Key Takeaways

• KaTeX renders math ~2.7× faster than MathJax 3 and ships a bundle that is ~70% smaller, making it the default choice for performance-critical web apps.
• MathJax 3 retains a decisive edge in coverage — it handles complex packages, accessibility features, and output format flexibility that KaTeX cannot yet match.
• WebAssembly is unlocking full TeX engine execution in the browser, with projects like TeXLive Wasm enabling server-free scientific document compilation.
• 97% of Mathematics and 89% of Statistics academic submissions still rely on LaTeX, making robust browser rendering a non-negotiable requirement for modern scientific publishing platforms.
• Emerging alternatives like Typst are worth monitoring, but LaTeX remains the undisputed standard for the foreseeable future.

KaTeX vs MathJax: Choosing the Best Math Engine for Your Web App

Introduction: Why LaTeX is Conquering the Web

The web was not designed for mathematics. HTML was built to structure text, and its original specification offered nothing resembling the elegant equation formatting that physicists, engineers, and mathematicians had relied upon for decades in print. Yet here we are in 2025, and LaTeX in the browser has evolved from a niche workaround into the architectural backbone of some of the world's most important scientific publishing platforms.

The numbers tell the story plainly. Overleaf — the cloud-based LaTeX editor — now serves over 20 million users and has facilitated the creation of more than 100 million documents globally. Academic submission pipelines at leading journals depend on LaTeX syntax as their lingua franca. And with the explosion of e-learning platforms, interactive textbooks, and AI-assisted research tools, the demand for high-fidelity math rendering on the web has never been more acute.

For developers tasked with building these systems, the decision of which math rendering engine to integrate is far from trivial. It touches performance budgets, accessibility compliance, SEO strategy, security architecture, and long-term maintainability. This guide cuts through the noise and provides a structured, research-backed framework for making that decision confidently.

The Heavyweights: Comparing KaTeX and MathJax 3

When developers discuss math rendering for the web, two names dominate every serious conversation: KaTeX by Khan Academy and MathJax by the MathJax Consortium. Both are open-source, both are actively maintained, and both can render LaTeX syntax into browser-displayable output — but their design philosophies diverge significantly.

KaTeX was built with one priority above all others: speed. Khan Academy developed it to serve millions of students with math-heavy content without degrading page performance. It renders synchronously, produces static HTML and CSS output, and deliberately limits its scope to the most commonly used LaTeX commands.

MathJax 3, by contrast, was engineered for completeness. It supports an extensive command set, multiple input formats (LaTeX, AsciiMath, MathML), and multiple output formats (HTML-CSS, SVG, CommonHTML). Its architecture is modular, allowing developers to load only the components they need, though its baseline footprint remains substantially larger than KaTeX.

The practical implication is this: if your application renders student-facing exercises, documentation portals, or any content where Core Web Vitals matter, KaTeX is likely your first instinct — and often the right one. If you are building a full-featured scientific journal platform, a LaTeX document editor, or a system that must handle arbitrary user-submitted LaTeX with complex package dependencies, MathJax 3's breadth becomes essential.

Performance Deep Dive: Bundle Sizes and Rendering Speed

Performance is where the KaTeX vs MathJax debate becomes most concrete. The data here is unambiguous.

KaTeX is approximately 70% smaller in gzipped bundle size compared to MathJax 3, making it the pragmatic choice for mobile-first web performance optimization. (Source: Hacker News Developer Discussion)

MathJax 3 is roughly 2.7 times slower than KaTeX in raw processing speed — though it remains significantly faster than its own version 2 predecessor. (Source: MathJax Project Team)

For developers working within tight Lighthouse score targets or serving users on constrained mobile networks, a 70% reduction in bundle size translates directly into measurable improvements in First Contentful Paint (FCP) and Time to Interactive (TTI). KaTeX's synchronous rendering model also eliminates the layout shift artifacts that asynchronous renderers can introduce — a meaningful win for Cumulative Layout Shift (CLS) scores.

That said, MathJax 3's modular build system allows experienced developers to create custom component bundles that dramatically reduce its footprint for specific use cases. A deployment that only needs LaTeX input and SVG output, for instance, can shed significant weight compared to the full distribution. The 2.7× speed differential narrows when comparing optimized custom builds, though it does not fully disappear.

The Wasm Revolution: Running Full TeX Engines in the Browser

Perhaps the most transformative development in LaTeX in the browser is the maturation of WebAssembly (Wasm) as a vehicle for running full TeX compilation pipelines client-side.

Projects like TeXLive Wasm (and the underlying

texlive.js

swiftlatex

.tex

The implications for scientific publishing are profound:

• Privacy-preserving compilation: Source documents never leave the user's device.
• Offline capability: Progressive Web Apps can compile LaTeX documents without internet access.
• Reduced server costs: Compute shifts from expensive cloud infrastructure to the client.
• Latency elimination: No network round-trips for compilation feedback loops.

