CPLEX vs. Hexaly
Compare a traditional enterprise MIP solver with the next-generation MIP solver. See how Hexaly performs on challenging optimization problems such as routing, scheduling, and packing — in terms of scalability, modeling expressiveness, and performance.
CPLEX VS. Hexaly
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CPLEX vs. Hexaly
What is the difference between Hexaly and CPLEX?
CPLEX is a widely used Mixed-Integer Programming (MIP) solver and represents the traditional modeling and solving approach in mathematical optimization.
Hexaly is a next-generation MIP solver that extends this approach. It enables more natural modeling of combinatorial and nonlinear problems using richer constructs, while delivering scalable performance on problems that are difficult to express or solve with traditional MIP formulations.
Is the Hexaly suite of products similar to CPLEX’s?
IBM offers a broad suite of products for mathematical optimization.
Hexaly is built around a next-generation MIP solver and goes further by providing an integrated platform to model, develop, and deploy optimization applications.
In addition to Hexaly Optimizer, tools such as Hexaly Studio, Modeler, and Cloud enable rapid prototyping and seamless deployment, helping teams move from idea to production in weeks rather than months.
Do I have to linearize my problem when modeling with Hexaly, as I do with CPLEX?
No, you do not need to linearize your model with Hexaly.
While traditional MIP workflows rely on linear formulations, Hexaly extends this approach with native support for nonlinear and combinatorial modeling. Problems can be expressed directly using standard mathematical operators such as min, max, logical expressions, and rich nonlinear functions, without complex reformulations.
For discrete problems, Hexaly provides high-level constructs such as set, list, and interval variables, enabling more compact and natural models compared to classical Boolean or integer formulations.
Hexaly also supports black-box optimization, allowing simulation or machine learning components to be integrated directly into optimization models. It natively supports lexicographic multi-objective optimization.
Does Hexaly outperform CPLEX on all types of optimization problems?
Hexaly is competitive with CPLEX on classical MILP and MIQP models.
However, when problems are reformulated using richer modeling constructs, Hexaly can deliver orders-of-magnitude reductions in computation time by avoiding complex linearizations and leveraging more natural problem structures.
More broadly, Hexaly focuses on classes of problems where traditional MIP approaches become difficult to model or scale, such as large-scale routing, scheduling, packing, network design, and workforce optimization.
To provide a transparent view, we share benchmark results on selected problem families of practical interest, based on publicly available instances and well-studied formulations. Additional benchmarks across other domains will be published progressively.
Can I provide my CPLEX MILP model, like a LP or MPS file, to Hexaly?
No, Hexaly does not directly accept LP or MPS models from CPLEX.
Traditional MIP workflows are based on linear formulations. Hexaly takes a different approach by providing a higher-level modeling framework designed for combinatorial and nonlinear problems. To benefit from its performance and scalability, models should be expressed using Hexaly’s native constructs rather than translated from existing MILP formulations.
This typically leads to more compact models and can significantly reduce solution times, especially when avoiding complex linearizations.
As a result, Hexaly is not compatible with standard modeling layers such as Pyomo, PuLP, AMPL, GAMS, or AIMMS. Instead, it provides intuitive modeling APIs in Python, Java, C#, and C++, designed to build optimization models more naturally and integrate easily into modern software environments.
How does Hexaly’s technical support compare to CPLEX’s?
CPLEX provides enterprise-grade support through structured support channels.
At Hexaly, support is delivered directly by the scientists and engineers who design the solver. Beyond technical expertise, they work closely with real-world optimization problems and help users model and solve them effectively.
Support includes assistance with modeling, answering technical questions, and reviewing models to ensure they follow best practices and achieve high performance.
Is Hexaly’s licensing and pricing model similar to CPLEX’s?
No, Hexaly’s licensing and pricing model is different.
While enterprise optimization solutions such as CPLEX often rely on usage-based, token-based, or resource-based pricing models, Hexaly offers a simple unlimited license with a flat fee, available on an annual or quarterly basis.
This removes restrictions on model size, usage, and deployment, and enables organizations to scale optimization across teams, applications, and environments without pricing constraints.
Hexaly also provides discounted pricing for startups and SMEs, and offers free access to students and academic users. For more details, see our pricing page or contact us.
