Lab paint timeline

Lab paint timeline on a cold crawl: TTFB, FCP, LCP in ms - setup-specific, not field data.

LCP INP CLS

At a glance the headline numbers for Lab paint timeline

Lab paint timeline on a cold crawl: TTFB, FCP, LCP in ms - setup-specific, not field data.

114ms

lab TTFB

on the typical page

366ms

lab FCP

on the typical page

556ms

lab LCP

on the typical page

185,348

sites measured

phone field data

In the lab, the typical site paints its main content after 556ms.

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

Distribution & median CLS site count and median CLS at each level of lab paint timeline — ttfb

0.00 0.04 0.08 0.11 0.15

0.1

0 43247 86494

0–99 100–199 200–299 300–399 400–499 500–599 600–699 700–799 800–899 900–999 1000+

Good (≤0.1) Needs improvement Poor (>0.25) Site count

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

Passing CLS by lab paint timeline — ttfb which level passes the CLS most often

Lab paint timeline — ttfbSitesPassing CLSCLS

0–99 86,494 88% 0.00

100–199 27,580 89% 0.00

200–299 14,515 90% 0.00

300–399 10,443 90% 0.00

400–499 7,871 90% 0.00

500–599 6,194 89% 0.00

600–699 4,941 91% 0.00

700–799 3,939 90% 0.00

800–899 3,253 88% 0.00

900–999 2,666 89% 0.00

1000+ 17,452 89% 0.00

Good Needs Improvement Poor Faded rows: under 100 sites

Lab paint timeline — ttfb 114ms. p75 399ms. p99 3.4s. At the low end (0–99ms): CLS 0.00. At the high end (1000+ms): CLS 0.00. computed

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

Distribution & median CLS site count and median CLS at each level of lab paint timeline — fcp

0.00 0.04 0.08 0.11 0.15

0.1

0 30210 60420

0–249 250–499 500–749 750–999 1000–1249 1250–1499 1500–1749 1750+

Good (≤0.1) Needs improvement Poor (>0.25) Site count

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

Passing CLS by lab paint timeline — fcp which level passes the CLS most often

Lab paint timeline — fcpSitesPassing CLSCLS

0–249 60,420 91% 0.00

250–499 52,470 89% 0.00

500–749 23,166 88% 0.00

750–999 13,077 88% 0.00

1000–1249 8,650 87% 0.00

1250–1499 5,799 87% 0.00

1500–1749 4,011 88% 0.00

1750+ 16,127 87% 0.00

Good Needs Improvement Poor Faded rows: under 100 sites

Lab paint timeline — fcp 366ms. p75 776ms. p99 10.2s. At the low end (0–249ms): CLS 0.00. At the high end (1750+ms): CLS 0.00. computed

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

Distribution & median CLS site count and median CLS at each level of lab paint timeline — lcp

0.00 0.04 0.08 0.11 0.15

0.1

0 40835 81670

0–499 500–999 1000–1499 1500–1999 2000–2499 2500–2999 3000–3499 3500–3999 4000–4499 4500+

Good (≤0.1) Needs improvement Poor (>0.25) Site count

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

Passing CLS by lab paint timeline — lcp which level passes the CLS most often

Lab paint timeline — lcpSitesPassing CLSCLS

0–499 81,670 91% 0.00

500–999 37,049 88% 0.00

1000–1499 16,429 86% 0.00

1500–1999 8,795 85% 0.00

2000–2499 5,047 86% 0.00

2500–2999 3,067 85% 0.00

3000–3499 2,832 86% 0.00

3500–3999 3,214 87% 0.00

4000–4499 3,290 87% 0.00

4500+ 14,786 86% 0.00

Good Needs Improvement Poor Faded rows: under 100 sites

Lab paint timeline — lcp 556ms. p75 1.4s. p99 19.3s. At the low end (0–499ms): CLS 0.00. At the high end (4500+ms): CLS 0.00. computed

The State of Web Vitals · Q2 2026 · 189,915 sites · phone field datacorewebvitals.io/state-of-cwv

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

These are lab numbers from our crawler: one cold load, one machine, one network profile. They are not what your visitors feel. That is what the field data on the rest of this site measures. What the lab gives you is a controlled comparison: identical conditions for every site, so a difference between two sites is a real difference between the pages.

