BenSyc - Binary Detection: leaderboard
BenSyc (2026) asks models whether a reply in an emotionally charged Bengali or Banglish Reddit conversation is sycophantic, that is, validating or escalating rather than offering balanced support. Scored by binary macro-F1 in percent against human labels; higher is better.
Metric: Binary sycophancy detection macro-F1 (%) (sycophantic versus non-sycophantic reply) on human-annotated Bengali and Banglish Reddit replies from Bangladesh and West Bengal communities, deterministic decoding (temperature 0), open models via Ollama; higher is better. Source: arxiv.org. Saturation forecast: Around 2029. 15 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Llama 3.3 70B | 61.8 | #1498 |
| 2 | Qwen 2.5 32B | 58.4 | #1332 |
| 3 | GPT-5.4 Mini | 57.5 | #389 |
| 4 | Qwen 2.5 7B | 56.9 | #2703 |
| 5 | Qwen 2.5 14B | 56.9 | #1785 |
| 6 | Llama 3.1 8B | 55.5 | #4861 |
| 7 | Gemma 2 27B | 55 | #2108 |
| 8 | Llama 3.2 3B | 53.5 | #7830 |
| 9 | Gemma 4 31B | 51.2 | #394 |
| 10 | Gemma 2 9B | 50.6 | #2802 |
| 11 | Mixtral 8x7B Instruct | 49.4 | #4444 |
| 12 | Mistral 7B Instruct | 43.7 | #7468 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=bensyc-binary-detection · How It Works · Data refreshed daily, snapshot 2026-11-13.