Dataset Viewer
Auto-converted to Parquet Duplicate
model
stringlengths
2
23
provider
stringclasses
16 values
rank
int64
1
83
average
float64
0.18
0.76
benchmarks_completed
int64
7
7
teleqna
stringlengths
14
20
teletables
stringlengths
12
20
oranbench
stringlengths
14
20
srsranbench
stringlengths
14
20
telemath
stringlengths
12
20
telelogs
stringlengths
10
20
three_gpp
stringlengths
12
20
gemini-3.1-pro-preview
Google
1
0.755529
7
[0.852, 0.0112]
[0.48, 0.0502]
[0.86, 0.028426]
[0.8467, 0.029518]
[0.73, 0.0446]
[0.82, 0.0386]
[0.7, 0.0461]
gemini-3-pro-preview
Google
2
0.746524
7
[0.829, 0.011912]
[0.45, 0.05]
[0.833333, 0.030531]
[0.813333, 0.031921]
[0.78, 0.041633]
[0.86, 0.034874]
[0.66, 0.04761]
claude-opus-4.6
Anthropic
3
0.732957
7
[0.844, 0.0115]
[0.43, 0.0498]
[0.9, 0.024577]
[0.8467, 0.029518]
[0.75, 0.0435]
[0.7, 0.0461]
[0.66, 0.0476]
gpt-5
OpenAI
4
0.718757
7
[0.838, 0.011657]
[0.37, 0.048524]
[0.86, 0.028426]
[0.8133, 0.031921]
[0.79, 0.040936]
[0.78, 0.041633]
[0.58, 0.049604]
gemini-3-flash-preview
Google
5
0.704095
7
[0.832, 0.011829]
[0.48, 0.050212]
[0.893333, 0.025289]
[0.833333, 0.030531]
[0.8, 0.040202]
[0.46, 0.050091]
[0.63, 0.048524]
claude-opus-4.5
Anthropic
6
0.696429
7
[0.845, 0.01145]
[0.39, 0.049021]
[0.893333, 0.025289]
[0.826667, 0.031011]
[0.72, 0.045126]
[0.56, 0.049889]
[0.64, 0.048242]
kimi-k2.5
Moonshot AI
7
0.694195
7
[0.836, 0.011715]
[0.42, 0.049604]
[0.826667, 0.031011]
[0.8467, 0.029518]
[0.76, 0.042923]
[0.6, 0.049237]
[0.57, 0.049757]
o3
OpenAI
8
0.693905
7
[0.834, 0.011772]
[0.34, 0.04761]
[0.846667, 0.029518]
[0.786667, 0.033561]
[0.74, 0.044084]
[0.72, 0.045126]
[0.59, 0.049431]
grok-4-fast
xAI
9
0.68419
7
[0.836, 0.011715]
[0.35, 0.047937]
[0.833333, 0.030531]
[0.84, 0.030034]
[0.66, 0.04761]
[0.75, 0.043519]
[0.52, 0.050212]
claude-opus-4
Anthropic
10
0.682476
7
[0.824, 0.012049]
[0.36, 0.048242]
[0.846667, 0.029518]
[0.806667, 0.032352]
[0.71, 0.045605]
[0.65, 0.047937]
[0.58, 0.049604]
o1
OpenAI
11
0.680814
7
[0.819, 0.012181]
[0.29, 0.045605]
[0.86, 0.028426]
[0.8067, 0.032352]
[0.72, 0.045126]
[0.73, 0.04462]
[0.54, 0.050091]
claude-opus-4.1
Anthropic
12
0.680429
7
[0.823, 0.012075]
[0.36, 0.048242]
[0.8733, 0.027248]
[0.8067, 0.032352]
[0.73, 0.04462]
[0.59, 0.049431]
[0.58, 0.049604]
grok-4.1-fast
xAI
13
0.671371
7
[0.813, 0.012336]
[0.32, 0.046883]
[0.7933, 0.033172]
[0.8133, 0.031921]
[0.7, 0.046057]
