AgentFloor: leaderboard
Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical routing question that existing evaluations do not directly answer: which parts of an agent workflow truly require large frontier intelligence, and which can be handled by smaller models? We introduce AgentFloor, a deterministic 30-task benchmark org
Metric: TCR (self-reported). Source: benchmarklist.com. Saturation forecast: Around January 2027. 17 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5 | 59.9 | #98 |
| 2 | Ministral 3 8B | 55.2 | #687 |
| 3 | Qwen 3 32B | 48.1 | #443 |
| 4 | Qwen 3 14B | 48 | #537 |
| 5 | GPT-OSS-20B | 42.9 | #518 |
| 6 | Ministral 3 3B | 42.7 | #918 |
| 7 | Qwen 3 0.6B | 29.2 | #1459 |
| 8 | Ministral 3 14B | 18.8 | #650 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=agentfloor · How It Works · Data refreshed daily, snapshot 2026-10-20.