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.

88
anthropic/claude-3.5-haiku
Average duration
6s
Average tokens
1074
Average cost
$0.00
100
6s
726
opper_sql_sample_01
100
6s
753
opper_sql_sample_02
100
5s
752
opper_sql_sample_03
75
6s
743
opper_sql_sample_04
100
6s
789
opper_sql_sample_05
100
7s
825
opper_sql_sample_06
100
4s
734
opper_sql_sample_07
100
5s
746
opper_sql_sample_08
100
5s
748
opper_sql_sample_09
75
5s
823
opper_sql_sample_10
100
4s
1016
opper_sql_sample_11
75
6s
1033
opper_sql_sample_12
100
5s
1072
opper_sql_sample_13
100
6s
1091
opper_sql_sample_14
75
7s
1097
opper_sql_sample_15
100
6s
1157
opper_sql_sample_16
75
5s
1113
opper_sql_sample_17
75
10s
1206
opper_sql_sample_18
50
8s
1081
opper_sql_sample_19
75
6s
1209
opper_sql_sample_20
100
5s
1275
opper_sql_sample_21
100
7s
1361
opper_sql_sample_22
100
13s
1373
opper_sql_sample_23
75
6s
1373
opper_sql_sample_24
100
6s
1344
opper_sql_sample_25
100
5s
1340
opper_sql_sample_26
50
5s
1291
opper_sql_sample_27
75
7s
1557
opper_sql_sample_28
75
5s
1329
opper_sql_sample_29
100
7s
1268
opper_sql_sample_30