EComAgentBench: leaderboard
As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked. Benchmarks that expose full intent upfront and grade only the final choice can neither pose this long-horizon challenge nor explain which requirement an agent missed. To addr
Metric: Acc. (self-reported). Source: benchmarklist.com. Saturation forecast: Around June 2027. 7 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Opus 4.6 | 57.1 | #63 |
| 2 | GPT-5.4 | 47 | #79 |
| 3 | Kimi K2.6 | 46.4 | #104 |
| 4 | GPT-5 | 39.6 | #98 |
| 5 | MiniMax-M2.7 | 36.6 | #258 |
| 6 | GPT-5 Mini | 31.6 | #186 |
| 7 | Qwen 3 30B A3B | 19.5 | #493 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=ecomagentbench · How It Works · Data refreshed daily, snapshot 2026-10-20.