Benchmarks / Agentic

Mercor APEX

Score: best pass@1 (loop_truncated_tools_agent). Collected by Jake Boggs' benchmark dashboard.

52models
–Open Index weight
–last updated
boggspublisher

Mercor APEX

Higher is better · best effort per model

Chart settings
52 of 52 selected

full table below

#ModelEffortScoreCost / taskHarness
1Gemini 4max82.4%––
2Sonnet 5.5max75.5%––
3Opus 5.5max73.5%––
4Fable 5.1max68.6%––
5Gemini 3.7 Flashmax67.8%––
6Opus 5max65.8%––
7Grok 4.6max65.3%––
8GPT-6 Astramax64.7%––
9Gemini 3.8 Flashmax64.3%––
10Fable 5max63.6%––
11Qwen 3.8 Maxmax63.3%––
12GPT-6.1 Solmax60.0%––
13MiMo V2.6 Prohigh59.5%––
14Muse Spark 1.3max58.6%––
15GPT-5.6 Terramax58.2%––
16MiMo V2.6 Flashhigh57.4%––
17GLM 5.3max56.6%––
18Grok 4.5max56.2%––
19Haiku 5.5max56.1%––
20DeepSeek V4 Flashmax55.3%––
21GPT-5.5xhigh55.1%––
22Grok 4.7max54.6%––
23Sonnet 5max54.5%––
24GPT-6 Solmax54.3%––
25GLM 5.3 Flashmax52.8%––
26GPT-5.4xhigh52.4%––
27GPT-5.6 Solmax51.5%––
28Kimi K3max50.6%––
29Opus 4.7max49.2%––
30Opus 4.8max48.9%––
31Qwen 3.8 27Bmax47.5%––
32DeepSeek V4 Pro 0813max47.3%––
33Gemini 3.6 Flashxhigh46.9%––
34Opus 4.6max46.3%––
35GLM 5.2max45.2%––
36GPT-6 Lunamax44.3%––
37Sonnet 4.6max43.0%––
38GPT-5.6 Lunamax43.0%––
39GLM 5.1max40.9%––
40DeepSeek V4.1 Flashmax39.5%––
41MiniMax M3max37.7%––
42Kimi K2.7 Codemax37.6%––
43Muse Spark 1.2max36.4%––
44Gemini 3.1 Prohigh35.3%––
45Inklingmax33.8%––
46Muse Spark 1.1max31.8%––
47Gemini 3.5 Flash-Litexhigh29.3%––
48Gemini 3.5 Flashxhigh27.5%––
49Qwen 3.5none24.9%––
50Nemotron 3 Ultra 550B A55Bmax22.7%––
51DeepSeek V3.2high21.3%––
52gpt-oss-120bhigh4.4%––