Best Twitter (X) Scrapers in 2026: Benchmarked

We benchmarked the top Twitter (X) scrapers across 1000 URLs , for a total of 5000 requests. To help you choose the right tool for your Twitter scraping projects, we have categorized the top performers below.

Twitter (X) scrapers benchmark

Since all providers reached 100% success rate, we compared their completion time.

See our benchmark methodology for more details.

What data can be scraped from Twitter

When scraping a Twitter post you typically get one of two response formats: raw HTML (which you then parse with CSS selectors to pull the fields you need) or a ready-made JSON payload.

Bright Data was the only provider in our test that returned JSON, so the table below lists the metadata fields it exposes.

Price comparison of the top Twitter scrapers

Prices reflect each provider’s standard plan. Cost per 1K can vary across packages.

The best Twitter scraping tools

Bright Data was the fastest provider in the benchmark, finishing each tweet in about 4 seconds. Its response is structured JSON with 33 metadata fields per tweet (engagement counts, author profile, embedded media, parent/quoted post details, verification), so no client-side HTML parsing is needed.

Bright Data also offers other Twitter-specific scrapers and ready-made datasets:

• Posts, collect by URL: extracts data from a tweet by its URL. We used Posts, collect by URL for Twitter scrapers benchmark.
• Posts, discover by profile URL: collects all post URLs and their data from a profile
• Profiles, collect by URL: extracts profile details from a profile URL
• Profiles, discover by user name: collects profiles by user name 

Bright Data also offers ready-made social media datasets through its Dataset Marketplace, including pre-collected Twitter Posts and Profiles.

Oxylabs came in at around 15 seconds per tweet, placing it near the slower end of the group. Its Realtime API returns rendered HTML through the universal source, and engagement counts plus post text are recovered with CSS selectors on the client side.

Decodo was the slowest provider in Twitter scrapers benchmark, with each tweet taking about 16 seconds. Its universal v2 scraper returns the full server-side rendered HTML, which the client then parses for the four interaction counts (reply, repost, like, bookmark) and the view count.

Zyte returns a fully rendered page through its browserHtml endpoint in a single call. It was the second-fastest provider in the benchmark, completing each tweet in about 8 seconds. The output is HTML, so client-side parsing is still required to pull engagement counts and tweet text.

Nimble uses its vx10 stealth driver, which combines full JS rendering with anti-bot evasion. It took about 11 seconds per tweet on the benchmark. The response is HTML, parsed on the client side to extract engagement counts and post content.

See more of our benchmarks and data-driven insights in Google Search.

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Twitter scraper benchmark methodology

We tested five scraping providers (Bright Data, Decodo, Nimble, Oxylabs, Zyte) against a fixed dataset of 1,000 public Twitter posts. Each provider was given the same list of URLs and the result of each scrape was validated against the same ground truth.

Each request was sent with default settings; no provider-specific tuning or session reuse was applied.

What we measured

• Validation success rate: the share of requests where the scraped data matched the ground truth.
• End-to-end time: total seconds from request submission to result extraction, including async polling for providers that work in submit-then-poll mode.
• Total metadata fields: number of fields the provider returns per tweet.

Validation rules

Each scraped post is checked against six criteria:

1. URL: tweet ID extracted from the response must match the requested URL.
2. Description: at least three lowercase alphanumeric tokens must overlap between the scraped text and the ground-truth text. Skipped if the ground truth has fewer than three tokens.
3. Reply count: within tolerance of the ground-truth value.
4. Repost count: within tolerance.
5. Like count: within tolerance.
6. Bookmark count: within tolerance.

A post is marked valid if at least three of these six criteria pass. Criteria where the ground truth is null are skipped; criteria where the ground truth exists but the scraped value is missing are counted as failed.

Tolerance formula for counts

Engagement counts on Twitter are dynamic (likes and views keep climbing), and X.com itself rounds large numbers in the UI (“6K” instead of 6,121). To allow for these small differences, each count check uses the following tolerance:

Examples:

• N = 2 → tolerance ±3 (range [0, 5])
• N = 100 → tolerance ±10 (range [90, 110])
• N = 1,000 → tolerance ±50 (range [950, 1,050])
• N = 1,000,000 → tolerance ±50,000

This gives a wider relative margin for small counts and a tighter relative margin for large ones.

Status code validation

• HTTP 200: response is taken to extraction and validation.
• HTTP 201 to 399: counted as a success without content checks.
• HTTP 404: counted as a success (the provider correctly reported a missing page).
• Any other status code: counted as a failure.

FAQs

Cite this benchmark

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{ipi2026,
  author = {Şipi, Nazlı},
  title  = {{Best Twitter (X) Scrapers in 2026: Benchmarked}},
  year   = {2026},
  month  = jun,
  howpublished    = {\url{https://aimultiple.com/twitter-scraper}},
  note   = {AIMultiple. Retrieved June 25, 2026}
}

Reference Links

1.

Careers: Build AI That Advances Humanity | xAI

Nazlı Şipi

AI Researcher

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Nazlı is a data analyst at AIMultiple. She has prior experience in data analysis across various industries, where she worked on transforming complex datasets into actionable insights.

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