Best Optimization and Solver Software: 2026 Comparison

Optimization software finds the best decision under constraints, and it comes in three layers: optimization solver engines that do the math, modeling languages and environments that express the problem, and spreadsheet front ends that put both inside Excel. Solver engines include Gurobi, IBM CPLEX, FICO Xpress, HiGHS, and Google OR-Tools. Modeling layers include AMPL, GAMS, AIMMS, Pyomo, JuMP, and Analytica Optimizer, while Excel Solver, Analytic Solver, and What’sBest! make up the spreadsheet class.

Analysts rarely start by choosing a solver. A least-cost plan, a schedule, or an allocation must become decision variables, an objective, and constraints before any engine can run, and the way a team writes that formulation decides how easily others can review, change, and reuse the model. Optimization sits next to decision analysis software, which compares a short list of options, whereas optimization searches a space of options too large to list by hand.

This comparison serves power and utility planners and operations analysts first, then supply chain, finance, water, and academic research teams. It reviews fourteen products across three classes, plus three more solvers in one closing paragraph, and it uses optimization in the mathematical programming sense only, so website, PC, and machine learning tuning tools fall outside it.

This review checks every feature and price against public vendor pages in the review month, and every price is a starting point for a vendor quote.

Optimization software compared: features and pricing

The comparison splits into two tables, one for problem types and interfaces and one for free versions and published prices, since vendors update the two on different schedules.

Feature matrix

The table below treats each product as linear programming software and beyond, listing its maker, layer, and interface alongside six capability columns, with a check mark where current vendor documentation confirms the capability. Free, academic, and open-source license terms appear in the pricing table that follows.

The columns marked not documented reflect current public documentation as of October 2026 rather than a confirmed absence, and “via solver” means the modeling layer passes the problem class to whichever installed engine supports it. Solver engines handle uncertainty through the modeling layer above them, which formulates the stochastic model as a larger deterministic one.

Editions and pricing

The second table covers free versions and current pricing for the same products.

Prices reflect public vendor pages as of October 2026. Where a vendor lists a range or a monthly rate, the table shows the vendor’s own figures, and the profiles below give the terms behind each one.

How these tools were evaluated

This review assessed each product against five criteria drawn from its own public documentation rather than from marketing claims: the layer it occupies, the problem classes it solves, the interface it offers, its license and price terms, and its documented size limits. Where current vendor documentation did not confirm a capability, this article states that fact rather than asserting an absence. The review makes no speed claims, because independent public benchmarks no longer compare the largest commercial engines.

Selection guide: matching the tool to the optimization problem

Teams that face a crowded field of optimization tools usually ask which one to try first, and the table below matches seven common problem shapes to the products that fit them.

Whichever row fits, the same order of decisions applies: pick how to write the problem first, then pick an engine that fits the problem class and size.

Core capabilities in optimization software

The subsections below define the vocabulary this article uses, independent of any vendor’s terminology.

What mathematical optimization is

Mathematical optimization finds the values of decision variables that minimize or maximize an objective while satisfying constraints. An optimization problem has three elements: the decision variables a team controls, the objective that scores a solution, and the constraints that limit it.

Solver, modeling language, and modeling environment

A solver is the engine that searches for an optimal solution, a modeling language is a notation for writing the problem, and a modeling environment adds a visual or application layer around that notation. The NEOS Guide to Optimization draws the same line between solver software and modeling software. The layers combine freely, so you can run a model written in AMPL or Pyomo on Gurobi, HiGHS, or another engine.

Linear programming and LP solvers

Linear programming optimizes a linear objective subject to linear constraints, and solvers typically use simplex or barrier (interior-point) methods. LP is the most mature problem class, and a free linear programming solver such as HiGHS covers small and mid-size models well.

Mixed-integer programming and branch-and-bound

Mixed-integer linear programming (MILP) adds integer or binary variables to a linear program, which lets a model express yes-or-no choices such as whether to start a generator. A MILP solver, also called a MIP solver or mixed integer programming solver, explores those choices with branch-and-bound, cutting planes, and presolve. Solve time varies sharply with model structure, so an integer linear programming solver may need hours on a poorly formulated model.

