Frameworks performance

How JavaScript frameworks score across LCP, INP and CLS.

Field data PhoneDesktopAll Scope All sites October 2026 edition · All devices 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.

89.4%
Best: Alpine.js
good LCP
80.7%
Most used: jQuery
good LCP
66.2%
Worst: Angular
good LCP

Alpine.js passes LCP most often of any framework. 89.4% of its sites pass. Angular passes least at 66.2%. jQuery (the most-used framework) sits at 80.7%. The gap is 23 percentage points.

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

The ranking sorted by LCP at p75, fastest first

# Framework LCP p75 Passing Sites
1 Alpine.js 1.9s 89.4%
2,671
2 htmx 1.9s 90.5%
301
3 React 2.1s 84.6%
9,570
4 Svelte 2.1s 83.3%
467
5 Next.js 2.1s 85.2%
1,832
6 Remix 2.2s 84.6%
53
7 jQuery 2.3s 80.7%
143,509
8 Vue 2.4s 76.3%
12,115
9 Gatsby 2.6s 71.4%
218
10 Nuxt 2.6s 71.9%
1,309
11 Angular 2.8s 66.2%
3,377
The State of Web Vitals · October 2026 · 210,902 crawled sites · all devices field datacorewebvitals.io/state-of-cwv
3

Passing LCP per framework which group passes the LCP most often

FrameworkSitesPassing LCPp75
htmx 0.1% 301 91% 1.9s
Alpine.js 1.3% 2,671 89% 1.9s
Next.js 0.9% 1,832 85% 2.1s
React 4.6% 9,570 85% 2.1s
Remix 0% 53 85% 2.2s
Svelte 0.2% 467 83% 2.1s
jQuery 69.6% 143,509 81% 2.3s
Vue 5.9% 12,115 76% 2.4s
Nuxt 0.6% 1,309 72% 2.6s
Gatsby 0.1% 218 71% 2.6s
Angular 1.6% 3,377 66% 2.8s
Good Needs Improvement Poor Sorted best-passing first · median colored by its own rating · pass = good LCP (2.5s at p75) · one value per site

91% of htmx sites pass LCP. Angular trails 25 points behind, leaving 34% of its sites failing. computed

The State of Web Vitals · October 2026 · 210,902 crawled sites · all devices 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%
812.3s
97104ms
880.04
731.9s
511.4s
Vue 5.9%
762.4s
95127ms
830.06
712.0s
531.4s
React 4.6%
852.1s
95103ms
920.01
811.5s
671.1s
Angular 1.6%
662.8s
88162ms
560.24
702.0s
76766ms
Alpine.js 1.3%
891.9s
97115ms
910.03
821.6s
571.1s
Next.js 0.9%
852.1s
68233ms
660.17
841.5s
69878ms
Nuxt 0.6%
722.6s
88157ms
690.14
731.9s
631.0s
Svelte 0.2%
832.1s
92138ms
850.05
821.6s
71885ms
htmx 0.1%
911.9s
99108ms
870.03
831.5s
591.1s
Gatsby 0.1%
712.6s
82180ms
650.19
931.4s
78755ms
Remix 0%
852.2s
87150ms
730.11
811.6s
561.1s
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 LCP: 91% of its sites pass. Angular trails at 66%. computed

The State of Web Vitals · October 2026 · 210,902 crawled sites · all devices 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, Alpine.js reaches a good LCP on 89.4% of sites; Angular on 66.2%.

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