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.

65
mistral/mistral-tiny-eu
Average duration
4s
Average tokens
1044
Average cost
$0.00
75
3s
679
opper_sql_sample_01
75
3s
700
opper_sql_sample_02
100
3s
698
opper_sql_sample_03
50
3s
686
opper_sql_sample_04
100
3s
724
opper_sql_sample_05
100
4s
822
opper_sql_sample_06
100
3s
680
opper_sql_sample_07
100
4s
691
opper_sql_sample_08
100
3s
692
opper_sql_sample_09
50
4s
766
opper_sql_sample_10
75
3s
991
opper_sql_sample_11
75
3s
1006
opper_sql_sample_12
75
4s
1060
opper_sql_sample_13
50
4s
1065
opper_sql_sample_14
50
4s
1158
opper_sql_sample_15
100
4s
1116
opper_sql_sample_16
0
4s
1081
opper_sql_sample_17
25
5s
1214
opper_sql_sample_18
0
4s
1091
opper_sql_sample_19
50
5s
1230
opper_sql_sample_20
75
3s
1228
opper_sql_sample_21
75
4s
1343
opper_sql_sample_22
50
5s
1377
opper_sql_sample_23
50
4s
1337
opper_sql_sample_24
50
4s
1356
opper_sql_sample_25
75
4s
1330
opper_sql_sample_26
50
3s
1258
opper_sql_sample_27
25
4s
1440
opper_sql_sample_28
50
4s
1286
opper_sql_sample_29
100
3s
1223
opper_sql_sample_30