Quadratic and conic programming

Quadratic programming allows a quadratic objective, such as portfolio variance, and conic programming generalizes it with second-order, exponential, power, and semidefinite cones. Convex versions solve reliably, and these classes appear in portfolio construction and in conic relaxations of power flow.

Nonlinear programming: local versus global

Nonlinear programming handles nonlinear objectives or constraints, and a local solver returns a solution optimal only near its starting point. A global solver searches the entire feasible region and can prove no better solution exists, usually at a higher cost. Knitro, Xpress, and Excel Solver’s GRG Nonlinear method offer nonlinear solvers, and the right choice depends on whether the model is convex.

Constraint programming

Constraint programming models a problem with logical and combinatorial rules, such as no two jobs on one machine at once, and a constraint programming solver searches by propagating those rules. Google OR-Tools CP-SAT and IBM CP Optimizer are two examples, and they often suit scheduling and assignment problems.

Optimization under uncertainty: stochastic and robust

Stochastic optimization makes decisions when some inputs follow probability distributions, while robust optimization protects a decision against the worst case within a set of plausible inputs. Both enlarge the model or multiply the number of solves, and many tools pair sampling with a search, as the comparison of Monte Carlo simulation software describes. Analytica Optimizer, Analytic Solver with Simulation, and What’sBest! with its Stochastic add-on document stochastic capability.

Formulation, presolve, and solve time

A formulation is the choice of variables, constraints, and objective that represent a problem, and presolve is the solver step that simplifies the model before the search begins. Two formulations of the same problem can differ by orders of magnitude in solve time, so formulation quality often matters more than which top-tier engine runs it. Sensitivity analysis software then helps a team check how an optimal solution changes when inputs shift.

Reading independent benchmarks

Independent benchmarks run solvers on shared test sets, and Mittelmann’s independent solver benchmarks at Arizona State University are the best-known public reference. The site states that IBM and FICO demanded the removal of their results, that Gurobi withdrew in August 2024, and that MindOpt followed on December 24, 2024. The benchmarks therefore no longer compare the three largest commercial MIP solvers, so vendor speed claims for CPLEX, Gurobi, and Xpress call for tests on a team’s own models.

Licensing models

Optimization products ship under commercial, academic, open-source, cloud, and token-based floating licenses. Commercial engines usually sell by quote, many vendors give degree-granting institutions a free academic license, and open-source engines such as HiGHS and SCIP cost nothing.

Metaheuristics and simulation-optimization

Metaheuristics such as genetic algorithms search for good solutions without proving optimality, and simulation-optimization searches over simulation model settings. They serve as a fallback when a problem resists mathematical programming, and OptQuest and the Evolutionary Solver engine belong here.

Solver engines

The five products in this class are optimization solver engines that do the math; each takes a problem from a modeling layer or an API and returns a solution.

Gurobi Optimizer

Gurobi Optimization develops Gurobi Optimizer, a commercial solver that its product page lists as handling linear programming (LP), mixed integer programming (MIP), quadratic programming (QP), combinatorial, nonlinear, conic, and multi-objective problems. Developers reach it through APIs, including a widely used Python API, and its global optimization for non-convex quadratic problems can prove a solution is the global optimum.

Best for: teams that run large MIP or quadratic models inside production code and can budget for an annual subscription.

Key strengths:

• One engine covers LP, MIP, QP, conic, nonlinear, and multi-objective problems
• A Python API places models inside applications and data pipelines
• Global optimization for non-convex quadratic problems can prove a global optimum
• Named-user, compute server, cloud, and web license service options fit laptops, servers, and containers
• A free academic license and a 30-day commercial trial support evaluation

Limitations:

• Commercial licenses are quote-based and billed annually, with no figures published
• The size-limited license that ships with gurobipy caps models at 2,000 variables and 2,000 linear constraints and excludes commercial use
• The product page lists nonlinear support without further detail as of October 2026

Price: Quote-based and billed annually, with no figures published, as of October 2026, per vendor website. Free academic license and 30-day trial.

