BenSyc: leaderboard
Metric: Five-class conversational alignment classification macro-F1 (%) (invalidation, neutral, support, validation, escalation) on human-annotated Bengali and Banglish Reddit replies from Bangladesh and West Bengal communities, deterministic decoding (temperature 0), open models via Ollama; the paper ranks the leaderboard by this score; higher is better. Source: arxiv.org. Saturation forecast: Around December 2026. 15 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Gemma 4 31B | 61.7 |
| 2 | GPT-5.4 Mini | 57.2 |
| 3 | Qwen 2.5 32B | 55.4 |
| 4 | Llama 3.3 70B | 54.3 |
| 5 | Qwen 2.5 14B | 44.2 |
| 6 | Llama 3.1 8B | 41.2 |
| 7 | Gemma 2 9B | 40.3 |
| 8 | Qwen 2.5 7B | 38.2 |
| 9 | Gemma 2 27B | 38.2 |
| 10 | Mixtral 8x7B | 33.2 |
| 11 | Mistral 7B Instruct | 30.5 |
| 12 | Llama 3.2 3B | 21.3 |
Interactive version: theaggregate.ai/benchmark?slug=bensyc · How It Works · Data refreshed daily, snapshot 2026-09-29.