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.

84
mistral/magistral-medium-2506-eu
Average duration
12s
Average tokens
977
Average cost
$0.00
100
4s
662
opper_sql_sample_01
100
4s
665
opper_sql_sample_02
100
40s
673
opper_sql_sample_03
100
4s
662
opper_sql_sample_04
100
7s
748
opper_sql_sample_05
100
13s
720
opper_sql_sample_06
100
3s
636
opper_sql_sample_07
100
1m 4s
657
opper_sql_sample_08
100
3s
665
opper_sql_sample_09
100
36s
697
opper_sql_sample_10
100
10s
937
opper_sql_sample_11
100
3s
908
opper_sql_sample_12
100
4s
952
opper_sql_sample_13
100
5s
963
opper_sql_sample_14
75
5s
983
opper_sql_sample_15
100
4s
1014
opper_sql_sample_16
100
5s
980
opper_sql_sample_17
75
4s
1052
opper_sql_sample_18
0
3s
845
opper_sql_sample_19
75
5s
1089
opper_sql_sample_20
100
3s
1110
opper_sql_sample_21
100
4s
1189
opper_sql_sample_22
100
10s
1826
opper_sql_sample_23
50
5s
1183
opper_sql_sample_24
75
5s
1277
opper_sql_sample_25
75
4s
1200
opper_sql_sample_26
0
14s
1424
opper_sql_sample_27
100
39s
1356
opper_sql_sample_28
0
35s
1153
opper_sql_sample_29
100
3s
1084
opper_sql_sample_30