The State of Web Vitals

Cache strategy mix

The caching posture across responses: immutable, long TTL, short TTL, no-store.

Field data PhoneDesktopAll Scope All sites Q1 2026 edition · Desktop field outcomes
Metric LCP INP CLS
1

At a glance the headline numbers for Cache strategy mix

The caching posture across responses: immutable, long TTL, short TTL, no-store.

7
Categories
In the distribution
35.1%
Fleet share
Top: unspecified
97.7%
Sites with any
Of unspecified

3.1% of responses forbid caching outright. 10.9% are immutable.

The State of Web Vitals · Q1 2026 · 94,910 sites · desktop field datacorewebvitals.io/state-of-cwv
2

The cache strategy mix mix who uses what, and how stable each group is

Median CLS (sites using feature)
0
0.10
0.20
0.30
0.40
0.50
Unspecified0.0335% of sites
Long ttl0.0325% of sites
Medium ttl0.0317% of sites
Immutable0.0311% of sites
Short ttl0.038% of sites
No store0.033% of sites
No cache0.032% of sites
VariantShare of sitesMedian
Unspecified
35%
0.03
Long ttl
25%
0.03
Medium ttl
17%
0.03
Immutable
11%
0.03
Short ttl
8%
0.03
No store
3%
0.03
No cache
2%
0.03

Cache strategy mix. On the fleet: 35.1% unspecified, 24.7% long ttl, 16.6% medium ttl. 97.7% of sites use at least one unspecified.

Lowest-share bucket: CLS 0.02. Highest-share bucket: CLS 0.03. r = +0.49.

The State of Web Vitals · Q1 2026 · 94,910 sites · desktop field datacorewebvitals.io/state-of-cwv
3

Passing CLS per bucket every category and count level at once - color is the pass rate

1
2
3
4
5
6
7
8
9
10
11
12
Unspecified 35.1%
85
82
82
80
77
78
78
77
78
76
75
73
Long ttl 24.7%
81
79
77
76
75
76
75
78
78
78
77
76
Medium ttl 16.6%
80
79
78
78
77
78
77
80
76
77
72
68
Immutable 10.9%
78
75
74
78
77
74
75
77
72
83
85
81
Short ttl 7.8%
82
79
78
78
78
77
75
73
70
72
70
No store 3.1%
81
80
79
77
78
74
74
73
72
70
70
No cache 1.8%
79
78
79
78
77
78
75
79
71
77
69
73
← few of this category on the pagemany →
60%95%+ of sites passing CLS Faded cells: under 100 sites

Each row is a category, each column its own count bucket (few on the left, many on the right); the cell is the share of those sites passing CLS.

Unspecified swings the hardest: 85% of sites pass CLS with few, 73% with many. computed

The State of Web Vitals · Q1 2026 · 94,910 sites · desktop field datacorewebvitals.io/state-of-cwv
4

Few vs many - does quantity cost CLS? the pass rate with few vs many of each category

60%70%80%90%100% few → many
Medium ttl 16.6% 80% → 68%
Unspecified 35.1% 85% → 73%
Short ttl 7.8% 82% → 70%
No store 3.1% 81% → 70%
No cache 1.8% 79% → 73%
Long ttl 24.7% 81% → 76%
Immutable 10.9% 78% → 81%
% of sites passing CLS · hollow ring = pages with few, solid dot = pages with many

Per category: the pass rate among pages with FEW of it (hollow ring) against pages with MANY (solid dot), worst trend first. Thin buckets are excluded from the endpoints.

More Medium ttl costs the most: the CLS pass rate falls from 80% with few to 68% with many. computed

The State of Web Vitals · Q1 2026 · 94,910 sites · desktop field datacorewebvitals.io/state-of-cwv
5

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

Field data includes repeat visitors, and caching decides how fast the page is the second time. A response marked immutable is served from disk without asking the server anything. A short TTL forces a revalidation request first. A no-store response is downloaded again in full on every visit.

The pattern to aim for is old and boring: hashed filenames with long, immutable TTLs for assets, and short caching only where content actually changes. Every revalidation you avoid is a round trip the LCP does not wait for.

How does this affect the Core Web Vitals?

Of the 7 categories, Unspecified separates passing sites from failing sites the most. Where Unspecified is rare: 91% pass the LCP. Where it is common: 86%.

Go fix it TTFB: cache duration → Configure Cloudflare for passing the Core Web Vitals →
Related signals Cacheable responses → HTTP protocol mix → HTML size (kB) → Last-Modified present → Chrome field data from 94,910 sites, representing millions of real page loads · How we measured