AMI-ME - Meeting Effectiveness Scoring (Non-Scenario Meetings): leaderboard
Metric: Segment-level Spearman correlation (-1 to 1) between the model's predicted effectiveness scores and the mean of three human annotators' scores on AMI-ME (segments of the unscripted non-scenario meetings of 130 AMI Corpus meetings), given gold topic segments and meeting objectives; G-Eval style scoring (probability-weighted scores, or the mean of five samples for reasoning models), context window 1; higher is better. Source: arxiv.org. Saturation forecast: Around July 2027. 6 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Qwen 3 32B (Non-reasoning) | 0.46 |
| 2 | DeepSeek R1 Distill Llama 70B | 0.39 |
| 3 | Qwen 3 32B | 0.32 |
| 4 | GPT-4o | 0.29 |
| 5 | Llama 3.3 70B Instruct | 0.24 |
| 6 | Gemini 2.5 Flash | 0.18 |
Interactive version: theaggregate.ai/benchmark?slug=ami-me-meeting-effectiveness-scoring-non-scenario-meetings · How It Works · Data refreshed daily, snapshot 2026-10-07.