Read the gaps. Lab LCP far worse than field LCP usually means caching and repeat visits carry the field numbers. The reverse usually means the crawler's connection is kinder than your visitors' phones.

How does this affect the Core Web Vitals?

Lab paint timeline correlates with the CLS. Lab LCP separates passing sites from failing sites the most. Where the lab LCP is low, 91% of sites pass the CLS. Where it is high, 86% do. The decline is gradual. There is no point where sites suddenly start failing.

Go fix it How we measured → Chrome DevTools network settings for CWV testing →

Related signals Total Blocking Time (lab) → Main-thread CPU (lab) → Document lifecycle (lab) → Chrome field data from 189,915 sites, representing millions of real page loads · How we measured

Live queries (6) — admin only

Query #1: 431.6 ms

SELECT COUNT(*) AS count,
            quantile_disc(m.synthetic.paint.ttfb, 0.10) AS p10,
            quantile_disc(m.synthetic.paint.ttfb, 0.25) AS p25,
            quantile_disc(m.synthetic.paint.ttfb, 0.50) AS p50,
            quantile_disc(m.synthetic.paint.ttfb, 0.75) AS p75,
            quantile_disc(m.synthetic.paint.ttfb, 0.90) AS p90,
            quantile_disc(m.synthetic.paint.ttfb, 0.99) AS p99
            FROM sites WHERE m.synthetic.paint.ttfb IS NOT NULL;

Query #2: 379.1 ms

SELECT CASE WHEN m.synthetic.paint.ttfb >= 0 AND m.synthetic.paint.ttfb < 100 THEN 0 WHEN m.synthetic.paint.ttfb >= 100 AND m.synthetic.paint.ttfb < 200 THEN 1 WHEN m.synthetic.paint.ttfb >= 200 AND m.synthetic.paint.ttfb < 300 THEN 2 WHEN m.synthetic.paint.ttfb >= 300 AND m.synthetic.paint.ttfb < 400 THEN 3 WHEN m.synthetic.paint.ttfb >= 400 AND m.synthetic.paint.ttfb < 500 THEN 4 WHEN m.synthetic.paint.ttfb >= 500 AND m.synthetic.paint.ttfb < 600 THEN 5 WHEN m.synthetic.paint.ttfb >= 600 AND m.synthetic.paint.ttfb < 700 THEN 6 WHEN m.synthetic.paint.ttfb >= 700 AND m.synthetic.paint.ttfb < 800 THEN 7 WHEN m.synthetic.paint.ttfb >= 800 AND m.synthetic.paint.ttfb < 900 THEN 8 WHEN m.synthetic.paint.ttfb >= 900 AND m.synthetic.paint.ttfb < 1000 THEN 9 WHEN m.synthetic.paint.ttfb >= 1000 THEN 10 END AS bucket_idx, COUNT(*) AS n,
            quantile_disc(crux."phone".cls, 0.5) AS median,
            COUNT(*) FILTER (WHERE crux."phone".cls IS NOT NULL AND crux."phone".cls <= 0.1) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE crux."phone".cls IS NOT NULL), 0) AS good_pct
            FROM sites WHERE m.synthetic.paint.ttfb IS NOT NULL GROUP BY bucket_idx;

Query #3: 415.1 ms

SELECT COUNT(*) AS count,
            quantile_disc(m.synthetic.paint.fcp, 0.10) AS p10,
            quantile_disc(m.synthetic.paint.fcp, 0.25) AS p25,
            quantile_disc(m.synthetic.paint.fcp, 0.50) AS p50,
            quantile_disc(m.synthetic.paint.fcp, 0.75) AS p75,
            quantile_disc(m.synthetic.paint.fcp, 0.90) AS p90,
            quantile_disc(m.synthetic.paint.fcp, 0.99) AS p99
            FROM sites WHERE m.synthetic.paint.fcp IS NOT NULL;