Verdict: Gurobi fits teams that embed large mixed-integer or quadratic models in applications and accept subscription pricing. Buyers with nonlinear models should test that capability on their own formulations.

Website: gurobi.com

IBM CPLEX

IBM develops ILOG CPLEX Optimization Studio, a package that bundles the CPLEX solver, the CP Optimizer constraint programming engine, the Optimization Programming Language (OPL), and an IDE. The CPLEX solver runs LP, MIP, mixed-integer quadratically constrained (MIQCP), and second-order cone (SOCP) models, and CP Optimizer handles constraint-based scheduling.

Best for: teams that want mathematical programming and constraint scheduling from one IBM package, or that already maintain OPL models.

Key strengths:

• One package combines a mathematical programming solver, a constraint programming engine, and a modeling language
• OPL declares sets, arrays, and tuples with linear and nonlinear constraints
• CP Optimizer models scheduling with interval variables and cumulative functions
• The IDE supports debugging, profiling, and JavaScript scripting
• A free Community Edition runs on Windows, Linux, and macOS within its size cap

Limitations:

• The Community Edition limits models to 1,000 variables and 1,000 constraints
• The Developer Subscription serves development and does not cover large-scale deployment
• The product page lists LP, MIP, MIQCP, and SOCP and does not list nonlinear programming as of October 2026

Price: Developer Subscription from $320 per authorized user per month, as of October 2026, per vendor website. Community Edition free; deployment options by quote.

Verdict: CPLEX fits teams that combine mathematical programming with constraint scheduling under one vendor. Teams with nonlinear models should confirm support before committing.

Website: ibm.com

FICO Xpress Optimization

FICO develops FICO Xpress Optimization, a platform that pairs the Xpress solver with Mosel, an algebraic modeling language, and Insight, an environment for dashboards and scenario analysis. The solver lists LP, MIP, (MI)QP, (MI)QCQP, (MI)SOCP, constraint programming, nonlinear programming, and global MINLP problem types.

Best for: teams that want problem coverage from LP through global MINLP from one vendor, with a dashboard layer for business users.

Key strengths:

• The solver spans linear, mixed-integer, quadratic, conic, constraint programming, nonlinear, and global MINLP problems
• Mosel provides a modeling language built for Xpress
• Insight turns optimization models into dashboards for scenario analysis
• Free Community, Academic, and Evaluation licenses support testing
• AMPL and Analytica Optimizer both list Xpress as a supported engine

Limitations:

• FICO publishes no prices for commercial licenses
• The product page does not state the size limits of the Community License as of October 2026
• Mosel is FICO’s own language, so Mosel models do not run on other vendors’ solvers without rewriting

Price: Not published; commercial licensing is quote-based, as of October 2026, per vendor website.

Verdict: Xpress fits teams that need broad problem coverage and a business dashboard from one vendor. Teams that need published pricing or portable models should weigh HiGHS or a vendor-neutral modeling language.

Website: fico.com

HiGHS

HiGHS is an open-source solver for large-scale sparse linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP) models, released under the MIT license. It provides serial and parallel solvers with interfaces to C, C#, FORTRAN, Julia, and Python.

Best for: teams that need a free LP, MILP, or QP solver inside Python or Julia workflows.

Key strengths:

• The permissive MIT license places few restrictions on use
• One solver covers LP, MIP, and QP
• Serial and parallel solvers run on large sparse models
• Interfaces cover C, C#, FORTRAN, Julia, and Python
• JuMP and SciPy offer HiGHS, so it drops into common modeling workflows

Limitations:

• The homepage lists no nonlinear or conic problem types as of October 2026
• HiGHS has no modeling language of its own, so models arrive through its interfaces or a modeling layer
• Commercial support terms are not documented on the homepage as of October 2026

Price: Free under the MIT license, as of October 2026, per vendor website.

Verdict: HiGHS fits teams that want a free engine for linear, mixed-integer, and quadratic models. Teams with nonlinear or conic models should look at Knitro, MOSEK, or the commercial engines.