[0.71, 0.045605]
[0.55, 0.05]
claude-sonnet-4.5
Anthropic
14
0.660424
7
[0.823, 0.012075]
[0.32, 0.046883]
[0.866667, 0.027849]
[0.7733, 0.034299]
[0.68, 0.046883]
[0.59, 0.049431]
[0.57, 0.049757]
claude-3.7-sonnet
Anthropic
15
0.655619
7
[0.816, 0.012259]
[0.38, 0.048783]
[0.82, 0.031474]
[0.773333, 0.034299]
[0.65, 0.047937]
[0.59, 0.049431]
[0.56, 0.049889]
claude-sonnet-4
Anthropic
16
0.648095
7
[0.83, 0.011884]
[0.31, 0.046482]
[0.853333, 0.028982]
[0.813333, 0.031921]
[0.7, 0.046057]
[0.47, 0.050161]
[0.56, 0.049889]
minimax-m2.5
MiniMax
17
0.64681
7
[0.791, 0.0129]
[0.35, 0.0479]
[0.8, 0.032769]
[0.846667, 0.029518]
[0.67, 0.0473]
[0.56, 0.0499]
[0.51, 0.0502]
grok-code-fast-1
xAI
18
0.642143
7
[0.785, 0.012998]
[0.32, 0.046883]
[0.76, 0.034988]
[0.82, 0.031474]
[0.65, 0.047937]
[0.61, 0.049021]
[0.55, 0.05]
gemini-2.5-pro
Google
19
0.639719
7
[0.818, 0.012208]
[0.3, 0.046057]
[0.813333, 0.031921]
[0.8467, 0.029518]
[0.75, 0.043519]
[0.37, 0.048524]
[0.58, 0.049604]
qwen3-235b-a22b-2507
Qwen
20
0.636229
7
[0.797, 0.012726]
[0.37, 0.048524]
[0.7933, 0.033172]
[0.7933, 0.033172]
[0.64, 0.048242]
[0.61, 0.049021]
[0.45, 0.05]
gpt-4.1
OpenAI
21
0.633852
7
[0.817, 0.012234]
[0.35, 0.047937]
[0.826667, 0.031011]
[0.8133, 0.031921]
[0.64, 0.048242]
[0.46, 0.050091]
[0.53, 0.050161]
gemini-2.5-flash
Google
22
0.633048
7
[0.788, 0.012931]
[0.38, 0.048783]
[0.833333, 0.030531]
[0.84, 0.030034]
[0.64, 0.048242]
[0.41, 0.049431]
[0.54, 0.050091]
gpt-5.2
OpenAI
23
0.631481
7
[0.827, 0.011967]
[0.33, 0.047258]
[0.866667, 0.027849]
[0.8267, 0.031011]
[0.71, 0.045605]
[0.33, 0.047258]
[0.53, 0.050161]
o4-mini
OpenAI
24
0.630667
7
[0.808, 0.012462]
[0.4, 0.049237]
[0.806667, 0.032352]
[0.78, 0.033936]
[0.68, 0.046883]
[0.49, 0.050242]
[0.45, 0.05]
gemini-2.0-flash-001
Google
25
0.614048
7
[0.805, 0.012535]
[0.37, 0.048524]
[0.793333, 0.033172]
[0.82, 0.031474]
[0.61, 0.049021]
[0.34, 0.04761]
[0.56, 0.049889]
grok-3-mini
xAI
26
0.610381
7
[0.806, 0.012511]
[0.38, 0.048783]
[0.806667, 0.032352]
[0.82, 0.031474]
[0.48, 0.050212]
[0.47, 0.050161]
[0.51, 0.050242]
claude-3.5-sonnet
Anthropic
27
0.608714
7
[0.821, 0.012129]
[0.3, 0.046057]
[0.78, 0.033936]
[0.84, 0.030034]
[0.56, 0.049889]
[0.41, 0.049431]
[0.55, 0.05]
mimo-v2-flash
Xiaomi
28
0.608486
7
[0.786, 0.012976]
[0.29, 0.045605]
[0.7867, 0.033561]
[0.8667, 0.027849]
[0.64, 0.048242]
[0.34, 0.04761]
