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Choose a source by the job it needs to do
First decide whether you need a broad web-text foundation, a more filtered corpus, or material concentrated in a particular language or domain. Then compare the corpus’s snapshot dates, processing, source mix, documentation and terms. A large token count is not itself a quality score, and a dataset card’s license label does not necessarily resolve the terms attached to every underlying document.
| Need | Good starting points | Main trade-off to examine |
|---|---|---|
| Raw web coverage and control over processing | Common Crawl | You take on extraction, filtering, deduplication and auditing. |
| Processed general web text | FineWeb, C4, RedPajama-Data-V2, DCLM-Baseline, RefinedWeb | Filtering recipes, dates, available variants and terms differ. |
| Broader languages or a specific domain | FineWeb-2, mC4, The Stack v2, scholarly or Wikimedia sources | Verify the current language mix, release and source-level permissions. |
| A blend beyond web pages | Dolma, The Pile, or a deliberately assembled mixture | Mixed sources bring different ages, provenance and component terms. |
For a web-heavy pretraining run, compare processed datasets using the same held-out evaluation plan and contamination checks. For a specialist model, a smaller, well-matched source can be more useful than adding undifferentiated web volume. FineWeb maintainers report aggregate benchmark comparisons favoring FineWeb over several commonly used open datasets, but that is their stated result, not an independent universal ranking; benchmark outcomes depend on the evaluation setup.
13 web data sources to evaluate
1. Common Crawl
Common Crawl is a broad archive of web crawls, not a ready-made, uniformly cleaned training corpus. It suits teams that want to choose snapshots and build their own extraction, deduplication, filtering and auditing pipeline. Common Crawl says its data is stored on AWS Public Data Sets and academic cloud platforms. That hosting can help with access planning, but it does not remove the engineering or storage work of selecting and processing data.
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2. FineWeb
FineWeb is an English-language corpus derived from Common Crawl with documented filtering and deduplication. Its dataset card, viewed in 2026, reports more than 18.5 trillion tokens from 96 Common Crawl dumps spanning summer 2013 through April 2024, and lists ODC-By 1.0. The card describes FineWeb as a research artifact and documents limitations. Its reported scale should not be confused with fresh coverage through 2026: the listed crawl period ends in April 2024. Review the card’s limitations and provenance notes alongside its top-level terms.
3. FineWeb-Edu
FineWeb-Edu is an education-oriented subset of FineWeb. It is a candidate when educational material is central to the model, but do not infer its present release size, recipe or terms from the parent corpus. Check the current dataset card and its release-specific documentation before selecting it.
4. FineWeb-2
FineWeb-2 extends the processing approach toward multilingual web data. Its 2025 paper reports a 20-terabyte, five-billion-document dataset covering more than 1,000 languages. Those are paper-reported figures, not a guarantee that the live release has the same size or language distribution today. Inspect the current release and confirm that its actual coverage includes the languages your evaluation requires.
5. C4 and mC4
C4 and its multilingual counterpart mC4 are cleaned Common Crawl corpora. C4 includes English variants, while mC4 offers multilingual subsets. The available variants differ materially in filtering, including filtered and less-filtered choices. Select a specific variant rather than treating “C4” as one interchangeable dataset, and check the dataset card for the characteristics and terms of that variant.
Rank #2
6. Dolma
Dolma combines more than web text: its source categories include web data, academic publications, code, books and encyclopedic material. AI2’s dataset card describes a three-trillion-token dataset, with v1.7 source-level statistics drawing on varied data types, and lists ODC-BY release terms. The card also says original source terms apply. That makes source-level review especially important: do not treat the aggregate license as a substitute for understanding component terms.
7. RedPajama-Data-V2
Together Computer’s live dataset card, checked in 2026, describes 84 Common Crawl snapshots and more than 100 billion documents. It reports quality signals for 30 billion documents and duplicate identifiers that can be used to form a 20-billion-document deduplicated collection. The listed languages are English, German, French, Spanish and Italian. These figures come from the card’s 2023 release documentation; verify the current release and data layout before planning a pipeline around them.
8. RefinedWeb
RefinedWeb is a Common Crawl-derived corpus associated with Falcon training. It is worth comparing with FineWeb and other derivatives when you want a documented filtering approach. Before depending on it, establish which snapshot scope, hosted release and access terms are currently available: a current hosted release was not established in the material available for this shortlist.
9. DCLM-Baseline
DCLM-Baseline is a research-documented Common Crawl-derived baseline for general web pretraining. Comparative research identifies it alongside FineWeb, C4, RefinedWeb, DolmaCC and RedPajama-V2. Treat that comparison as a reason to investigate the baseline, not proof that a particular current release is the best fit. Verify the live release, processing details and use terms.
10. The Stack v2
The Stack v2 is a code-focused source family for training code models or adding code to a broader mixture. Its repository-level rights and metadata need separate attention: code repositories can have heterogeneous licenses, and opt-out or removal policies matter to dataset maintenance. Current release details were not established here, so confirm the exact version, repository metadata and applicable policies before use.
11. The Pile
The Pile is a mixed-source text corpus that may broaden a web-heavy training mixture. Its constituent sources, age and terms should be assessed individually. The aggregate corpus name does not establish that every component suits a current project or has identical permissions. FineWeb’s dataset card includes The Pile in its comparisons.
