Benchmarks / Agentic

HANDBOOK.md Agents

Uses Surge AI's first-party MCP-based enterprise policy-following agent score. Score: score. Collected by Jake Boggs' benchmark dashboard.

32models
–Open Index weight
–last updated
boggspublisher

HANDBOOK.md Agents

Higher is better · best effort per model

Chart settings
32 of 32 selected

full table below

#ModelEffortScoreCost / taskHarness
1Fable 5.1max38.8%––
2Fable 5max36.2%––
3Opus 5max32.3%––
4Grok 4.6xhigh27.3%––
5DeepSeek V4 Promax26.9%––
6GPT-5.6 Solmax23.5%––
7DeepSeek V4 Flashmax23.1%––
8Opus 4.8max21.9%––
9GPT-5.5xhigh21.5%––
10Qwen 3.8 Maxxhigh16.5%––
11Grok 4.5high15.8%––
12Muse Spark 1.3xhigh15.4%––
13GLM 5.3max15.0%––
14Muse Spark 1.1xhigh13.5%––
15Muse Spark 1.2xhigh13.1%––
16Kimi K3max11.9%––
17Gemini 3.5 Flashhigh11.2%––
18Gemini 3.7 Flashhigh10.8%––
19Gemini 3.8 Flashhigh10.8%––
20Sonnet 4.6max10.4%––
21GLM 5.3 Flashmax10.4%––
22Gemini 3.1 Prohigh10.0%––
23GLM 5.2xhigh10.0%––
24Qwen 3.7 Maxmax8.5%––
25Hy3high7.7%––
26Kimi K2.6max6.9%––
27Gemini 3.6 Flashhigh5.0%––
28Gemini 3.5 Flash-Litehigh3.1%––
29Inklingxhigh2.3%––
30Grok 4.3high1.9%––
31Nemotron 3 Ultra 550B A55Bmax1.5%––
32Inkling Smallxhigh1.2%––