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.

93
mistral/mistral-medium-2508-eu
Average duration
5s
Average tokens
1000
Average cost
$0.00
100
3s
637
opper_sql_sample_01
100
3s
671
opper_sql_sample_02
100
4s
675
opper_sql_sample_03
100
4s
668
opper_sql_sample_04
100
4s
729
opper_sql_sample_05
100
8s
806
opper_sql_sample_06
100
4s
647
opper_sql_sample_07
100
3s
680
opper_sql_sample_08
100
3s
682
opper_sql_sample_09
100
6s
716
opper_sql_sample_10
100
4s
896
opper_sql_sample_11
100
5s
950
opper_sql_sample_12
100
5s
979
opper_sql_sample_13
100
5s
972
opper_sql_sample_14
100
4s
974
opper_sql_sample_15
100
7s
1029
opper_sql_sample_16
75
7s
1049
opper_sql_sample_17
75
7s
1289
opper_sql_sample_18
50
6s
1033
opper_sql_sample_19
100
5s
1114
opper_sql_sample_20
100
3s
1116
opper_sql_sample_21
100
6s
1267
opper_sql_sample_22
75
6s
1349
opper_sql_sample_23
75
7s
1278
opper_sql_sample_24
75
8s
1301
opper_sql_sample_25
100
4s
1242
opper_sql_sample_26
100
4s
1218
opper_sql_sample_27
75
5s
1311
opper_sql_sample_28
75
12s
1615
opper_sql_sample_29
100
3s
1120
opper_sql_sample_30