[0.55, 0.05]
gpt-5-nano
OpenAI
29
0.603043
7
[0.768, 0.013355]
[0.41, 0.049431]
[0.78, 0.033936]
[0.7933, 0.033172]
[0.67, 0.047258]
[0.48, 0.050212]
[0.32, 0.046883]
gpt-5.1
OpenAI
30
0.601476
7
[0.827, 0.011967]
[0.36, 0.048242]
[0.853333, 0.028982]
[0.8, 0.032769]
[0.65, 0.047937]
[0.16, 0.036845]
[0.56, 0.049889]
claude-haiku-4.5
Anthropic
31
0.599857
7
[0.789, 0.012909]
[0.28, 0.045126]
[0.84, 0.030034]
[0.8, 0.032769]
[0.63, 0.048524]
[0.34, 0.04761]
[0.52, 0.050212]
llama-4-maverick
Meta
32
0.596
7
[0.802, 0.012608]
[0.34, 0.04761]
[0.7, 0.037542]
[0.82, 0.031474]
[0.66, 0.04761]
[0.35, 0.047937]
[0.5, 0.050252]
deepseek-v3-0324
DeepSeek
33
0.592762
7
[0.816, 0.012259]
[0.28, 0.045126]
[0.793333, 0.033172]
[0.8, 0.032769]
[0.57, 0.049757]
[0.4, 0.049237]
[0.49, 0.050242]
gpt-oss-120b
OpenAI
34
0.582714
7
[0.799, 0.012679]
[0.3, 0.046057]
[0.8, 0.032769]
[0.84, 0.030034]
[0.57, 0.049757]
[0.45, 0.05]
[0.32, 0.046883]
gpt-4.1-mini
OpenAI
35
0.580243
7
[0.795, 0.012773]
[0.3, 0.046057]
[0.8067, 0.032352]
[0.8, 0.032769]
[0.59, 0.049431]
[0.4, 0.049237]
[0.37, 0.048524]
llama-v3p3-70b-instruct
Meta
36
0.546614
7
[0.773, 0.013253]
[0.29, 0.045605]
[0.76, 0.034988]
[0.8533, 0.028982]
[0.45, 0.05]
[0.18, 0.038612]
[0.52, 0.050212]
qwen2.5-72b
Qwen
37
0.539686
7
[0.7649, 0.0042]
[0.292, 0.0203]
[0.7471, 0.0112]
[0.7947, 0.0104]
[0.4547, 0.021]
[0.2731, 0.013]
[0.4513, 0.0111]
command-a
Cohere
38
0.535
7
[0.775, 0.013212]
[0.29, 0.045605]
[0.82, 0.031474]
[0.78, 0.033936]
[0.41, 0.049431]
[0.28, 0.045126]
[0.39, 0.049021]
mistral-small-24b
Mistral
39
0.516271
7
[0.7374, 0.0044]
[0.2973, 0.0204]
[0.7171, 0.0116]
[0.763, 0.011]
[0.3613, 0.0207]
[0.2311, 0.0127]
[0.5067, 0.0112]
gpt-4o-mini
OpenAI
40
0.512519
7
[0.751, 0.013682]
[0.37, 0.048524]
[0.753333, 0.035315]
[0.8133, 0.031921]
[0.39, 0.049021]
[0.13, 0.0338]
[0.38, 0.048783]
qwen2.5-32b
Qwen
41
0.506714
7
[0.7499, 0.0043]
[0.2853, 0.0201]
[0.7327, 0.0114]
[0.7594, 0.011]
[0.4187, 0.0207]
[0.2488, 0.0128]
[0.3522, 0.0107]
phi-4-14b
Microsoft
42
0.504529
7
[0.7308, 0.0044]
[0.2993, 0.0199]
[0.7369, 0.0114]
[0.8336, 0.0096]
[0.4007, 0.0209]
[0.1979, 0.0115]
[0.3325, 0.0105]
gemma3-27b
Google
43
0.504329
7
[0.7131, 0.0045]
[0.3333, 0.0211]
[0.7158, 0.0116]
[0.8056, 0.0102]
[0.4067, 0.0205]
[0.1601, 0.0108]
[0.3957, 0.0109]
gpt-5-mini
OpenAI
44
0.501957
7
[0.447, 0.01573]
[0.28, 0.045126]
[0.5267, 0.040903]
