Solid i18n Libraries - 2026 Benchmark Report

This page is a benchmark report for i18n solutions on Solid.

Table of Contents

Interactive Benchmark

Results reference:

See complete benchmark data

See complete benchmark repository here.

Introduction

Internationalization solutions are among the heaviest dependencies in a Solid app. The main risk is shipping unnecessary content: translations for other pages and other locales in a single route’s bundle.

As your app grows, that problem can quickly blow up the JavaScript sent to the client and slow down navigation.

In practice, for the least optimized implementations, an internationalized page can end up several times heavier than the version without i18n.

The other impact is on developer experience: how you declare content, types, namespace organization, dynamic loading, and reactivity when the locale changes.

TL;DR

• Intlayer: Recommended choice for professional Solid applications needing advanced features and optimization (v8.7.12).
• @solid-primitives/i18n: Excellent lightweight alternative for simple projects, though lacks advanced features like lazy loading.
• solid-i18next: Standard but heavy option (~4.7× Intlayer) with same downsides as React i18next.
• Paraglide: Innovative approach but complex DX and tree-shaking issues in some setups.

Test your app

To quickly spot i18n leakage issues, I set up a free scanner available here.

The problem

Two levers are essential to limit the cost of a multilingual app:

• Split content by page / namespace so you do not load whole dictionaries when you do not need them
• Load the right locale dynamically, only when needed

Understanding the technical limitations of these approaches:

Dynamic loading

Without dynamic loading, most solutions keep messages in memory from the first render, which adds significant overhead for apps with many routes and locales.

With dynamic loading, you accept a trade-off: less initial JS, but sometimes an extra request when switching language.

Content splitting

Syntaxes built around t('a.b.c') are very convenient but often encourage keeping large JSON objects at runtime. That model makes tree-shaking hard unless the library offers a real per-page split strategy.

Methodology

For this benchmark, we compared the following libraries:

• Base App (No i18n library)
• solid-intlayer (v8.7.12)
• @solid-primitives/i18n (v2.2.1)
• solid-i18next (v17.0.2)
• @inlang/paraglide-js (v2.17.0)

The framework is Solid with a multilingual app of 10 pages and 10 languages.

We compared four loading strategies:

Strategy summary

• Static: Simple; no network latency after the initial load. Downside: large bundle size.
• Dynamic: Reduces initial weight (lazy-loading). Ideal when you have many locales.
• Scoped static: Keeps code organized (logical separation) without complex extra network requests.
• Scoped dynamic: Best approach for code splitting and performance. Minimizes memory by loading only what the current view and active locale need.

What I measured:

I ran the same multilingual app in a real browser for every stack, then wrote down what actually showed up on the wire and how long things took. Sizes are reported after normal web compression, because that is closer to what people download than raw source counts.

• Internationalization library size: After bundling, tree-shaking and minification, the size of the i18n library is the size of the providers + hooks/primitives code in an empty component. It does not include the loading of translation files. It answers how expensive the library is before your content enters the picture.

• JavaScript per page: For each benchmark route, how much script the browser pulls in for that visit, averaged across the pages in the suite (and across locales where the report rolls them up). Heavy pages are slow pages.

• Leakage from other locales: It's the content of the same page but in another language that would be loaded by mistake in the audited page. This content is unnecessary and should be avoided. (e.g. /fr/about page content in /en/about page bundle)

• Leakage from other routes: The same idea for other screens in the app: whether their copy is riding along when you only opened one page. (e.g. /en/about page content in /en/contact page bundle). A high score hints at weak splitting or over-broad bundles.

• Average component bundle size: Common UI pieces are measured one at a time instead of hiding inside one giant app number. It shows whether internationalization quietly inflates everyday components. For instance, if your component rerenders, it will load all that data from memory. Attaching a giant JSON to any component is like connecting a big store of unused data that will slow down your components’ performance.

• Language switch responsiveness: I flip the language using the app’s own control and time how long it takes until the page has clearly switched, what a visitor would notice, not a lab micro-step.

• Rendering work after a language change: A narrower follow-up: how much effort the interface took to repaint for the new language once the switch is in flight. Useful when the “felt” time and the framework cost diverge.

• Initial page load time: From navigation to the browser considering the page fully loaded for the scenarios I tested. Good for comparing cold starts.

• Hydration time: When the app exposes it, how long the client spends turning server HTML into something you can actually click. A dash in the tables means that implementation did not provide a reliable hydration figure in this benchmark.

GitHub STARs

GitHub stars are a strong indicator of a project's popularity, community trust, and long-term relevance. While not a direct measure of technical quality, they reflect how many developers find the project useful, follow its progress, and are likely to adopt it. For estimating the value of a project, stars help compare traction across alternatives and provide insights into ecosystem growth.

Results in detail

1 - Solutions to avoid

No clear solution to avoid in solid ecosystem.

2 - Acceptable solutions

(solid-i18next) (solid-i18next@17.0.2):

solid-i18next is probably the most popular option because it was among the first to serve JavaScript app i18n needs. It also has a wide set of community plugins for specific problems.

The package is heavy (~14.6kb, which is about 4.7× solid-intlayer).

Still, it shares the same major downsides as stacks built on t('a.b.c'): optimizations are possible but very time-consuming, and large projects risk bad practices (namespaces + dynamic loading + types).

(@solid-primitives/i18n) (@solid-primitives/i18n@2.2.1):

Solid primitive is extremely light and efficient, I recommend that solution for light projects, but it can quickly become lacking features for professional solutions including cookie management, proxy redirection, formatters etc. It also misses lazy loading and scoping namespaces for page size optimization.

(Paraglide) (@inlang/paraglide-js@2.17.0):

Paraglide offers an innovative, well-thought-out approach. Even so, in this benchmark the tree-shaking their company advertises did not work for my implementation. The workflow and DX are also more complex than other options. Personally I dislike having to regenerate JS files before every push, which creates constant merge conflict risk via PRs. Finally, in comparison with other solutions, Paraglide does not use a store (e.g. Solid signal) to retrieve the current locale to render the content. For each node parsed, it will request the locale from the localStorage / cookie etc. It leads to execution of unnecessary logic that impacts the component reactivity.

3 - Recommendations

(Intlayer) (solid-intlayer@8.7.12):

I will not personally judge solid-intlayer for objectivity’s sake, since it is my own solution.

Personal note

This note is personal and does not affect the benchmark results. Still, in the i18n world you often see consensus around a pattern like const t = useTranslation('xx') + <>{t('xx.xx')}</> for translated content.

In Solid apps, injecting a function as a JSX.Element is, in my view, an anti-pattern. It also adds avoidable complexity and JavaScript execution overhead (even if it is barely noticeable).