By the fastCRW team · Benchmark numbers from diagnose_3way.py on Firecrawl's public scrape-content dataset, verified 2026-05-18 (single run, 3,000 requests, 2026-05-08) · Verify independently before quoting.
Disclosure: We build fastCRW. This is a vendor-authored comparison, so weight it accordingly — every number here traces to one shared run on a dataset we did not create.
Crawl4AI truth-recall vs fastCRW accuracy: the head-to-head
If you are choosing between two open, Firecrawl-adjacent scrapers, the question that actually matters is not "which one returned a 200" — it is "which one returned the content you came for." That metric is truth-recall: the share of labeled ground-truth content a scraper actually brings back. On Firecrawl's own public scrape-content-dataset-v1 — 1,000 URLs, of which 819 carry labeled ground truth — fastCRW recalled 63.74% of those 819 labeled URLs versus Crawl4AI's 59.95% (diagnose_3way.py, 2026-05-08). That is a 3.79 percentage-point edge to fastCRW on an identical set, with both tools comfortably ahead of Firecrawl's 56.04% on the same run.
The two engines are effectively tied on the typical request, and fastCRW's fast mode posts the lowest p90 of the three tools tested. Below is the full picture, then the trade-off that explains it.
fastCRW 63.74% vs Crawl4AI 59.95% truth-recall
Truth-recall is measured against labeled ground truth, not against the request count. The denominator is 819 labeled URLs (not 1,000 URLs and not 3,000 requests). fastCRW returned the correct content for 522 of those 819; Crawl4AI for 491. Both beat Firecrawl's 459. Phrased the way it should always be phrased: fastCRW recalled 63.74% of 819 labeled URLs, Crawl4AI 59.95%.
+3.79 pp on the same 819 labeled URLs
A 3.79-point recall gap sounds small until you compound it. If your pipeline scrapes 100,000 pages, the difference is roughly 3,790 pages where fastCRW returned the labeled content and Crawl4AI returned a thinner or partial page. In a RAG index or an extraction job, those are not random misses — they are silent gaps that surface later as "the model didn't know that" or as empty fields that need a manual re-scrape.
Both beat Firecrawl on recall
Worth stating plainly so nobody reads this as fastCRW-only spin: on this run, both open engines out-recalled the category reference. fastCRW led at 63.74%, Crawl4AI followed at 59.95%, and Firecrawl trailed at 56.04% of the 819 labeled URLs. Recall and "managed cloud maturity" are different axes.
The latency head-to-head
We never publish a single average latency, because one slow tail makes an average meaningless. We publish the p50 and fast-mode p90 for both engines, on the same run.
| Metric | fastCRW | Crawl4AI |
|---|---|---|
| Truth-recall (of 819 labeled) | 63.74% (522) | 59.95% (491) |
| Scrape-success (of reachable URLs) | ~92% (0 errors) | 83.5% (835) |
| Thrown errors (of 3,000) | 0 | 0 |
| p50 latency | 1914 ms | 1916 ms |
| p90 latency (fast mode) | 4348 ms | 4754 ms |
p50 nearly tied: 1914 ms vs 1916 ms
On the median request — the one most of your traffic actually experiences — the two engines are indistinguishable: fastCRW's p50 is 1914 ms, Crawl4AI's is 1916 ms, two milliseconds apart (diagnose_3way.py, 2026-05-08). For comparison, Firecrawl's p50 on the same run was 2305 ms, so both open engines feel quicker on the typical page. If your workload is dominated by the median, these two are a wash on speed and the recall gap is the deciding factor.
p90: fastCRW 4348 ms (fast mode) vs Crawl4AI 4754 ms
In fast mode, fastCRW's p90 is 4348 ms — the lowest of the three tools tested (Crawl4AI 4754 ms, Firecrawl 6937 ms). The chrome-stealth fallback that recovers stubborn URLs produces a longer tail on the hardest pages, but fast mode keeps the 90th percentile well below Crawl4AI's. For synchronous agent loops where every call blocks, set a per-call timeout informed by the p90 and let the agent re-plan on a slow fetch rather than stall.
