉热窝事件  B. 波茨坦会议  C. 波兰偷袭  D. 孤立政策
{"text":"下面是一道多选题,它包含多个正确选项,请仔细查看选项并直接给出所有正确的答案。\n 第一次和第二次世界大战爆发的导火线事件是?\n A. 萨拉热窝事件\n B. 波茨坦会议\n C. 波兰偷袭\n D. 孤立政策"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ 0.06774086505174637, 0.001862619537860155, -0.0658533126115799, 0.036766137927770615, -0.018232813104987144, -0.02153130993247032, -0.009020081721246243, -0.006530591286718845, 0.017466183751821518, 0.011050984263420105, 0.027185000479221344, -0.002260406967252493, 0.005167292430996895, -0...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 1, 0.8999999761581421, 1, 0.8999999761581421, 1, 0.8999999761581421, 0.8999999761581421, 1, 0.8999999761581421, 1 ]
[ 0.007851, 0.012753, 0.0046704, 0.0088074, 0.04216, 0.027288, 0.006718, 0.01962, 0.05088, 0.052476 ]
[ 1362337.3180000002, 3885440.42, 4311534.735, 409789.778, 4317438.889, 3251480.974, 4821066.237, 2864046.638, 5682200.477, 15311875.312 ]
chinese
train
train
train
AlignBench-generation_open-107
null
false
AlignBench
AlignBench
AlignBench-generation_open-107
original
板块构造学说是谁提出的?主要观点是什么?
{"text":"板块构造学说是谁提出的?主要观点是什么?"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ -0.026792019605636597, -0.0051191444508731365, -0.013297738507390022, 0.03367234393954277, 0.016255715861916542, -0.006690606940537691, -0.03842083364725113, -0.006047685164958239, 0.0037578302435576916, -0.011371535249054432, -0.006634525954723358, 0.04089149087667465, -0.025997359305620193...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 0.8999999761581421, 0.8999999761581421, 1, 1, 0.8999999761581421, 0.8999999761581421, 0.8999999761581421, 0.800000011920929, 1, 0.8999999761581421 ]
[ 0.00639, 0.031806, 0.0137466, 0.0147798, 0.26582, 0.100152, 0.017498, 0.042588, 0.054336, 0.083316 ]
[ 1335168.621, 3797431.637, 4251190.847, 390591.143, 4265622.058, 3202605.1659999997, 4769510.926, 2773821.5409999997, 5613221.361, 98581.959 ]
chinese
train
train
train
AlignBench-generation_open-108
null
false
AlignBench
AlignBench
AlignBench-generation_open-108
original
惟知跃进是谁的口号?
{"text":"惟知跃进是谁的口号?"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ -0.026676656678318977, 0.00369324185885489, -0.006559711880981922, 0.0011632699752226472, -0.005029201973229647, -0.06069295108318329, -0.005657801870256662, 0.013673890382051468, -0.031810320913791656, 0.004280693829059601, -0.026744535192847252, -0.01820833794772625, -0.027271291241049767,...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 1, 0.8999999761581421, 0.30000001192092896, 0.30000001192092896, 0.8999999761581421, 0.30000001192092896, 0.20000000298023224, 0.30000001192092896, 1, 0.10000000149011612 ]
[ 0.001605, 0.003195, 0.0014385, 0.0083832, 0.07252, 0.09762, 0.032458, 0.0483, 0.020544, 0.008784 ]
[ 1323292.5210000002, 3746976.3680000002, 4163583.0340000005, 379213.196, 4180928.7830000003, 3196022.608, 91555.526, 2761332.265, 5587132.19, 14879066.290000001 ]
chinese
train
train
train
AlignBench-generation_open-109
null
false
AlignBench
AlignBench
AlignBench-generation_open-109
original
什么是超新星?
{"text":"什么是超新星?"}
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[ -0.024349210783839226, -0.03600218892097473, -0.056103359907865524, 0.03659119829535484, 0.016003184020519257, -0.036291297525167465, -0.030122457072138786, 0.027094734832644463, 0.004578980151563883, -0.0076813772320747375, -0.022359661757946014, 0.03168077394366264, -0.008781712502241135, ...
[ "deepseek/deepseek-v4-flash", "deepseek/deepseek-v4-pro", "minimax/minimax-m2.7", "minimax/minimax-m3", "moonshot/kimi-k3", "qwen/qwen3.7-max", "qwen/qwen3.7-plus", "qwen/qwen3.8-max", "z-ai/glm-5.2", "z-ai/glm-5.3" ]
[ 0.800000011920929, 1, 0.800000011920929, 1, 1, 0.800000011920929, 1, 0.8999999761581421, 0.800000011920929, 1 ]
[ 0.011835, 0.027864, 0.0190218, 0.0135618, 0.11558, 0.063564, 0.013428, 0.013164, 0.055288, 0.03258 ]
[ 1394748.411, 3968260.1169999996, 4470372.277000001, 435560.527, 4489060.470000001, 3346197.184, 4912531.79, 2947457.983, 497436.361, 15652300.233 ]
chinese
train
train
train
AlignBench-generation_open-11
null
false
AlignBench
AlignBench
AlignBench-generation_open-11
original
"法国、英国、西班牙、瑞士、德国、意大利、荷兰、比利时,其中任意2个(...TRUNCATED)
"{\"text\":\"法国、英国、西班牙、瑞士、德国、意大利、荷兰、比利时,其中(...TRUNCATED)
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[-0.014275794848799706,0.029666796326637268,-0.011096927337348461,-0.027701865881681442,-0.010627190(...TRUNCATED)
["deepseek/deepseek-v4-flash","deepseek/deepseek-v4-pro","minimax/minimax-m2.7","minimax/minimax-m3"(...TRUNCATED)
[ 0.699999988079071, 0.699999988079071, 1, 1, 1, 0.8999999761581421, 1, 1, 1, 1 ]
[ 0.007893, 0.004752, 0.0160482, 0.0188958, 0.1075, 0.059496, 0.009102, 0.03246, 0.040064, 0.035556 ]
[1351429.14,3832811.867,4321172.358,408035.02099999995,4292436.583,3227586.6289999997,4794786.917,28(...TRUNCATED)
chinese
train
train
train
AlignBench-generation_open-111
null
false
AlignBench
AlignBench
AlignBench-generation_open-111
original
"有哪些在家庭经济学领域擅长理论模型推导、在核心期刊上发表过高被引文(...TRUNCATED)
"{\"text\":\"有哪些在家庭经济学领域擅长理论模型推导、在核心期刊上发表过(...TRUNCATED)
[]
{"benchmark":"AlignBench","task_category":["Generation"],"kind":"original"}
BAAI/bge-m3
[-0.03772673010826111,0.0011747878743335605,-0.05109231919050217,-0.006364902015775442,0.02295780926(...TRUNCATED)
["deepseek/deepseek-v4-flash","deepseek/deepseek-v4-pro","minimax/minimax-m2.7","minimax/minimax-m3"(...TRUNCATED)
[0.800000011920929,0.8999999761581421,0.6000000238418579,0.5,0.800000011920929,1.0,0.899999976158142(...TRUNCATED)
[ 0.016542, 0.098631, 0.0118818, 0.0058926, 1.62962, 0.157272, 0.042778, 0.590784, 0.116828, 0.033152 ]
[1430586.1930000002,414549.77999999997,3518504.119,460142.48,1889575.321,3481438.6909999996,5062656.(...TRUNCATED)
End of preview. Expand in Data Studio

