How to Benchmark Optimization Solutions the Right Way
Don’t rely on solver marketing claims. Use your own models, your own success metrics, and a proven process to find the best fit for your business.
We’ve created a step-by-step guide to help teams run transparent, fair, and repeatable evaluations. Whether you’re comparing multiple commercial solvers, exploring open-source tools, or benchmarking your current approach (e.g., manual processes, heuristics, or spreadsheets), this guide will help you:
- Define metrics that matter to your business (e.g., quality, robustness, speed)
- Design consistent, fair, and meaningful tests
- Decide when to tune or reformulate models
- Track performance in a structured and reproducible way
- Evaluate not just performance, but also support, licensing, and long-term value
It’s the same process our team uses with customers every day.
Download the Benchmarking Guide to Get Started
Download Benchmarking Guide
Why Most Public and Vendor Benchmarks Miss the Mark
Public benchmarks have historically played an important role in advancing computational optimization. However, they no longer reflect today’s best practices for evaluating real-world optimization performance.
Gurobi has made the decision to discontinue participation in public benchmarks because many of these tests no longer provide reliable or actionable insight for users and practitioners. In particular:
- Many benchmark test sets are outdated in size and complexity, making them poorly aligned with modern, production-scale optimization problems.
- Rigorous solution validation and detection of solver over‑tuning—especially as machine learning techniques are increasingly used for tuning—require significantly more time and resources than benchmark administrators can reasonably provide.
- Without this level of rigor and transparency, benchmark results can be misleading and should not be used to infer how a solver will perform on real customer models.
Vendor-published benchmarks present additional issues: they’re often designed to highlight one solver’s strengths, using selective models, tuned parameters, or undisclosed assumptions.
Without transparency into how these tests are designed or how models are formulated and tuned, such benchmarks often mislead more than they inform.
A Better Way: Benchmarking with Your Own Models
The best benchmark is the one you design. Evaluating solvers using your own models, data, and success metrics gives you the clearest picture of how a solver will perform in your environment. That’s the foundation of Gurobi’s approach.
Gurobi is optimized for a wide variety of real-world applications and has been rigorously tested on over 10,000 models sourced directly from customer use cases. These models span industries such as energy, logistics, manufacturing, and finance—ensuring that the Gurobi Optimizer performs not just in theory, but in the environments where it matters most. This focus on real customer models is one of the key ways Gurobi stands apart.
You’re in Good Company