[0.6, 0.040134]
[0.71, 0.045605]
[0.52, 0.050212]
[0.43, 0.049757]
claude-3.5-haiku
Anthropic
45
0.495233
7
[0.74, 0.013878]
[0.25, 0.043519]
[0.7733, 0.034299]
[0.753333, 0.035315]
[0.36, 0.048242]
[0.15, 0.035887]
[0.44, 0.049889]
gpt-4
OpenAI
46
0.485762
7
[0.767, 0.013375]
[0.18, 0.038612]
[0.733333, 0.036228]
[0.78, 0.033936]
[0.19, 0.039428]
[0.22, 0.041633]
[0.53, 0.050161]
qwen2.5-14b
Qwen
47
0.485386
7
[0.7248, 0.0045]
[0.294, 0.0204]
[0.7256, 0.0115]
[0.7763, 0.0108]
[0.324, 0.0193]
[0.218, 0.0112]
[0.335, 0.0106]
gpt-4.1-nano
OpenAI
48
0.482757
7
[0.726, 0.014111]
[0.28, 0.045126]
[0.7133, 0.037046]
[0.74, 0.035934]
[0.52, 0.050212]
[0.13, 0.0338]
[0.27, 0.04462]
qwen3-32b
Qwen
49
0.467714
7
[0.784, 0.013]
[0.34, 0.048]
[0.746667, 0.03563]
[0.793333, 0.033172]
[0.27, 0.045]
[0.0, 0.0]
[0.34, 0.048]
mistral-small-22b
Mistral
50
0.4666
7
[0.6698, 0.0047]
[0.284, 0.0201]
[0.6936, 0.0119]
[0.7696, 0.0109]
[0.2353, 0.0179]
[0.2442, 0.0132]
[0.3697, 0.0108]
gemma3-12b
Google
51
0.463843
7
[0.6879, 0.0046]
[0.274, 0.0199]
[0.6953, 0.0119]
[0.8007, 0.0103]
[0.274, 0.0175]
[0.1852, 0.011]
[0.3298, 0.0105]
falcon3-10b
TII
52
0.458814
7
[0.6866, 0.0046]
[0.2767, 0.02]
[0.6369, 0.0124]
[0.7679, 0.0109]
[0.3433, 0.0204]
[0.191, 0.0117]
[0.3093, 0.0103]
gemma2-27b
Google
53
0.458486
7
[0.718, 0.0045]
[0.2727, 0.0199]
[0.7071, 0.0118]
[0.8111, 0.0101]
[0.2393, 0.0181]
[0.1142, 0.0108]
[0.347, 0.0106]
claude-3-haiku
Anthropic
54
0.458376
7
[0.722, 0.014175]
[0.27, 0.04462]
[0.753333, 0.035315]
[0.7533, 0.035315]
[0.21, 0.040936]
[0.1, 0.030151]
[0.4, 0.049237]
qwen2.5-7b
Qwen
55
0.457943
7
[0.7024, 0.0046]
[0.3007, 0.0205]
[0.6982, 0.0118]
[0.7772, 0.0107]
[0.2973, 0.019]
[0.1431, 0.0084]
[0.2867, 0.0101]
claude-sonnet-4.6
Anthropic
56
0.44781
7
[0.448, 0.0157]
[0.44, 0.0499]
[0.266667, 0.036228]
[0.2, 0.032769]
[0.7, 0.0461]
[0.46, 0.0501]
[0.62, 0.0488]
phi-4
Microsoft
57
0.444095
7
[0.682, 0.014734]
[0.21, 0.040936]
[0.713333, 0.037046]
[0.833333, 0.030531]
[0.33, 0.047258]
[0.04, 0.019695]
[0.3, 0.046057]
gpt-3.5-turbo
OpenAI
58
0.438524
7
[0.713, 0.014312]
[0.21, 0.040936]
[0.7, 0.037542]
[0.786667, 0.033561]
[0.17, 0.037753]
[0.09, 0.028762]
[0.4, 0.049237]
granite3.2-8b
IBM
59
0.433729
7
[0.6745, 0.0047]
[0.288, 0.0203]
[0.676, 0.0121]
[0.8107, 0.0101]
[0.126, 0.0127]
[0.1489, 0.012]
[0.312, 0.0103]
deepseek-r1-0528
DeepSeek
60
0.433714
7
