AGORA: leaderboard
Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and non
Metric: Overall (self-reported). Source: benchmarklist.com. Saturation forecast: Around September 2027. 8 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3.1 Pro (Preview) | 59.39 | #58 |
| 2 | GPT-5.5 | 54.7 | #31 |
| 3 | GLM-5.1 | 50 | #97 |
| 4 | DeepSeek V4 Pro | 45.86 | #102 |
| 5 | DeepSeek V4 Flash | 40.06 | #137 |
| 6 | Qwen 3.5 35B A3B | 11.33 | #256 |
| 7 | Gemini 3.1 Flash Lite | 6.35 | #191 |
| 8 | Qwen 3.5 9B | 3.04 | #379 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=agora · How It Works · Data refreshed daily, snapshot 2026-10-20.