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.

95
anthropic/claude-sonnet-4.5
Average duration
6s
Average tokens
1192
Average cost
$0.00
100
4s
836
opper_sql_sample_01
100
5s
848
opper_sql_sample_02
100
4s
860
opper_sql_sample_03
100
4s
873
opper_sql_sample_04
100
7s
908
opper_sql_sample_05
100
5s
934
opper_sql_sample_06
100
6s
839
opper_sql_sample_07
100
4s
853
opper_sql_sample_08
100
4s
855
opper_sql_sample_09
100
4s
907
opper_sql_sample_10
100
4s
1130
opper_sql_sample_11
100
5s
1161
opper_sql_sample_12
100
5s
1188
opper_sql_sample_13
100
26s
1191
opper_sql_sample_14
75
8s
1211
opper_sql_sample_15
100
5s
1245
opper_sql_sample_16
100
5s
1228
opper_sql_sample_17
75
6s
1336
opper_sql_sample_18
75
5s
1213
opper_sql_sample_19
100
6s
1352
opper_sql_sample_20
100
10s
1378
opper_sql_sample_21
100
6s
1478
opper_sql_sample_22
100
5s
1500
opper_sql_sample_23
75
6s
1485
opper_sql_sample_24
75
6s
1510
opper_sql_sample_25
100
6s
1471
opper_sql_sample_26
100
6s
1468
opper_sql_sample_27
75
5s
1551
opper_sql_sample_28
100
8s
1565
opper_sql_sample_29
100
4s
1379
opper_sql_sample_30