[0.726, 0.014111]
[0.11, 0.031447]
[0.786667, 0.033561]
[0.793333, 0.033172]
[0.12, 0.03266]
[0.0, 0.0]
[0.5, 0.050252]
gemma2-9b
Google
61
0.433614
7
[0.6938, 0.0046]
[0.2893, 0.0203]
[0.6982, 0.0118]
[0.8038, 0.0102]
[0.1513, 0.0149]
[0.1177, 0.0107]
[0.2812, 0.01]
qwen2.5-coder-7b
Qwen
62
0.421043
7
[0.6692, 0.0047]
[0.294, 0.02]
[0.6702, 0.0121]
[0.6507, 0.0123]
[0.2067, 0.0169]
[0.1782, 0.0112]
[0.2783, 0.01]
mistral-nemo-12b
Mistral
63
0.417729
7
[0.6422, 0.0048]
[0.278, 0.0199]
[0.6611, 0.0122]
[0.7, 0.037542]
[0.1053, 0.0121]
[0.142, 0.0103]
[0.3955, 0.0109]
granite3.3-8b
IBM
64
0.414771
7
[0.6578, 0.0047]
[0.2813, 0.0201]
[0.6618, 0.0122]
[0.7579, 0.011]
[0.1433, 0.0138]
[0.1335, 0.0107]
[0.2678, 0.0099]
qwen2.5-3b
Qwen
65
0.4133
7
[0.6638, 0.0047]
[0.272, 0.0183]
[0.6713, 0.012]
[0.7281, 0.0114]
[0.1373, 0.0136]
[0.1381, 0.0085]
[0.2825, 0.01]
qwen3-8b
Qwen
66
0.410714
7
[0.745, 0.014]
[0.29, 0.046]
[0.673333, 0.038422]
[0.746667, 0.03563]
[0.11, 0.031]
[0.0, 0.0]
[0.31, 0.046]
deepseek-v3p2
DeepSeek
67
0.409
7
[0.713, 0.014312]
[0.08, 0.027266]
[0.78, 0.033936]
[0.78, 0.033936]
[0.08, 0.027266]
[0.0, 0.0]
[0.43, 0.049757]
phi-3.5-mini
Microsoft
68
0.408014
7
[0.6216, 0.0048]
[0.2887, 0.0202]
[0.6338, 0.0124]
[0.7559, 0.011]
[0.162, 0.0151]
[0.1181, 0.0106]
[0.276, 0.01]
internlm2.5-20b
InternLM
69
0.405914
7
[0.6721, 0.0047]
[0.256, 0.0189]
[0.6231, 0.0125]
[0.7011, 0.0118]
[0.126, 0.0133]
[0.1566, 0.0102]
[0.3065, 0.0103]
phi-4-mini
Microsoft
70
0.405329
7
[0.6489, 0.0048]
[0.2593, 0.0194]
[0.6996, 0.0118]
[0.7213, 0.0115]
[0.2233, 0.0165]
[0.0262, 0.004]
[0.2587, 0.0097]
internlm2.5-7b
InternLM
71
0.403771
7
[0.669, 0.0047]
[0.3013, 0.0204]
[0.6547, 0.0122]
[0.7381, 0.0113]
[0.088, 0.0107]
[0.1211, 0.0082]
[0.2542, 0.0097]
deepseek-r1-32b
DeepSeek
72
0.403524
7
[0.511333, 0.014985]
[0.073333, 0.023962]
[0.746667, 0.03563]
[0.786667, 0.033561]
[0.376667, 0.045358]
[0.173333, 0.0323]
[0.156667, 0.035598]
gemma3-4b
Google
73
0.397
7
[0.652333, 0.01506]
[0.273333, 0.04454]
[0.66, 0.038808]
[0.74, 0.035934]
[0.136667, 0.029242]
[0.116667, 0.026958]
[0.2, 0.040202]
falcon3-3b
TII
74
0.377157
7
[0.6082, 0.0049]
[0.2127, 0.018]
[0.5504, 0.0128]
[0.6303, 0.0124]
[0.23, 0.0177]
[0.1312, 0.009]
[0.2773, 0.01]
command-r7b-12-2024
Cohere
75
0.355757
7
[0.517, 0.01581]
[0.2, 0.040202]
[0.6333, 0.039478]
[0.72, 0.036783]
[0.06, 0.023868]
[0.12, 0.03266]
