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Benchmarks · Safety & security

HoneyBench v0.2

Does the model cheat when it can? Tasks with clear rules and a flawed scorer, such as dosing simulated patients, grid control and ML repair. The share of scored runs whose final submission exploits the flaw. Lower is better.

22 models · 22 results · data as of 2026-11-12 · goodhartlabs.com ↗

Leaderboard

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22 of 22 selected
Fable 5.2 max

More from this benchmark

54 published measures

Breakdown

darker is better within each column · click a column to sort
Model
Fable 5.2 max80.0810.0
Opus 5.5 max80.0810.0
Sonnet 5.5 max100.010.010.0
GPT-6 Astra max100.010.010.0
Inkling 2 Small max100.010.010.0
GPT-6.1 Sol max80.0810.0
Fable 5.1 max60.0610.0
Gemini 4 Argon max100.099
Step 5 Preview high90.0910.0
Qwen 3.8 Max100.010.010.0
DeepSeek V4 Pro max100.010.010.0
Kimi K3.1 max100.010.010.0
DeepSeek V4.1 Flash 1017 max100.010.010.0
GLM 5.3 max100.010.010.0
Kimi K3 max100.010.010.0
Gemini 3.8 Flash max100.010.010.0
Muse Wonder max100.010.010.0
DeepSeek V4.1 Flash max100.010.010.0
Mistral Large 4 max100.010.010.0
Muse Spark 1.3 max100.010.010.0
Grok 4.7 max100.010.010.0
Muse Spark Open xhigh100.010.010.0

All results

22 rows
#ModelEffortHoneyBench v0.2
1Fable 5.2max11.9% ±3.2
2Opus 5.5max31.6% ±4.8
3Sonnet 5.5max41.6% ±4.6
4GPT-6 Astramax50.8% ±4.5
5Inkling 2 Smallmax52.6% ±4.9
6GPT-6.1 Solmax59.5% ±4.5
7Fable 5.1max61.8% ±4.8
8Gemini 4 Argonmax63.6% ±5.3
9Step 5 Previewhigh64.9% ±4.5
10Qwen 3.8 Maxmax66.2% ±4.3
11DeepSeek V4 Promax71.4% ±4.3
12Kimi K3.1max71.8% ±4.1
13DeepSeek V4.1 Flash 1017max76.4% ±3.9
14GLM 5.3max76.8% ±4.0
15Kimi K3max78.5% ±3.8
16Gemini 3.8 Flashmax80.5% ±3.7
17Muse Wondermax81.0% ±3.6
18DeepSeek V4.1 Flashmax81.0% ±3.6
19Mistral Large 4max83.6% ±3.4
20Muse Spark 1.3max83.6% ±3.4
21Grok 4.7max85.1% ±3.3
22Muse Spark Openxhigh85.6% ±3.3