High-performance parallel linear optimization
HiGHS (highs.dev) scores 70 out of 100 on Brand Analyzer, placing it in the Developing tier among Technology brands, SaaS brands, Other brands, in the 24th percentile of 53921 Technology brands.
HiGHS is an open-source software package providing high-performance solvers for large-scale linear, mixed-integer, and quadratic programming problems. Built in C++11, it offers serial and parallel computation capabilities along with interfaces to multiple programming languages. Released under a permissive open-source license, HiGHS serves as a free alternative to commercial optimization solvers, targeting researchers, developers, data scientists, and engineers working in operations research, engineering, and computational applications requiring large-scale optimization.
| Brand Name | HiGHS |
|---|---|
| Domain | highs.dev |
| Industry | Technology, SaaS |
| Main Competitors | Gurobi (74/100), IBM CPLEX (85/100), FICO Xpress (86/100), CBC (COIN-OR) (81/100), GLPK (73/100), SCIP (69/100), Mosek (81/100) |
Moz Domain Authority 33/100 vs category average 39 / leader 100 - limited third-party links, so AI systems rarely encounter mentions of the brand.
No Wikipedia presence was found - a major gap, since Wikipedia is among the most heavily weighted sources in AI training data.
The homepage meta description reads "To provide freely available, high-performance software for solving large-scale linear, mixed-integer, and quadratic programming problems." (No meta description) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: No structured data or Open Graph tags detected. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: No robots.txt found (default: all bots allowed, but explicit file preferred) - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Social footprint: verified profiles on LinkedIn, GitHub, X (Twitter), Facebook, Instagram, YouTube - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked October 2026.
When asked "What is HiGHS?", Claude could identify the brand as of October 2026. HiGHS is a high-performance open-source solver for large-scale linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP) problems, written in C++. It's widely used as a free alternative to commercial solvers like Gurobi or CPLEX, and is integrated i Best known for being a fast, open-source LP/MIP optimization solver used as a free alternative to commercial solvers.
When asked "Best brands similar to HiGHS?", Claude would recommend HiGHS as of October 2026. It's a genuinely strong, widely adopted open-source solver that I'd mention when someone asks about free alternatives to commercial optimization solvers.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
HiGHS's main competitors in Technology: Gurobi (74/100), IBM CPLEX (85/100), FICO Xpress (86/100), CBC (COIN-OR) (81/100), GLPK (73/100).
Sources: Brand Analyzer scan
HiGHS offers software solvers for three main types of optimization problems: linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP). The software is built in C++11 and includes both serial and parallel processing capabilities, along with interfaces to multiple programming languages, enabling integration into various computational workflows and applications.
Sources: HiGHS official site
HiGHS is a freely available, open-source software package for solving large-scale linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP) problems. It is built in C++11 and provides high-performance serial and parallel solvers, along with interfaces to multiple programming languages, making it accessible to a wide range of users without licensing costs.
Sources: HiGHS official site
HiGHS has low AI-search visibility, scoring 22/100 on Brand Analyzer's AI visibility composite (visibility 15, trust 22, recommendation 33). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: HiGHS official site
HiGHS provides algorithms and software tools to solve large-scale optimization problems, specifically linear programming, mixed-integer programming, and quadratic programming models. It offers both serial and parallel computation options, allowing users to efficiently solve complex mathematical optimization problems commonly encountered in operations research, engineering, and computational applications.
Sources: HiGHS official site
HiGHS scores 33/100 on Brand Analyzer's AI recommendation signal, indicating it is less likely to be surfaced when AI assistants like ChatGPT suggest Technology options. Recommendation depends on crawlability, structured data, and category authority.
Sources: HiGHS official site
HiGHS is known for being a high-performance, open-source alternative to commercial optimization solvers, offering comparable performance for large-scale linear and mixed-integer programming applications. It is built in C++11 and provides multi-language interfaces, making it accessible to researchers, developers, and engineers who need robust optimization capabilities without the cost of proprietary software.
Sources: HiGHS official site
HiGHS can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: HiGHS official site
HiGHS is used by researchers, developers, data scientists, and engineers who need to solve large-scale optimization problems. Its target applications span operations research, engineering, and other computational fields where linear, mixed-integer, or quadratic programming models are required to find optimal solutions to complex problems.
Sources: HiGHS official site
HiGHS scores 22/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
HiGHS has a weak AI-visibility profile at 22/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is recommendation likelihood (33/100) and its weakest is visibility (15/100). It benefits from a Wikidata knowledge-graph entry and open access for AI crawlers (GPTBot, ClaudeBot, etc.). The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, a low Moz Domain Authority of 33/100 and thin schema.org structured data. In a live check, Claude could already identify HiGHS from memory (October 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
Ranked closest to HiGHS: HiFiCorp (70/100), ヘテムル (heteml) (70/100), Holst Centre (70/100), UOP (Honeywell) (70/100).
A step up - brands to learn from: Zypp Electric (71/100), 作业帮 (Zuoyebang) (71/100).
Category leader: Hostinger (97/100).
Brand Analyzer is built by DataEase AI. Track your brand in ChatGPT, Gemini and Perplexity.