Website: highs.dev

Google OR-Tools

Google develops OR-Tools, an open-source software suite for vehicle routing, flows, integer and linear programming, and constraint programming. Its overview states that the CP-SAT solver has taken top honors at the international constraint programming competition every year since 2013. The suite also connects to commercial solvers such as CPLEX, Gurobi, and FICO Xpress and to open-source solvers such as SCIP, GLPK, and Google’s GLOP.

Best for: teams that solve scheduling, assignment, and routing problems with many logical constraints and want an open-source toolkit.

Key strengths:

• CP-SAT handles scheduling and combinatorial problems with logical constraints
• A vehicle routing library targets delivery and dispatch problems
• One interface connects to commercial and open-source linear and integer solvers
• C++, Python, C#, and Java interfaces fit most development stacks
• Open-source distribution carries no license fee

Limitations:

• The overview does not list nonlinear programming as a feature as of October 2026
• Constraint programming uses a different formulation style than algebraic LP and MILP
• The commercial solvers that OR-Tools wraps still require separate vendor licenses

Price: Free and open source, as of October 2026, per vendor website.

Verdict: OR-Tools fits teams with scheduling, rostering, or routing problems and a developer-led workflow. Teams that want an algebraic modeling language or nonlinear solvers should look at the modeling layers below.

Website: developers.google.com/optimization

Three more solvers target problem classes that the five above cover less directly. MOSEK solves linear, conic, semidefinite, convex quadratic, and mixed-integer problems but not nonconvex ones; Artelys Knitro targets nonlinear and mixed-integer nonlinear problems, and SCIP solves MIP and MINLP problems as a constraint integer programming framework under the Apache 2.0 license from version 8.0.3. Analytica’s page on the solver engines that plug into Analytica Optimizer lists MOSEK, Knitro, XPRESS, and Gurobi among its plug-in options.

Modeling languages and modeling environments

The six products in this class sit above the engines: they let a team write the problem in algebraic notation, code, or a visual diagram and then hand it to a solver. Their prices and licenses are separate from the engines they call.

AMPL

AMPL describes itself as an optimization platform whose purpose-built modeling language separates model logic from data and solver configuration. The AMPL MP interface connects models to commercial solvers such as Gurobi, CPLEX, Xpress, COPT, MOSEK, and Artelys Knitro, as well as open-source solvers such as HiGHS, SCIP, IPOPT, and CBC. Interfaces cover Python, C++, Java, C#, and R.

Best for: teams that maintain algebraic models across many scenarios and want a choice of solvers behind one model.

Key strengths:

• A purpose-built algebraic language keeps model logic, data, and solver settings apart
• One model connects to many commercial and open-source solvers
• Python, C++, Java, C#, and R interfaces fit programmatic workflows
• Data integrations include pandas, PostgreSQL, MySQL, and Databricks
• The vendor publishes solver add-on prices, so buyers can estimate total cost

Limitations:

• Commercial solvers cost extra, for example, CPLEX and Knitro at $400 per month each
• The free Community Edition includes open-source solvers only
• AMPL sets Gurobi and Xpress prices by need, with no figure published
• AMPL is a modeling layer, so it needs at least one solver to run

Price: AMPL starts at $300 per month, and solvers start at $105 per month, billed annually, as of October 2026, per vendor website. Free Community Edition and academic license.

Verdict: AMPL fits teams that standardize on an algebraic language and want solver choice. Teams on a tight budget should plan around the Community Edition’s open-source solvers.

Website: ampl.com

GAMS

GAMS describes its modeling system as an algebraic modeling language for optimization and equilibrium problems, covering linear, nonlinear, discrete, stochastic, and equilibrium models. GAMSPy provides a Python-native interface, and MIRO creates interactive decision applications from optimization models. GAMSPy Academic licenses include commercial solvers such as CPLEX, CONOPT, MOSEK, COPT, and PATH, and free solvers such as HiGHS, SCIP, SHOT, and IPOPT.

Best for: research and planning teams that model optimization and equilibrium problems and want a web interface for non-technical users.