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Testimonials
Are you in need of a powerful optimization solver to tackle complex problems like VRP, TSP, scheduling, and packing? Look no further than Hexaly! I’ve been using it daily, and it’s been an absolute game-changer. It’s not just lightning-fast and user-friendly—it’s incredibly flexible and can easily adapt to your specific needs. With its intuitive Studio, you can design use cases in under 20 lines of code. If you're searching for the fastest, most efficient optimization solver on the market, I wholeheartedly recommend Hexaly. Try it out and experience the results for yourself!
Building a bridge between Model-Based Systems Engineering (MBSE) techniques and Operations Research demanded an optimization solver that could seamlessly combine ease of use with powerful performance. Hexaly Optimizer exceeded all expectations. Its intuitive and elegant mathematical formalism, coupled with lightning-fast, transparent algorithms, provided us with the ideal platform for designing optimized system architectures that met multi-perspective constraints. What truly stands out is how Hexaly's team guided us through the implementation of complex constraints, ensuring everything was tailored to our specific needs. I am truly grateful for their expertise and unwavering support throughout the project, making Hexaly Optimizer an essential tool in our success.
Hexaly revolutionizes the way we complete our patient transport missions, combining efficiency and precision to deliver exceptional schedules. Not only does it save us invaluable time—allowing us to focus on high-impact tasks—but it also enhances our tactical reasoning and medium-term resource planning. With Hexaly, we’re equipped to make smarter decisions and achieve greater results.
Preparing Fret SNCF train drivers’ schedules is an intricate challenge, balancing numerous operational and regulatory constraints. Tackling such a highly combinatorial problem required a solver capable of mastering this complexity while delivering high-quality solutions quickly. Hexaly Optimizer proved to be the best tool on the market for the job. It empowers us to generate optimized schedules annually and adapt them seamlessly to evolving requirements, ensuring efficiency and compliance in an ever-changing environment.
We developed the Pasco supply chain optimization model using Hexaly in just a few days, and the results have exceeded all expectations. Hexaly Optimizer delivered outstanding solutions, as praised by Pasco planners, in only a few minutes of run time—even with tens of millions of variables involved. Initially, we could not have imagined solving such a complex problem so efficiently, especially since industry-standard MILP solvers like CPLEX, Xpress, and Gurobi struggled to handle the problem in hours. Thanks to Hexaly's incredible speed and scalability, we now know that even the most challenging optimization tasks are within reach. The performance and capabilities of Hexaly Optimizer have truly transformed our approach to supply chain optimization.
I have worked with Hexaly on several large-scale optimization projects in highly dynamic environments, and it has consistently proven to be instrumental in delivering high-quality solutions within minutes, meeting the critical need for dynamic re-planning. The unique expressiveness of Hexaly’s modeling API, combined with the highly knowledgeable support team, makes Hexaly my go-to choice for tackling complex optimization problems. Their ability to adapt and respond quickly has been invaluable in ensuring our success across various challenging projects.
The speed, scalability, and unique nonlinear modeling features of Hexaly have enabled us to tackle a variety of problems that MILP solvers like Gurobi and Cplex couldn’t effectively solve. Hexaly Optimizer’s performance has far exceeded our expectations, especially in areas such as traveling salesman (TSP), vehicle routing (VRP), assignment and matching, and facility location problems. With Hexaly, we successfully optimized the entire supply chain network for our customer Argel, achieving an impressive 12% reduction in OPEX. The thorough and responsive support from Nikolas, Senior Optimization Scientist at Hexaly, has been invaluable in helping us make rapid progress on our projects. We are extremely impressed by Hexaly’s capabilities and the substantial impact it has had on our business.
Solving our complex routing problem became a breeze with Hexaly. Its high-level declarative language made tackling multiple constraints intuitive and straightforward. The lightning-fast performance of Hexaly Optimizer didn’t just speed up solving times—it unlocked the ability to run powerful what-if analyses, giving us deeper insights for fleet management decisions. Even the unpredictable, stochastic nature of the problem was no match, as we could simulate hundreds of scenarios in just minutes. Hexaly turned a challenging task into an opportunity for smarter, faster, and more confident decision-making.
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