Frameworks performance

How JavaScript frameworks score across LCP, INP and CLS.

Field data PhoneDesktopAll Scope All sites October 2026 edition · Phone field outcomes
Metric LCP INP CLS FCP TTFB
1

At a glance the headline numbers for Frameworks performance

How JavaScript frameworks score across LCP, INP and CLS.

97.2%
Best: htmx
good INP
94.5%
Most used: jQuery
good INP
60.0%
Worst: Next.js
good INP

htmx passes INP most often of any framework. 97.2% of its sites pass. Next.js passes least at 60.0%. jQuery (the most-used framework) sits at 94.5%. The gap is 37 percentage points.

The State of Web Vitals · October 2026 · 210,902 crawled sites · phone field datacorewebvitals.io/state-of-cwv
2

The ranking sorted by INP at p75, fastest first

# Framework INP p75 Passing Sites
1 React 120ms 92.4%
9,570
2 jQuery 121ms 94.5%
143,509
3 htmx 130ms 97.2%
301
4 Alpine.js 136ms 93.7%
2,671
5 Vue 144ms 92.2%
12,115
6 Svelte 153ms 88.7%
467
7 Nuxt 175ms 84.0%
1,309
8 Angular 191ms 78.8%
3,377
9 Remix 191ms 78.1%
53
10 Gatsby 213ms 68.9%
218
11 Next.js 268ms 60.0%
1,832
The State of Web Vitals · October 2026 · 210,902 crawled sites · phone field datacorewebvitals.io/state-of-cwv
3

Passing INP per framework which group passes the INP most often

FrameworkSitesPassing INPp75
htmx 0.1% 301 97% 130ms
jQuery 69.6% 143,509 95% 121ms
Alpine.js 1.3% 2,671 94% 136ms
React 4.6% 9,570 92% 120ms
Vue 5.9% 12,115 92% 144ms
Svelte 0.2% 467 89% 153ms
Nuxt 0.6% 1,309 84% 175ms
Angular 1.6% 3,377 79% 191ms
Remix 0% 53 78% 191ms
Gatsby 0.1% 218 69% 213ms
Next.js 0.9% 1,832 60% 268ms
Good Needs Improvement Poor Sorted best-passing first · median colored by its own rating · pass = good INP (200ms at p75) · one value per site

97% of htmx sites pass INP. Next.js trails 37 points behind, leaving 40% of its sites failing. computed

The State of Web Vitals · October 2026 · 210,902 crawled sites · phone field datacorewebvitals.io/state-of-cwv
4

All five vitals at once the whole category without toggling - cell is the pass rate, small number the p75

LCP
INP
CLS
FCP
TTFB
jQuery 69.6%
802.3s
95121ms
900.02
721.9s
521.3s
Vue 5.9%
762.5s
92144ms
850.04
692.0s
531.3s
React 4.6%
842.1s
92120ms
920.01
811.6s
681.0s
Angular 1.6%
652.9s
79191ms
580.26
692.0s
77745ms
Alpine.js 1.3%
892.0s
94136ms
920.01
801.7s
551.2s
Next.js 0.9%
832.2s
60268ms
660.18
831.6s
67915ms
Nuxt 0.6%
692.7s
84175ms
720.12
682.0s
611.0s
Svelte 0.2%
842.1s
89153ms
860.05
811.6s
69935ms
htmx 0.1%
902.0s
97130ms
930.01
831.6s
571.1s
Gatsby 0.1%
622.9s
69213ms
670.19
891.5s
77767ms
Remix 0%
782.2s
78191ms
750.10
731.8s
501.2s
60%95%+ passing Cell: pass rate, small number = p75 · faded rows: under 100 sites

One row per framework, one column per vital - the cell is the share of sites passing, the small number the p75. No toggling needed to see where the category actually differs.

htmx leads on INP: 97% of its sites pass. Next.js trails at 60%. computed

The State of Web Vitals · October 2026 · 210,902 crawled sites · phone field datacorewebvitals.io/state-of-cwv
5

Why this matters for the Core Web Vitals, and where to start fixing it

A JavaScript framework shapes how much code runs in the browser to put your page on the screen. Some render on the server and ship light; others send a large bundle and rebuild the page on the client, which lands on the main thread and shows up in INP. The framework is not destiny, but its defaults and how you use them decide how much JavaScript the visitor has to pay for.

Start by looking at what ships to the browser and when it runs. Render on the server where you can, split the bundle so each page only loads what it needs, and defer the work that is not required for the first interaction. Framework JavaScript runs on the main thread, so the less of it the browser has to execute, the better INP gets.

How does your framework affect Core Web Vitals?

Among the JavaScript frameworks, React reaches a good INP on 92.4% of sites; Next.js on 60.0%.

Related signals CDN (7) → CMS (12) → 210,902 crawled sites with Chrome field data, representing millions of real page loads · How we measured