[0.24, 0.042923]
gemma2-2b
Google
76
0.353029
7
[0.6051, 0.0049]
[0.272, 0.0199]
[0.63, 0.0125]
[0.7364, 0.0114]
[0.0167, 0.0057]
[0.0035, 0.002]
[0.2075, 0.0091]
qwen2.5-1.5b
Qwen
77
0.351986
7
[0.6102, 0.0049]
[0.274, 0.02]
[0.6193, 0.0125]
[0.5672, 0.0128]
[0.0413, 0.0071]
[0.1119, 0.0086]
[0.24, 0.0095]
mixtral-8x7b
Mistral
78
0.348957
7
[0.4878, 0.005]
[0.1987, 0.0171]
[0.5669, 0.0127]
[0.6522, 0.0122]
[0.1, 0.0106]
[0.0999, 0.0073]
[0.3372, 0.0105]
mistral-7b
Mistral
79
0.310343
7
[0.4532, 0.005]
[0.188, 0.0174]
[0.4687, 0.0128]
[0.5923, 0.0126]
[0.0607, 0.0087]
[0.1227, 0.0092]
[0.2868, 0.0101]
gemma3-1b
Google
80
0.2727
7
[0.4567, 0.005]
[0.206, 0.0181]
[0.526, 0.0129]
[0.5912, 0.0127]
[0.032, 0.0079]
[0.0, 0.0]
[0.097, 0.0066]
qwen2.5-0.5b
Qwen
81
0.243086
7
[0.4642, 0.0049]
[0.18, 0.0151]
[0.4789, 0.0128]
[0.3668, 0.0123]
[0.026, 0.0056]
[0.0, 0.0]
[0.1857, 0.0086]
falcon3-1b
TII
82
0.225529
7
[0.3696, 0.0048]
[0.114, 0.0142]
[0.44, 0.0128]
[0.3848, 0.0126]
[0.0393, 0.0071]
[0.0845, 0.0082]
[0.1465, 0.0079]
falcon3-7b
TII
83
0.178543
7
[0.1612, 0.0037]
[0.0633, 0.0108]
[0.1762, 0.0098]
[0.0885, 0.0072]
[0.3167, 0.02]
[0.1582, 0.01]
[0.2857, 0.0101]

Open Telco Leaderboard Scores

Benchmark scores for 83 models across 7 telecom-domain benchmarks, sourced from the MWC leaderboard.

This dataset publishes scores only (no energy metrics).

Files

  • leaderboard_scores.csv: Flat table for the dataset viewer.
  • leaderboard_scores.json: Structured JSON with per-model benchmark scores and standard errors.

Schema (leaderboard_scores.csv)

Core columns:

  • model — Model name
  • provider — Model provider (e.g. OpenAI, Google, Meta)
  • rank — Rank by average score (descending)
  • average — Mean of available benchmark scores
  • benchmarks_completed — Number of benchmarks with scores

One column per benchmark — each cell contains [score, stderr] as a JSON tuple, or empty if not evaluated:

Benchmarks:

  • teleqna — Telecom Q&A (multiple choice)
  • teletables — Table understanding
  • oranbench — O-RAN knowledge
  • srsranbench — srsRAN knowledge
  • telemath — Telecom math problems
  • telelogs — Telecom log analysis
  • three_gpp — 3GPP specification knowledge

Usage

from datasets import load_dataset

ds = load_dataset("GSMA/leaderboard", split="train")
print(ds.column_names)
print(ds[0])
Downloads last month
262

Space using GSMA/leaderboard 1