Key strengths:

• An algebraic language covers optimization and equilibrium models
• GAMSPy offers a Python-native route to the same models
• MIRO turns models into interactive web applications
• The academic license bundles many commercial and free solvers
• A free demo license ships with every distribution

Limitations:

• Professional licenses are quote-based, with no figures published
• The demo license expires after five months and caps LP, RMIP, and MIP models at 2,000 variables and 2,000 constraints
• The Community license serves academic, non-commercial, non-production use and expires after 12 months

Price: Professional license by quotation, as a one-time payment plus annual maintenance or a yearly subscription, as of October 2026, per vendor website. Free demo, Community, and GAMSPy Academic licenses.

Verdict: GAMS fits academic and planning teams that need equilibrium modeling and a solver bundle. Commercial buyers should request a quotation early, because the free licenses exclude production use.

Website: gams.com

AIMMS

AIMMS describes itself as an AI-augmented decision platform for supply chain optimization and decision intelligence. Its products include SC Navigator, which offers ready-to-use supply chain design and planning applications, and Optimization Tooling, which lets users build custom applications in the AIMMS Developer environment. The overview names Gurobi and IBM ILOG CPLEX as solver integrations.

Best for: supply chain teams that want packaged planning applications plus a developer environment for custom models.

Key strengths:

• SC Navigator covers network design, production planning, sales and operations planning, and transportation planning
• Further application areas include workforce scheduling, warehouse slotting, sourcing, and carbon footprint optimization
• AIMMS Developer builds custom optimization applications
• A SaaS delivery model, with deployment inside existing infrastructure described on its IT security page
• Free Academic and Free Community licenses support learning and testing

Limitations:

• The pricing page publishes no figures or free-license terms as of October 2026
• The overview does not name formulation types such as LP or MIP
• The overview names Gurobi and CPLEX only, so open-source solver support is not documented there

Price: Not published, as of October 2026, per the vendor website; the pricing page names Free Academic and Free Community licenses without terms.

Verdict: AIMMS fits supply chain organizations that want ready-made planning applications. Power and utility teams should confirm formulation support with the vendor, because the overview centers on supply chain use cases.

Website: aimms.com

Pyomo and JuMP

Pyomo is an open-source optimization modeling language for Python, and its homepage lists linear, nonlinear, and mixed-integer programming capabilities. JuMP is a modeling language for mathematical optimization in Julia, and its homepage says it can formulate and solve linear, integer, conic, semidefinite, and constrained nonlinear programs. Both separate the model from the solver.

Best for: teams that program in Python or Julia and want free modeling layers that run on the solver they already own.

Key strengths:

• Pyomo brings LP, NLP, and MIP modeling to Python code
• JuMP covers linear, integer, conic, semidefinite, and nonlinear models in Julia
• Both are open source, so a team pays no license fee for the modeling layer
• Models live in ordinary code, so version control and testing tools apply
• HiGHS lists JuMP among the modeling systems that offer it

Limitations:

• Both require programming in Python or Julia
• Neither includes a solver, so a team installs at least one engine separately
• The Pyomo homepage names no license and no solvers as of October 2026

Price: Free and open source, as of October 2026, per vendor websites; solver licenses cost extra.

Verdict: Pyomo and JuMP fit developer-led teams that want a free modeling layer and solver choice. Teams that prefer a workflow without code should compare AMPL, GAMS, or Analytica Optimizer.

Website: pyomo.org and jump.dev

Analytica Optimizer

Lumina Decision Systems develops Analytica Optimizer, the optimization edition of Analytica, which includes all Developer edition features plus solver engines. A user builds the model as an influence diagram in which node shapes and colors distinguish decision variables, objectives, constraints, and chance variables, and Intelligent Arrays carry scenarios and time through the same formulation. Analytica Optimizer belongs to the modeling-environment class, so it expresses the problem visually and passes it to a solver rather than competing with Gurobi or CPLEX as an engine.

The edition selects the solver automatically and covers LP, MIP, QP, and NLP programs. Lumina licensed optimization and solver technology from Frontline Systems when it introduced Optimizer, according to a 2005 press release, and Analytica’s engines page still refers readers to Frontline’s solver.com, so readers should weigh that relationship alongside the Analytic Solver review below.

