TelcoAgent-Bench - Intent Recognition (Arabic): leaderboard
Metric: Intent recognition accuracy: mean cosine similarity x100 (-100 to 100, sentence embeddings) between the agent's free-text intent and the gold intent label on TelcoAgent-Bench (15 telecom troubleshooting intents, 49 blueprints with 30 sampled dialogues each; the agent starts from the engineer's first message and calls core and distractor network tools), Arabic dialogues, without the intent list; higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 8 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Qwen 3 8B | 74.2 |
| 2 | Granite 3.3 8B Instruct | 72.1 |
| 3 | Mistral 7B Instruct | 71.2 |
| 4 | Gemma 3 4B (IT) | 70.9 |
| 5 | Llama 3 8B Instruct | 70.7 |
| 6 | granite-3.1-3B-a800m-instruct | 70.3 |
| 7 | Qwen 2.5 7B Instruct | 70 |
| 8 | Qwen-7B Chat | 67.7 |
Interactive version: theaggregate.ai/benchmark?slug=telcoagent-bench-intent-recognition-arabic · How It Works · Data refreshed daily, snapshot 2026-10-07.