The current limitations are real but shrinking. Full TeXLive Wasm bundles are large (often several hundred megabytes uncompressed), and initialization times on first load can be significant. Clever strategies — lazy-loading package subsets, using service workers for caching, and streaming Wasm compilation — are actively narrowing these gaps. For teams building next-generation web-based TeX editors, Wasm-native compilation is no longer a research curiosity; it is a production-viable architecture.

Beyond Equations: Handling TikZ and Complex Packages on the Web

Most math rendering discussions fixate on inline and display equations. But the LaTeX ecosystem extends far beyond

\frac

\sum

For these scenarios, developers must choose between several architectural strategies:

1. Server-side compilation: Submit LaTeX source to a backend that runs pdfLaTeX or LuaLaTeX, returning a rendered image or SVG. Reliable but introduces latency and server cost.
2. Wasm-based compilation: Use a browser-compiled TeX engine to generate PDF output, then render it via a PDF.js viewer or convert to SVG. Eliminates server dependency at the cost of client compute.
3. Selective substitution: Replace complex packages with JavaScript-native equivalents (e.g., D3.js for plots, Mermaid for diagrams) and reserve server rendering for cases where LaTeX fidelity is non-negotiable.
4. Pre-compilation pipelines: For static sites and documentation portals, compile all LaTeX assets at build time, outputting static SVGs or images — zero runtime cost.

The right choice depends heavily on whether your use case requires dynamic, user-generated LaTeX or static, authored content. Most production platforms use a hybrid: client-side math rendering for equations (KaTeX or MathJax) combined with server or build-time pipelines for complex graphical packages.

SEO and SSR: Ensuring Your Math Content is Discoverable

Mathematical content presents a unique SEO challenge. Search engines index text; they cannot "read" equations rendered as images or parsed from complex JavaScript DOM manipulation. For platforms whose discoverability depends on Google indexing their formulas — think educational content, research summaries, or documentation — this is a genuine ranking concern.

The solution is Server-Side Rendering (SSR). Both KaTeX and MathJax support SSR workflows:

• KaTeX is particularly well-suited to SSR because its synchronous API makes it trivially embeddable in Node.js build pipelines. Frameworks like Next.js and Nuxt.js can render KaTeX output at build time or request time, delivering fully-formed HTML to crawlers.
• MathJax 3 offers

  mathjax-node

  and similar server-side packages, though the async architecture requires more careful orchestration.

The additional benefit of SSR for math is eliminating the flash of unrendered content (FOUC) that purely client-side renderers produce — where users briefly see raw LaTeX source before it is processed. For scientific publishing platforms where credibility is paramount, this visual polish matters as much as the SEO benefit.

For scientific web publishing at scale, the recommended stack is SSR-rendered KaTeX for equations combined with structured data markup (Schema.org

MathSolver

LearningResource

Accessibility in STEM: Making Formulas Screen-Reader Friendly

Accessible math on the web is not merely a compliance checkbox — it is an ethical imperative for platforms serving students, researchers, and professionals who rely on assistive technologies. Screen readers cannot interpret rendered math images, and poorly structured HTML output is equally opaque.

The standard solution is MathML (Mathematical Markup Language), a W3C specification designed specifically to convey mathematical meaning to assistive technologies. Both KaTeX and MathJax generate MathML output, but with important differences:

• MathJax 3 has deep accessibility integration via its built-in Speech Rule Engine (SRE), which generates natural-language descriptions of equations. It supports keyboard navigation within complex expressions and integrates with AT software like NVDA and JAWS.
• KaTeX generates MathML as part of its HTML output, providing baseline accessibility, but lacks the advanced speech and navigation features of MathJax's SRE.

For platforms serving educational content under WCAG 2.1 AA or Section 508 compliance requirements, MathJax's accessibility infrastructure provides capabilities that KaTeX currently cannot replicate.

Developers should also consider tools like MathSpeak and the A11y Project's recommendations for supplementing rendered equations with ARIA labels and descriptive alt text in image-based fallback scenarios.

Real-World Case Study: How Overleaf Scaled to 20 Million Users

No discussion of LaTeX in the browser at scale is complete without examining Overleaf. What began as a cloud-based collaborative editor has grown into the de facto standard platform for academic scientific writing, currently serving over 20 million registered users and having facilitated more than 100 million documents across its lifetime.

Overleaf's architecture illuminates the engineering realities of browser-based LaTeX at scale. Rather than attempting to compile LaTeX client-side, Overleaf runs full LaTeX compilation on its server infrastructure (using pdfLaTeX, XeLaTeX, and LuaLaTeX) and streams output back to the browser. This server-centric model provides:

• Complete package compatibility: Any package available in TeXLive works, without browser-side constraints.
• Consistent output: Compilation results are identical to what users would see locally.
• Collaborative editing: Real-time collaboration is coordinated server-side via Operational Transformation.

