Imagine an AI that could not only understand complex business problems but also find the perfect solution, optimizing everything from supply chains to marketing campaigns. That's the promise of optimization modeling, a field where algorithms find the best way to allocate resources under specific constraints. Large language models (LLMs) like GPT-4 have shown impressive mathematical skills, but how do they handle real-world optimization tasks? A new benchmark called OptiBench aims to find out. Researchers have created a challenging set of optimization problems, including linear and nonlinear programming with or without tabular data, pushing LLMs to their limits. These aren't just theoretical equations; they represent scenarios like maximizing metal extraction from ores while minimizing pollution or designing the most profitable product lineup while considering environmental impact. OptiBench requires LLMs to go beyond just formulating the problem; they must generate Python code that calls a solver to find the optimal solution. This end-to-end approach tests the LLM's understanding of the problem, its coding abilities, and its capacity to interact with external tools. Initial results are promising but reveal a significant gap between cutting-edge models and smaller, open-source alternatives. While GPT-4 performs well, smaller models struggle, especially with nonlinear problems or those involving tabular data. To address this performance gap and the limited availability of training data, researchers have developed a novel method called ReSocratic. This technique synthesizes realistic optimization problems by starting with the solution and working backward to create the corresponding question. It's like reversing the Socratic method of questioning, incrementally building the problem through a series of logical steps. This approach, implemented with an open-source LLM DeepSeek-V2, has generated a dataset of 29,000 synthetic optimization problems. When smaller models are fine-tuned on this data, their performance improves significantly, narrowing the gap with the leading models. OptiBench and ReSocratic offer a glimpse into how LLMs can tackle real-world optimization tasks. The benchmark provides a robust measure of LLM capabilities, while the synthetic data generation technique offers a path towards creating more powerful and accessible optimization tools. The challenge now is to make LLMs even better at optimization, possibly by integrating more advanced solvers or developing better training methods. If successful, AI-powered optimization could transform industries by providing solutions to complex problems faster and more efficiently than ever before.