Frameworks performance

How JavaScript frameworks score across LCP, INP and CLS.

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

88.0%
Best: Gatsby
good TTFB
56.6%
Most used: jQuery
good TTFB
52.5%
Worst: React
good TTFB

Gatsby passes TTFB most often of any framework. 88.0% of its sites pass. React passes least at 52.5%. jQuery (the most-used framework) sits at 56.6%. The gap is 36 percentage points.

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

The ranking sorted by TTFB at p75, fastest first

# Framework TTFB p75 Passing Sites
1 Gatsby 562ms 88.0%
218
2 Angular 643ms 82.0%
3,396
3 Svelte 807ms 74.7%
471
4 Next.js 874ms 70.8%
1,840
5 Remix 1.0s 60.7%
53
6 Nuxt 1.0s 64.2%
1,311
7 htmx 1.1s 62.3%
305
8 Alpine.js 1.1s 61.3%
2,689
9 Vue 1.1s 60.9%
12,199
10 jQuery 1.2s 56.6%
144,465
11 React 1.5s 52.5%
9,658
The State of Web Vitals · October 2026 · 212,308 crawled sites · desktop field datacorewebvitals.io/state-of-cwv
3

Passing TTFB per framework which group passes the TTFB most often

FrameworkSitesPassing TTFBp75
Gatsby 0.1% 218 88% 562ms
Angular 1.6% 3,396 82% 643ms
Svelte 0.2% 471 75% 807ms
Next.js 0.9% 1,840 71% 874ms
Nuxt 0.6% 1,311 64% 1.0s
htmx 0.1% 305 62% 1.1s
Alpine.js 1.3% 2,689 61% 1.1s
Vue 5.9% 12,199 61% 1.1s
Remix 0% 53 61% 1.0s
jQuery 69.6% 144,465 57% 1.2s
React 4.7% 9,658 53% 1.5s
Good Needs Improvement Poor Sorted best-passing first · median colored by its own rating · pass = good TTFB (800ms at p75) · one value per site

88% of Gatsby sites pass TTFB. React trails 35 points behind, leaving 47% of its sites failing. computed

The State of Web Vitals · October 2026 · 212,308 crawled sites · desktop 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%
852.0s
9962ms
780.09
801.6s
571.2s
Vue 5.9%
832.2s
9978ms
720.12
801.6s
611.1s
React 4.7%
792.3s
9966ms
800.08
771.7s
531.5s
Angular 1.6%
752.5s
96106ms
490.27
801.6s
82643ms
Alpine.js 1.3%
921.7s
9966ms
800.08
871.4s
611.1s
Next.js 0.9%
862.0s
95111ms
660.15
861.4s
71874ms
Nuxt 0.6%
782.4s
9798ms
640.16
781.7s
641.0s
Svelte 0.2%
881.9s
9886ms
790.07
871.3s
75807ms
htmx 0.1%
961.7s
10065ms
780.07
911.3s
621.1s
Gatsby 0.1%
832.3s
86130ms
690.13
931.0s
88562ms
Remix 0%
872.0s
100100ms
730.11
871.4s
611.0s
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: 88% of its sites pass. React trails at 53%. computed

The State of Web Vitals · October 2026 · 212,308 crawled sites · desktop 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 88.0% of sites; React on 52.5%.

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