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AI model leaderboards

See top models, labs, apps, and providers on AI Gateway with token, request, and cost trends.

DeepsecBench measures how well different AI models find security vulnerabilities in application code, based on the deepsec cyber harness(opens in new tab). The report combines recall and precision into a single benchmark score.

Score vs. cost

Score versus Cost (USD) on a linear x-axis for each model configurationLower cost (usd) appears to the right. Ringed points and the solid line mark the Pareto frontier: configurations that are not dominated by another configuration with at least as high a score and no higher cost (usd), with one of those metrics strictly better.01020304050$0$50$100$150Cost (USD)Score

Score vs. total time

Score versus Total time on a linear x-axis for each model configurationLower total time appears to the right. Ringed points and the solid line mark the Pareto frontier: configurations that are not dominated by another configuration with at least as high a score and no higher total time, with one of those metrics strictly better.010203040500h2h4h6h8hTotal timeScore

Results

1
40.75
35.6%96.0%4$12.761h 09m 09s1,716.8Codex
2
39.49
34.3%99.0%1$12.371h 21m 01s1,389.0Codex
3
37.64
32.6%97.9%2$63.7049m 47s1,683.9Codex
4
37.50
32.6%93.3%6$65.256h 16m 28s2,574.9Claude Code
5
37.45
32.6%92.0%7$16.262h 02m 48s2,155.3Pi
6
35.30
30.5%96.3%3$55.983h 39m 02s2,274.9Codex
7
32.31
27.9%88.0%10$127.932h 34m 19s4,453.7Claude Code
8
30.69
26.2%98.6%1$12.1537m 26s753.6Codex
9
29.97
25.8%87.0%10$7.871h 04m 41s1,640.1Pi
10
29.21
24.9%95.5%3$35.2021m 49s973.0Codex
11
29.19
24.9%94.2%4$12.0528m 08s932.1Codex
12
28.71
24.5%93.8%4$5.5042m 18s893.5Pi
13
28.14
24.5%70.5%26$31.9647m 01s999.7Claude Code
14
26.57
22.7%81.2%13$24.593h 01m 40s3,343.4Codex
15
26.57
22.7%81.0%15$40.313h 51m 08s1,175.1Claude Code
16
24.90
21.0%94.3%3$17.9530m 52s703.0Codex
17
24.71
21.0%82.1%12$0.5647m 39s1,180.7Pi
18
21.82
18.5%80.7%11$16.222h 05m 37s1,232.1Pi
19
21.03
17.6%95.5%2$43.422h 22m 57s2,158.8Codex
20
20.97
17.6%89.6%5$0.511h 00m 36s1,423.5Codex
21
20.31
17.2%75.9%13$14.601h 29m 34s1,241.8Pi
22
19.06
15.9%95.3%2$27.811h 45m 32s2,108.7Codex
23
17.42
14.6%77.1%11$12.381h 59m 39s731.9Pi
24
16.41
13.7%74.0%13$5.941h 18m 09s1,481.0Pi
25
16.41
13.7%73.9%12$6.331h 52m 20s342.6Pi
26
16.40
13.7%73.3%12$11.041h 37m 33s909.6Pi
27
16.10
13.3%100.0%0$22.294h 02m 36s3,456.4Pi
28
15.46
12.9%78.9%8$6.222h 03m 33s383.4Pi
29
15.45
12.9%77.8%10$5.601h 24m 17s1,010.5Pi
30
15.35
12.9%66.7%16$5.061h 21m 16s1,422.5Pi
31
14.94
12.4%75.6%10$43.012h 54m 53s4,213.5Claude Code
32
13.53
11.2%89.7%3$1.7524m 56s873.5Codex
33
12.87
10.7%64.3%15$0.3521m 21s1,264.6Pi
34
12.53
10.3%92.3%2$14.9424m 05s751.4Codex
35
12.02
9.9%92.0%2$5.1513m 15s617.7Codex
36
11.98
9.9%82.8%5$5.2311m 59s572.0Codex
37
11.48
9.4%84.6%4$4.3303m 18s23.9Claude Code
38
11.46
9.4%80.0%6$10.7426m 21s2,730.7Pi
39
11.38
9.4%64.1%14$10.841h 29m 16s676.6Pi
40
10.89
9.0%64.7%12$13.2842m 23s1,703.4Pi
41
10.87
9.0%62.5%15$1.8341m 04s1,598.4Pi
42
10.37
8.6%62.5%12$11.091h 03m 38s1,498.9Pi
43
9.95
8.2%82.6%4$7.3207m 01s38.0Claude Code
44
9.82
8.2%53.8%18$51.842h 30m 44s4,727.6Pi
45
8.91
7.3%77.3%5$5.3322m 18s1,822.4Pi
46
7.81
6.4%53.3%14$3.9721m 28s904.5Pi
47
6.86
5.6%81.3%3$7.5619m 29s80.8Claude Code
48
6.82
5.6%61.9%8$10.8118m 16s78.1Claude Code
49
6.27
5.2%48.0%13$1.9311m 34s300.9Pi
50
5.79
4.7%61.1%7$6.6615m 47s442.2Claude Code
51
5.21
4.3%37.0%17$2.1810m 31s509.0Pi
52
4.69
3.9%33.3%18$5.9129m 40s912.4Claude Code
53
2.13
1.7%44.4%5$0.5801m 46s459.9Pi
54
1.06
0.9%28.6%5$0.1801m 07s406.5Pi