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
mistral/mistral-large-eu
Average duration
8s
Average tokens
994
Average cost
$0.00
100
5s
637
opper_sql_sample_01
100
5s
667
opper_sql_sample_02
100
8s
674
opper_sql_sample_03
100
5s
657
opper_sql_sample_04
100
6s
731
opper_sql_sample_05
100
7s
824
opper_sql_sample_06
100
4s
652
opper_sql_sample_07
100
5s
666
opper_sql_sample_08
100
30s
666
opper_sql_sample_09
100
10s
720
opper_sql_sample_10
100
4s
886
opper_sql_sample_11
100
7s
938
opper_sql_sample_12
100
9s
1012
opper_sql_sample_13
100
5s
958
opper_sql_sample_14
75
7s
987
opper_sql_sample_15
100
6s
1033
opper_sql_sample_16
75
8s
1061
opper_sql_sample_17
100
10s
1191
opper_sql_sample_18
50
6s
1003
opper_sql_sample_19
100
10s
1156
opper_sql_sample_20
100
6s
1115
opper_sql_sample_21
100
7s
1204
opper_sql_sample_22
100
11s
1323
opper_sql_sample_23
75
12s
1437
opper_sql_sample_24
75
7s
1255
opper_sql_sample_25
100
9s
1302
opper_sql_sample_26
100
6s
1209
opper_sql_sample_27
75
13s
1346
opper_sql_sample_28
100
9s
1407
opper_sql_sample_29
100
6s
1107
opper_sql_sample_30