SQL

Natural language to SQL query generation evaluates text-to-query fidelity and schema reasoning. This task is particularly relevant for analytics chat assistants and simplified database interfaces where users need to query data using natural language. Models must understand both the intent behind the question and the structure of the underlying database schema.

94
groq/moonshotai/kimi-k2-instruct
Average duration
6s
Average tokens
864
Average cost
$0.00
100
14s
595
opper_sql_sample_01
100
4s
614
opper_sql_sample_02
100
4s
626
opper_sql_sample_03
100
3s
612
opper_sql_sample_04
100
5s
681
opper_sql_sample_05
100
4s
687
opper_sql_sample_06
100
6s
596
opper_sql_sample_07
100
18s
607
opper_sql_sample_08
100
10s
604
opper_sql_sample_09
100
4s
673
opper_sql_sample_10
100
3s
807
opper_sql_sample_11
100
4s
836
opper_sql_sample_12
100
5s
881
opper_sql_sample_13
100
3s
888
opper_sql_sample_14
100
12s
878
opper_sql_sample_15
100
5s
944
opper_sql_sample_16
100
10s
875
opper_sql_sample_17
75
10s
987
opper_sql_sample_18
100
7s
888
opper_sql_sample_19
75
10s
939
opper_sql_sample_20
100
10s
1012
opper_sql_sample_21
75
3s
1055
opper_sql_sample_22
75
4s
1117
opper_sql_sample_23
50
3s
1076
opper_sql_sample_24
100
3s
1092
opper_sql_sample_25
100
3s
1100
opper_sql_sample_26
100
4s
1058
opper_sql_sample_27
75
9s
1139
opper_sql_sample_28
100
4s
1045
opper_sql_sample_29
100
4s
1019
opper_sql_sample_30