FrontierOR: leaderboard
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world
Metric: Sol. quality (self-reported). Source: benchmarklist.com. Saturation forecast: Around June 2027. 7 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3.1 Pro (Preview) | 52 | #58 |
| 2 | Claude Opus 4.6 | 48 | #63 |
| 3 | GPT-5.3 Codex | 48 | #69 |
| 4 | DeepSeek R1 | 31 | #251 |
| 5 | Grok 4.20 | 22 | #175 |
| 6 | Qwen 3 Coder Plus | 20 | #338 |
| 7 | Llama 4 Maverick | 13 | #464 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=frontieror · How It Works · Data refreshed daily, snapshot 2026-10-20.