12. Wikimedia projects
Wikimedia project dumps can add encyclopedic and reference material, which is useful when factual and knowledge-heavy coverage is a priority. Treat them as a focused complement, not a substitute for broad web coverage. Select the relevant project dump and check its attribution and license requirements. Wikipedia and Wikibooks are also documented as source categories in Dolma.
13. arXiv and scholarly corpora, including S2ORC and peS2o
Scientific and technical papers can complement general web data for research-heavy models. Confirm the exact corpus version, access conditions and publisher rights for the included material; availability of a corpus does not settle the rights to every paper within it. Dolma documents academic publication sources that include peS2o.
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How to compare candidate datasets before committing
Match language and domain to the evaluation target
Write down the languages, subject areas and output behavior the model must support, then check actual dataset coverage rather than relying on labels such as “multilingual” or “educational.” FineWeb is English-focused; FineWeb-2’s paper describes broad multilingual coverage; RedPajama-Data-V2 lists five languages. Code, scholarship and encyclopedic data are specialized additions, not automatic improvements to every general-purpose mixture.
Inspect preprocessing and provenance
Record what has been extracted, filtered and deduplicated, and which decisions remain yours. With raw archives such as Common Crawl, those choices are largely pipeline work. With processed derivatives, inspect the documented recipe, variants, snapshot period and available metadata. Preserve source identifiers and processing records so you can explain what entered a training run and act on corrections or removals.
Review permissions at both dataset and source level
Read the current license and terms for the particular release, then look for component-level terms, attribution obligations, commercial-use conditions, personal or sensitive data documentation, and removal mechanisms. Dolma explicitly notes that original source terms apply. FineWeb documents limitations and social-impact concerns. Neither a public download nor a top-level license label alone establishes unrestricted use of all source documents.
Plan for freshness, reproducibility and infrastructure
Compare the dates of the underlying snapshots, not just the date a card was viewed. A very large archive may require substantial selection, storage and processing; a processed collection can reduce some curation work but still has a specific scope and recipe. Check hosting, formats and access instructions in each current project card. Pin versions and preserve a manifest of snapshots, filters and deduplication choices so later runs can be compared meaningfully.
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Where ScreenshotNeo fits—and where it does not
ScreenshotNeo is not a replacement for Common Crawl or a text corpus: a screenshot API returns rendered page images or PDFs, not a bulk, rights-cleared web-text dataset. It can be relevant when the training target specifically includes visual page appearance, rendered layouts or screenshot-based page understanding, and you need to capture selected URLs as images. It is a website screenshot API and MCP server for developers, made by Yorker Media. See ScreenshotNeo.
For a single capture, the API accepts a URL in a GET request. The example below saves a WebP screenshot of Stripe; replace the target URL as needed. Keep your API key private rather than embedding it in public client-side code. See the ScreenshotNeo API documentation for request options and response details.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpEquivalent Python and Node.js requests are:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);For a rendered-page collection, define a permitted URL set, choose capture settings consistently, and record the requested URL and response verdict with each asset. The API offers options including full-page capture with lazy images loaded, CSS-selector element capture, device and viewport settings, custom CSS or JavaScript, wait conditions, request blocking, headers and cookies, and PDF output. These options are useful for visual capture workflows, but they do not by themselves establish permission to collect or train on a site’s content.
ScreenshotNeo’s cookie/consent-banner handling, newsletter-popup and chat-widget removal can be turned off per step. It reports page verdict and billing status through response headers: bot checks or CAPTCHAs, blank pages, timeouts, failed loads and cache hits cost nothing. Its MCP server provides take_screenshot, get_page_info and capture_pdf for Claude, Cursor and other MCP clients. Plans include 1,000 free shots per month without a card; paid plans start at $5 for 3,000 shots. Every feature is available on every plan. These characteristics make it an alternative for targeted rendered captures, not for acquiring a conventional language-model training corpus.
Sign up for ScreenshotNeo to get 1,000 screenshots a month free with no card.
Common selection and collection mistakes
- Choosing by token count alone: Volume does not reveal snapshot recency, language balance, duplication, filtering quality or fit to the target task.
- Treating dataset labels as rights clearance: Review release and source terms, including attribution and commercial-use conditions, before training or redistribution.
- Mixing versions without a record: Pin the card/release and underlying snapshot period; otherwise a changed source or recipe can make comparisons hard to reproduce.
- Assuming processed means current: FineWeb’s reported data ends with April 2024 crawl coverage, despite its card being viewed in 2026.
- Confusing screenshots with text corpora: Rendered images are suitable only if the model objective needs visual webpage content; they do not replace text extraction and corpus curation.
Frequently Asked Questions
How should I check whether a dataset comparison is meaningful?
Compare candidates under the same training budget, data mixture policy, deduplication rules and held-out evaluation suite, and check for overlap between training and evaluation material. A benchmark result without its evaluation setup is not a reliable universal ordering.
Does a public dataset download mean I can redistribute a trained model commercially?
Not by itself. The result depends on the specific release terms, component sources, jurisdiction and intended use. Get legal review for consequential commercial deployments rather than treating public availability as permission.
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