The paper proposes OSCAR, a framework for verifying improvements and allocating attempts across large language models (LLMs) for optimization modeling. OSCAR uses a certified simulator to compare candidates and continues searching beyond feasibility. It achieves high accuracy and reduces costs compared to other LLMs like Codex and Claude Code.