Turning a real world decision problem into a solver ready optimization model can take days—sometimes weeks—even for experienced teams. The hardest part is often not solving the problem; it’s translating business intent into precise mathematical objectives, constraints, and variables.

OptiMind is designed to try and remove that bottleneck. This optimization‑aware language model translates natural‑language problem descriptions into solver‑ready mathematical formulations, can help organizations move from ideas to decisions faster.

Now available through public preview as an experimental model through Microsoft Foundry, OptiMind targets one of the more expertise‑intensive steps in modern optimization workflows.

Addressing the Optimization Bottleneck

Mathematical optimization underpins many enterprise‑critical decisions—from designing supply chains and scheduling workforces to structuring financial portfolios and deploying networks. While today’s solvers can handle enormous and complex problem instances, formulating those problems remains a major obstacle.

Defining objectives, constraints, and decision variables is an expertise‑driven process that often takes days or weeks, even when the underlying business problem is well understood.

OptiMind tries to address this gap by automating and accelerating formulation. Developed by Microsoft Research, OptiMind transforms what was once a slow, error‑prone modeling task into a streamlined, repeatable step—freeing teams to focus on decision quality rather than syntax.

What makes OptiMind different?

OptiMind is not just as a language model, but as a specialized system built for real-world optimization tasks. Unlike general-purpose large language models adapted for optimization through prompting, OptiMind is purpose-built for mixed integer linear programming (MILP), and its design reflects this singular focus.

At inference time, OptiMind follows a multi‑stage process:

1. Problem classification (e.g., scheduling, routing, network design)
2. Hint retrieval tailored to the identified problem class
3. Solution generation in solver‑compatible formats such as GurobiPy
4. Optional self‑correction, where multiple candidate formulations are generated and validated

This design can improve reliability without relying on agentic orchestration or multiple large models. In internal evaluations on cleaned public benchmarks—including IndustryOR, Mamo‑Complex, and OptMATH—OptiMind demonstrated higher formulation accuracy than similarly sized open models and competitive performance relative to significantly larger systems.

OptiMind improved accuracy by approximately 10 percent over the base model. In comparison to open-source models under 32 billion parameters, OptiMind was also found to match or exceed performance benchmarks.

For more information on the model, please read the official research blog or the technical paper for OptiMind.

Practical use cases: Unlocking efficiency across domains

OptiMind is especially valuable where modeling effort—not solver capability—is the primary bottleneck. Typical use cases include:

• Supply Chain Network Design: Faster formulation of multi‑period network models and logistics flows
• Manufacturing and Workforce Scheduling: Easier capacity planning under complex operational constraints
• Logistics and Routing Optimization: Rapid modeling that captures real‑world constraints and variability
• Financial Portfolio Optimization: More efficient exploration of portfolios under regulatory and market constraints

By reducing the time and expertise required to move from problem description to validated model, OptiMind helps teams reach actionable decisions faster and with greater confidence.

Getting started

OptiMind is available today as an experimental model, and Microsoft Research welcomes feedback from practitioners and enterprise teams.

Next steps:

• Explore the research details: Read more about the model on Foundry Labs and the technical paper on arXiv
• Try the model: Access OptiMind through Microsoft Foundry
• Test sample code: Available in the OptiMind GitHub repository

Take the next step in optimization innovation with OptiMind—empowering faster, more accurate, and cost-effective problem solving for the future of decision intelligence.