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.

76.1%
Best: Angular
good TTFB
62.6%
Typical framework
good TTFB
51.3%
Worst: jQuery
good TTFB

Angular passes TTFB most often of any framework. 76.1% of its sites pass. jQuery (the most-used framework) passes least at 51.3%. The gap is 25 percentage points.

The State of Web Vitals · October 2026 · 212,308 crawled sites · all devices field datacorewebvitals.io/state-of-cwv
2

The ranking sorted by TTFB at p75, fastest first

# Framework TTFB p75 Passing Sites
1 Gatsby 755ms 77.7%
218
2 Angular 768ms 76.1%
3,396
3 Next.js 880ms 69.0%
1,840
4 Svelte 885ms 71.1%
471
5 Nuxt 1.0s 62.6%
1,311
6 React 1.1s 66.5%
9,658
7 Remix 1.1s 56.3%
53
8 htmx 1.1s 58.1%
305
9 Alpine.js 1.1s 57.0%
2,689
10 Vue 1.4s 52.4%
12,199
11 jQuery 1.4s 51.3%
144,465
The State of Web Vitals · October 2026 · 212,308 crawled sites · all devices field datacorewebvitals.io/state-of-cwv
3

Passing TTFB per framework which group passes the TTFB most often

FrameworkSitesPassing TTFBp75
Gatsby 0.1% 218 78% 755ms
Angular 1.6% 3,396 76% 768ms
Svelte 0.2% 471 71% 885ms
Next.js 0.9% 1,840 69% 880ms
React 4.7% 9,658 67% 1.1s
Nuxt 0.6% 1,311 63% 1.0s
htmx 0.1% 305 58% 1.1s
Alpine.js 1.3% 2,689 57% 1.1s
Remix 0% 53 56% 1.1s
Vue 5.9% 12,199 52% 1.4s
jQuery 69.6% 144,465 51% 1.4s
Good Needs Improvement Poor Sorted best-passing first · median colored by its own rating · pass = good TTFB (800ms at p75) · one value per site

78% of Gatsby sites pass TTFB. JQuery trails 27 points behind, leaving 49% of its sites failing. computed

The State of Web Vitals · October 2026 · 212,308 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
521.4s
React 4.7%
852.1s
95103ms
920.01
811.5s
671.1s
Angular 1.6%
662.9s
88162ms
570.24
702.0s
76768ms
Alpine.js 1.3%
891.9s
97115ms
910.03
821.6s
571.1s
Next.js 0.9%
852.1s
68233ms
660.17
841.5s
69880ms
Nuxt 0.6%
722.6s
88158ms
690.14
731.9s
631.0s
Svelte 0.2%
832.1s
92138ms
860.05
821.6s
71885ms
htmx 0.1%
911.9s
99108ms
870.03
831.5s
581.1s
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.

Gatsby leads on TTFB: 78% of its sites pass. jQuery trails at 51%. computed

The State of Web Vitals · October 2026 · 212,308 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, Gatsby reaches a good TTFB on 77.7% of sites; jQuery on 51.3%.

Related signals CDN (7) → CMS (12) → 212,308 crawled sites with Chrome field data, representing millions of real page loads · How we measured