Best for: teams whose optimization models stakeholders or regulators must review and that combine decisions with uncertain inputs.

Key strengths:

• Influence diagram nodes show decisions, objectives, constraints, and chance variables, so reviewers read the formulation without code
• Automatic solver selection spans LP, MIP, QP, and NLP programs
• Intelligent Arrays carry scenario and time dimensions through one formulation
• Chance variables with probability distributions support stochastic optimization alongside Monte Carlo simulation
• Plug-in engines such as Gurobi and Knitro raise capacity without changing the formulation

Limitations:

• Standard engines solve LP and quadratic problems up to 8,000 variables (2,000 integer), conic problems up to 2,000 variables, and nonlinear, global, and nonsmooth problems up to 500 variables
• Plug-in engines are separate paid add-ons, for example, $10,775 for the Gurobi Solver Engine and $4,795 for KNITRO
• The free edition excludes optimization, so evaluation requires the 30-day Optimizer trial
• Analytica does not target embedded production use, and teams fluent in AMPL, GAMS, or Python modeling may prefer those layers

Price: Analytica pricing lists the Optimizer edition at $4,000 per year, with Developer at $2,000 and Professional at $1,000, as of October 2026, per vendor website. Add-on solver engines carry separate prices.

Verdict: Analytica Optimizer fits teams that need a reviewable, diagram-based optimization model with uncertainty built in. Teams that need the fastest solves on very large mixed-integer models should pair a commercial engine with a code-based modeling layer instead.

Website: analytica.com

Spreadsheet optimization tools

The three products in this class bring optimization into Excel, and the Excel Solver add-in is the baseline that the other two upgrade. This class matters most to readers who search for an Excel Solver alternative after a model outgrows the built-in limits.

Excel Solver

Microsoft ships Solver as an Excel add-in that Frontline Systems developed, and it offers the Simplex LP, GRG Nonlinear, and Evolutionary methods. Frontline’s listing for the free Solver add-in states limits of 200 decision variables and 100 constraints in addition to bounds on the variables, and the Excel for the web version caps solving time at 30 seconds.

Best for: analysts with a small model already in Excel who need a quick LP, nonlinear, or integer solution.

Key strengths:

• The Solver add-in is free and familiar to most Excel users
• Objective, variables, and constraints sit in ordinary worksheet cells
• The add-in supports bounds and integer constraints
• Three methods cover linear, nonlinear, and evolutionary search
• A free Excel for the web version brings Solver to the browser

Limitations:

• The documented limits are 200 decision variables and 100 constraints in addition to variable bounds
• The Excel for the web version caps solving time at 30 seconds
• Frontline directs users with larger or stochastic models to its enhanced Solvers

Price: Free with Excel, as of October 2026, per the Microsoft AppSource listing.

Verdict: Excel Solver fits small models that already live in a workbook. Teams that reach 200 variables or need stochastic models should look at What’sBest! from LINDO or at Analytic Solver.

Website: appsource.microsoft.com

Analytic Solver

Frontline Systems sells Analytic Solver as a tiered family of Excel upgrades, and the Optimization tier, formerly Premium Solver Platform, lists five built-in engines and eight optional plug-in engines. Frontline states that the tier solves linear models 40 times larger and nonlinear models five times larger than Excel Solver. The product runs in Excel desktop and Excel for the web.

Best for: Excel users whose models outgrew Excel Solver and who want larger models, more engines, and an upgrade path to uncertainty.