Query #4: 504.2 ms

SELECT CASE WHEN m.synthetic.paint.fcp >= 0 AND m.synthetic.paint.fcp < 250 THEN 0 WHEN m.synthetic.paint.fcp >= 250 AND m.synthetic.paint.fcp < 500 THEN 1 WHEN m.synthetic.paint.fcp >= 500 AND m.synthetic.paint.fcp < 750 THEN 2 WHEN m.synthetic.paint.fcp >= 750 AND m.synthetic.paint.fcp < 1000 THEN 3 WHEN m.synthetic.paint.fcp >= 1000 AND m.synthetic.paint.fcp < 1250 THEN 4 WHEN m.synthetic.paint.fcp >= 1250 AND m.synthetic.paint.fcp < 1500 THEN 5 WHEN m.synthetic.paint.fcp >= 1500 AND m.synthetic.paint.fcp < 1750 THEN 6 WHEN m.synthetic.paint.fcp >= 1750 THEN 7 END AS bucket_idx, COUNT(*) AS n,
            quantile_disc(crux."phone".cls, 0.5) AS median,
            COUNT(*) FILTER (WHERE crux."phone".cls IS NOT NULL AND crux."phone".cls <= 0.1) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE crux."phone".cls IS NOT NULL), 0) AS good_pct
            FROM sites WHERE m.synthetic.paint.fcp IS NOT NULL GROUP BY bucket_idx;

Query #5: 429.3 ms

SELECT COUNT(*) AS count,
            quantile_disc(m.synthetic.paint.lcp, 0.10) AS p10,
            quantile_disc(m.synthetic.paint.lcp, 0.25) AS p25,
            quantile_disc(m.synthetic.paint.lcp, 0.50) AS p50,
            quantile_disc(m.synthetic.paint.lcp, 0.75) AS p75,
            quantile_disc(m.synthetic.paint.lcp, 0.90) AS p90,
            quantile_disc(m.synthetic.paint.lcp, 0.99) AS p99
            FROM sites WHERE m.synthetic.paint.lcp IS NOT NULL;

Query #6: 428.9 ms

SELECT CASE WHEN m.synthetic.paint.lcp >= 0 AND m.synthetic.paint.lcp < 500 THEN 0 WHEN m.synthetic.paint.lcp >= 500 AND m.synthetic.paint.lcp < 1000 THEN 1 WHEN m.synthetic.paint.lcp >= 1000 AND m.synthetic.paint.lcp < 1500 THEN 2 WHEN m.synthetic.paint.lcp >= 1500 AND m.synthetic.paint.lcp < 2000 THEN 3 WHEN m.synthetic.paint.lcp >= 2000 AND m.synthetic.paint.lcp < 2500 THEN 4 WHEN m.synthetic.paint.lcp >= 2500 AND m.synthetic.paint.lcp < 3000 THEN 5 WHEN m.synthetic.paint.lcp >= 3000 AND m.synthetic.paint.lcp < 3500 THEN 6 WHEN m.synthetic.paint.lcp >= 3500 AND m.synthetic.paint.lcp < 4000 THEN 7 WHEN m.synthetic.paint.lcp >= 4000 AND m.synthetic.paint.lcp < 4500 THEN 8 WHEN m.synthetic.paint.lcp >= 4500 THEN 9 END AS bucket_idx, COUNT(*) AS n,
            quantile_disc(crux."phone".cls, 0.5) AS median,
            COUNT(*) FILTER (WHERE crux."phone".cls IS NOT NULL AND crux."phone".cls <= 0.1) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE crux."phone".cls IS NOT NULL), 0) AS good_pct
            FROM sites WHERE m.synthetic.paint.lcp IS NOT NULL GROUP BY bucket_idx;

JSON file lookups (4)

                    20260625/meta.json
                    0.03 ms
                

                    20260625/types.json
                    0.57 ms
                

                    20260625/loaf-scripts.json
                    0.41 ms
                

                    20260625/menu.json
                    0.23 ms
                