RobustRouteBench

RobustRouteBench is a unified public benchmark for general-text, multimodal, and Chinese-language model routing.

from datasets import load_dataset

text = load_dataset("SinapisAI/RobustRouteBench", "text")
multimodal = load_dataset("SinapisAI/RobustRouteBench", "multimodal")
chinese = load_dataset("SinapisAI/RobustRouteBench", "chinese")

Each scenario exposes separate train, validation, and test splits. The 500-row validation split is a convenience overlay containing 400 standard and 100 robust queries. Those rows intentionally remain in the complete test split, so validation and test overlap by design.

The test split contains both standard and robustness evaluation rows. Use evaluation_type (standard or robust) and variant_type to report slices.

Each row is query-centric. performance, cost_cny, and latency_ms are aligned to model_ids. All costs use CNY; historical USD proxies use the frozen benchmark conversion 1 USD = 7.2 CNY.

Source outcomes that were not successful have performance zero. No source diagnostic payloads or raw model responses are published. Official BGE-M3 embeddings are included as baseline features but alternative encoders are allowed.

The repository is self-contained. All MMR-4000 training images and all standard and robustness evaluation media are stored under media/multimodal/; no other dataset repository is required to train or evaluate a router. See LICENSES.md for upstream attribution and terms. This compilation uses license: other; no blanket license is asserted over all upstream questions or images.

Downloads last month
-