GENSTRAT: leaderboard
Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior in any specific deployment is hard. Existing strategic-reasoning benchmarks evaluate models on fixed canonical games. These benchmarks may saturate as the frontier improves, and they do not allow evaluators to generalize with confidence from benchma
Metric: Alpha (chips/game) (self-reported). Source: benchmarklist.com. Saturation forecast: Around August 2027. 9 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5 (High) | 85 | #98 (GPT-5) |
| 2 | Gemini 3.1 Pro (Preview) | 83 | #58 |
| 3 | Claude Sonnet 4.6 (Max) | 64 | #90 (Claude Sonnet 4.6) |
| 4 | Gemini 2.5 Pro | 37 | #152 |
| 5 | Gemma 4 31B (IT) | 25 | #176 |
| 6 | Gemini 3.1 Flash Lite (Preview) | -27 | #194 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=genstrat · How It Works · Data refreshed daily, snapshot 2026-10-20.