Key strengths:

• A tier ladder lets a team buy only the model size it needs
• Built-in LP/Quadratic, SOCP Barrier, Interval Global, and Evolutionary engines extend what Excel Solver covers
• Optional plug-in engines include Gurobi, Xpress, MOSEK, KNITRO, and OptQuest
• Combined with Analytic Solver Simulation, the product adds stochastic programming and robust optimization
• Models can publish to Excel Online or Google Sheets

Limitations:

• The Optimization tier lists only limited simulation trials, and Frontline lists the full simulation tiers on a separate page
• Add-on large-scale engines cost more than the base tiers, for example $1,575 to $2,025 per month for the Gurobi engine
• Another Frontline page lists the combined Optimization plus Simulation license at $5,000 for one year, so buyers should confirm which tier they need

Price: Monthly billing at $90 for Analytic Solver Excel, $180 for Excel Upgrade, $375 for Optimization, and from $1,400 for Large Scale, as of October 2026, per vendor website; annual billing saves 42 to 44 percent. A 15-day free trial is available.

Verdict: Analytic Solver fits Excel-based teams that need larger models and plug-in engines without leaving the workbook. Buyers who need uncertainty should price the Simulation tier together with Optimization.

Website: solver.com

What’sBest!

LINDO Systems develops What’sBest!, an Excel add-in whose product page lists nonlinear (convex and nonconvex or global) and integer optimization along with linear, quadratic, quadratically constrained, second-order cone, semidefinite, and stochastic models. The current release is version 20. LINDO Systems also sells LINGO, an optimization modeling product, and the LINDO API for programmers.

Best for: Excel users who want a published perpetual price list and optional add-ons for nonlinear, global, conic, or stochastic models.

Key strengths:

• One add-in covers linear, integer, nonlinear, and global optimization
• Four editions, from Commercial to Extended, carry published perpetual single-user prices
• Add-ons include Barrier, Nonlinear, Nonlinear & Global, Stochastic, and Barrier & Conic
• A 60-day money-back guarantee covers LINDO products
• A free evaluation license is available through sales

Limitations:

• Base prices exclude add-ons, for example, Nonlinear & Global at $300 to $3,000 depending on the edition
• The store lists capacity limits only in an image, so buyers must confirm sizes with LINDO
• The store lists Windows versions only
• The evaluation license caps capacity and requires contacting sales

Price: Perpetual, single-user base prices are $495 for Commercial, $995 for Professional, $2,995 for Industrial, and $4,995 for Extended as of October 2026, per the vendor website, with add-ons extra.

Verdict: What’sBest! fits Excel users who prefer a one-time purchase with clear add-on options. Buyers should confirm the capacity of each edition before choosing, because the store does not state it in the text.

Website: lindo.com

Adjacent categories worth knowing

Three neighboring categories share the word optimization without answering the same question. Simulation-optimization tools such as AnyLogic, which the review of system dynamics simulation software covers, @RISK’s RiskOptimizer, and OptQuest search over simulations rather than solve a mathematical program. Energy system and production-cost models such as PLEXOS, AURORA, and PyPSA embed solvers inside a sector-specific model, so they use the engines reviewed here rather than replace them.

Machine learning hyperparameter optimization, such as Optuna, shares the term but not the method, so this comparison excludes it.

Optimization in the power and utility sector

Six recurring problems in energy and power call for mathematical optimization, since each asks for the least-cost or most reliable decision under physical and operating constraints.

Capacity expansion and integrated resource planning compare many scenarios across decades, which suits array-based modeling layers. Lumina’s energy and utility applications page lists integrated resource planning among its demand side management topics. A published study on microgrid dispatch, Lee and colleagues in Energies, compared CPLEX and Gurobi with the open-source GLPK and CBC and found that all four reached the same minimal operating costs.

The same study reports that the modeling package, not the solver, accounted for most of the elapsed time, and that direct APIs ran much faster for CPLEX and Gurobi. The same methods apply outside utilities.

• Supply chain and manufacturing: network design, production planning, and routing
• Finance and portfolio construction: allocation under risk and cost constraints
• Water allocation: reservoir operation and supply scheduling across users
• Healthcare scheduling: staff rostering and operating room planning

Across all ten situations, the same test applies: optimization software earns its place wherever a decision must be the best one that satisfies physical, financial, or operational constraints.

Is this comparison objective?

A team that includes staff of Lumina Decision Systems, the maker of Analytica, researched and wrote this comparison, and readers should weigh that affiliation against the following safeguards.