The trade-off is infrastructure complexity and cost, which Overleaf manages through horizontal scaling and intelligent compilation queuing. Its success validates the server-side compilation approach for professional-grade web-based TeX editors while simultaneously demonstrating the enormous market demand that makes these engineering investments worthwhile.

The broader statistic underscoring this demand:

97% of academic submissions in Mathematics and 89% in Statistics continue to rely on LaTeX as the primary document preparation standard. (Source: TeX StackExchange Community Data)

Alternative Horizons: Is Typst the Future of Web-Based Typesetting?

No honest survey of Typst vs LaTeX can ignore Typst's rapid rise. Typst is a modern typesetting system designed from first principles with the web era in mind: it compiles faster than LaTeX, uses a cleaner syntax, produces deterministic output, and is written in Rust — a language that compiles efficiently to WebAssembly.

Typst's browser integration story is consequently smoother than LaTeX's. Its Wasm compilation target is smaller and more performant, and its live-preview capabilities in web editors are dramatically more responsive than equivalent LaTeX setups.

However, the transition cost from LaTeX to Typst remains substantial for most organizations:

• Existing document libraries (potentially millions of

  .tex

  files) require migration.
• Journal submission systems are built around LaTeX and PDF workflows.
• The scientific community's institutional knowledge is deeply embedded in LaTeX syntax and tooling.

Typst is best understood as the right choice for new projects that are not bound by legacy compatibility requirements — internal documentation systems, modern e-learning platforms, or personal knowledge management tools. For any platform that must interoperate with the existing academic publishing ecosystem, LaTeX remains the non-negotiable standard for the foreseeable future.

Security First: Sanitizing User-Generated LaTeX Input

Any platform that allows users to submit LaTeX for rendering must treat that input as potentially adversarial. LaTeX's macro system is Turing-complete, meaning maliciously crafted input can, in certain contexts, execute arbitrary commands or cause denial-of-service through deliberate complexity.

Key security practices for platforms handling user-generated LaTeX:

• Allowlisting commands: Both KaTeX and MathJax operate on restricted command sets by default. Ensure user input is passed through these renderers rather than to a full TeX compiler without sanitization.
• Input length limits: Prevent computational DoS attacks by enforcing maximum equation length limits before rendering.
• Sandboxed compilation: If server-side full TeX compilation is required for user input, run it in isolated containers (Docker/Podman with restricted system access) with CPU and memory limits.
• Output sanitization: Validate and sanitize any HTML output generated by rendering pipelines before injecting it into the DOM to prevent XSS attacks.
• Dependency pinning: Pin math library versions and audit updates carefully, as vulnerabilities in rendering pipelines can cascade into application security issues.

Developer's Toolbox: Essential Libraries and Framework Integrations

react-katex

vue-katex

rehype-katex

remark-math

better-react-mathjax

mathjax-node

SwiftLaTeX

Typst.ts

pdf.js

pandoc

For Next.js deployments, the

remark-math

rehype-katex

Conclusion: The Road Ahead for Web-Based Scientific Writing

LaTeX in the browser has traveled an extraordinary distance — from hacky workarounds using Unicode approximations to production-grade WebAssembly compilation pipelines capable of rendering entire document classes client-side. The ecosystem has matured, the tooling has consolidated, and the developer experience has improved dramatically.

The decision framework, summarized: choose KaTeX when performance, bundle size, and render speed are your primary constraints and your equation set falls within common LaTeX commands. Choose MathJax 3 when you need comprehensive command coverage, advanced accessibility features, or multiple output format flexibility. Architect Wasm-based compilation when you need full LaTeX package support without server dependency. And watch Typst carefully for greenfield projects where legacy compatibility is not a requirement.

What is not in question is the direction of travel. Academic and scientific content is moving to the web at an accelerating pace, driven by open-access publishing mandates, collaborative research tools, and AI-assisted discovery platforms. The 97% LaTeX adoption rate in mathematics academia ensures that the demand for sophisticated browser-based rendering will only intensify.

The developers who build fluency in this ecosystem — who understand the performance trade-offs, the accessibility requirements, the security implications, and the emerging Wasm capabilities — will be the architects of the next generation of scientific publishing infrastructure. This guide is your starting point. The equations are waiting.

Sources

• Overleaf About Us — Overleaf Official Site
• KaTeX — Fast Math Typesetting for the Web, Khan Academy
• MathJax Project — Beautiful Math in All Browsers
• The State of WebAssembly 2024 and 2025 — Platform.uno
• MathJax Performance Discussion — MathJax Users Google Group
• KaTeX vs MathJax Bundle Size Discussion — Hacker News
• Statistics About LaTeX Use in the World — TeX StackExchange