Where Crawl4AI fits
For teams that need a Python-native library they can import directly rather than call over HTTP, Crawl4AI's in-process ergonomics are a genuine advantage. It also carries permissive licensing (no AGPL consideration) and a large community of examples.
Why its recall is lower (less aggressive recovery)
The mechanism: fastCRW's higher recall comes from the same place as its fast-mode p90 lead. When a lighter renderer fails to extract the labeled content, fastCRW's auto renderer escalates through a chrome → lightpanda → http fallback to recover content that thinner approaches miss. That recovery is exactly why fastCRW's truth-recall is highest. Crawl4AI does not push as hard on those stubborn URLs, so it gives up some recall (59.95% vs 63.74%). The relationship is causal, not incidental.
Where fastCRW wins
Three axes beyond the recall headline, all from the same run.
Highest truth-recall and scrape-success
fastCRW leads recall (63.74% vs 59.95% of 819 labeled) and also leads scrape-success: ~92% of reachable URLs versus Crawl4AI's 83.5% (835 of 1,000), with zero thrown errors across all 3,000 requests for both engines. We always pair "0 errors" with the success rate — 0 errors alone would overstate the result, because an error-free run can still quietly skip URLs. So the honest pairing is: fastCRW recovered more of the reachable corpus with 0 thrown errors, and of the labeled subset recalled 63.74% of the ground truth — the highest of the three.
Fastest median and lowest fast-mode p90
fastCRW's p50 is 1914 ms — two milliseconds faster than Crawl4AI's 1916 ms — and in fast mode the p90 is 4348 ms, the lowest of the three (Crawl4AI 4754 ms, Firecrawl 6937 ms). For most batch and concurrency-heavy workloads, fast mode is the right configuration, and its tail is the most comfortable of the three.
Firecrawl-compatible drop-in API
Beyond the numbers, fastCRW exposes a Firecrawl-compatible REST surface (/v1/scrape, /v1/crawl, /v1/map, /v1/search) — the official Firecrawl SDK works against it after a base-URL swap, no rewrite. It ships as a single static Rust binary (~8 MB image, one container) under AGPL-3.0, so self-hosting is one docker run rather than a multi-service stack. Crawl4AI is a capable open-source Python library with its own idioms; if you have standardized on the Firecrawl API shape, fastCRW slots in without touching your client code. Screenshots (via CDP) and batch extraction across up to 50 URLs in one /v1/extract call are both supported; the engine is stateless per request.
Picking between them
This is a genuine trade-off, not a verdict. Choose by the shape of your latency budget, not by a headline.
Python-native embedding: Crawl4AI
If your whole stack is Python and you want scraping in-process as a library, Crawl4AI fits that pattern. Set a per-call timeout informed by the p90 and let the orchestrator re-plan on slow fetches rather than stall indefinitely — see our write-up on honest tail latency for RAG agents.
Recall-first extraction and API compatibility: fastCRW
If you are building a RAG index or a structured-extraction pipeline where a missed page is a silent quality bug, recall is the metric that compounds — and fastCRW's +3.79 pp edge, higher scrape-success, and faster fast-mode p90 mean fewer gaps and fewer re-scrapes. Add a drop-in Firecrawl-compatible API and a single-binary self-host story, and fastCRW is the fit for most extraction-heavy batch work.
Sources
- fastCRW canonical benchmark (3-way,
diagnose_3way.py, Firecrawl publicscrape-content-dataset-v1, 819 labeled of 1,000 URLs, 3,000 requests, single run 2026-05-08): see /benchmarks and our benchmark write-up. - Crawl4AI project: github.com/unclecode/crawl4ai (verified 2026-05-18).
- fastCRW repo and docs: github.com/us/crw · fastcrw.com.
Related: What truth-recall actually measures · fastCRW vs Crawl4AI · Crawl4AI alternatives · Firecrawl vs Crawl4AI vs fastCRW · p50 vs p90 vs p99 latency