• This article reviews Analytica Optimizer as one of fourteen products, in its natural class alongside AMPL and GAMS, rather than presenting it separately
• This article checked every competitor limitation against public vendor documentation before inclusion
• This article includes no limitation that a vendor could reasonably dispute as factually inaccurate
• Readers who find an inaccuracy in any review, including the Analytica review, can contact Lumina Decision Systems through the official contact page at lumina.com

These safeguards narrow the risk of bias but do not remove the underlying affiliation, so readers evaluating a purchase should weigh this article alongside independent sources as well. Lumina also licensed solver technology from Frontline Systems, whose Analytic Solver this article reviews, and readers should weigh that commercial relationship in the same way.

Frequently asked questions

What is optimization software?

Optimization software finds the best values for a set of decisions under constraints, a field called mathematical optimization. Every optimization problem has three elements: decision variables, an objective to minimize or maximize, and constraints that limit the choices. The software is split into solver engines, modeling languages and environments, and spreadsheet add-ins.

What is the difference between a solver and a modeling language?

A solver is the engine that searches for an optimal solution, while a modeling language is the notation a team uses to write the problem. AMPL, GAMS, Pyomo, and JuMP are modeling layers, and Gurobi, CPLEX, Xpress, and HiGHS are solvers that run what the layer produces. The pairing lets a team change engines without rewriting the model.

Is Gurobi better than CPLEX?

Neither engine wins on every problem, and a CPLEX vs Gurobi answer depends on the problem class, model size, and formulation. Independent public benchmarks cannot settle the question because Mittelmann’s site says IBM and FICO demanded the removal of their results, and Gurobi withdrew in August 2024. The reliable method is to test both on your own models, using the free academic, community, or trial licenses each vendor offers.

What is the best free or open-source optimization solver?

HiGHS covers LP, MIP, and QP under the MIT license, SCIP covers MIP and MINLP under Apache 2.0, and OR-Tools covers constraint programming and routing. Commercial engines also offer free routes: Gurobi’s academic license, the CPLEX Community Edition, and the Xpress Community License, making Gurobi alternatives easy to test. Analytica Optimizer offers a 30-day Optimizer trial because its free edition excludes optimization.

When does Excel Solver stop being enough?

Excel Solver stops at 200 decision variables and 100 constraints, plus variable bounds, and Excel for the web caps solving time at 30 seconds. A model that reaches those limits, needs more engines, or includes uncertainty calls for an Excel Solver alternative such as What’sBest! or Analytic Solver. Teams that outgrow Excel altogether can move to a modeling language and a solver engine.

Linear vs nonlinear vs mixed-integer: which do I need?

Linear programming fits problems where every relationship is proportional, nonlinear programming fits curved relationships such as power flow, and mixed-integer programming fits yes-or-no decisions such as starting a generator. Many real models combine them, which is why solvers list several problem classes. Start with the simplest class that represents the problem accurately, because simpler classes solve faster and more reliably.

How do you optimize decisions under uncertainty?

Stochastic optimization makes decisions when some inputs follow probability distributions, while robust optimization protects a decision against the worst case within a set of plausible inputs. Analytica Optimizer supports chance variables with probability distributions, Analytic Solver adds stochastic programming and robust optimization with its Simulation tier, and What’sBest! sells a Stochastic add-on.

Which optimization tools are used in the power and utility sector?

The Energies microgrid study used CPLEX, Gurobi, GLPK, and CBC for dispatch problems, and energy system models such as PLEXOS, AURORA, and PyPSA embed solvers within sector-specific models. Unit commitment and capacity expansion use MILP engines, and optimal power flow uses nonlinear or conic solvers. Current vendor documentation reviewed here does not name utility customers for the products in this comparison.

How should a team evaluate a solver before buying?

Build two or three representative models, run them on each candidate engine, and record solve time, solution quality, and ease of integration. Most vendors offer a free path to do this: a Gurobi trial, the CPLEX Community Edition, an Xpress Evaluation License, a 30-day GAMS evaluation, or a 15-day Analytic Solver trial. The NEOS Server also lets teams test models on several solvers before buying.