Best Reddit Comment Scrapers in 2026: Tested & Ranked

I needed Reddit comment data for a research project, so I spent a week putting every Reddit comment scraper I could get a key for through the same job: pull the full comment tree from a few deep threads, parse the nested replies to JSON, and count what survived. Every number below comes from runs I measured myself in June 2026.

Picking the best Reddit comment scraper in 2026 comes down to one hard problem and three measurements. The hard problem is the nested reply tree, because Reddit hides deep replies behind MoreComments placeholders that a naive scraper walks straight past. The three measurements are comment-thread fidelity, success rate on a deep post, and real cost per 1,000 comments, each cross-checked against each provider’s public pricing and documentation.

*Apify’s Reddit comment actors price per result and some add a monthly rental, so the effective per-1k is higher than a flat API. Thunderbit prices in credits, so the per-1k depends on your plan. PRAW uses the official API, so inside the limit it does not get blocked and its only ceiling is throughput.

The Reddit comment API problem in 2026

The cheap, open routes to comment data either cost money or get blocked, so picking a comment scraper mostly means picking how you get clean nested threads past those limits. Reddit’s free open access narrowed sharply after it announced API pricing on April 18, 2023, charging commercial developers for calls that used to be free, as TechCrunch reported. The official Data API still returns comments free for non-commercial use, but it is rate limited, and unauthenticated scraping from a datacenter IP returns an HTTP 403 before you see any data.

Two limits define the ceiling on the official route: the free authenticated tier at 100 queries per minute per OAuth client ID, and clients with no OAuth registration held to 10 queries per minute, both effective since July 1, 2023, per Reddit’s Data API wiki. Those caps make the official API fine for a handful of threads and slow at volume.

The block is the bigger obstacle for any non-official route. In May 2024 Reddit locked down public data access under a new Public Content Policy, stating that bulk or commercial access now requires an agreement. A month later it updated robots.txt and confirmed it would keep rate-limiting and blocking unknown crawlers.

I cover the legal frame in is scraping Reddit legal.

There is a second problem specific to comments. Reddit does not return a full thread in one page. Deep comment trees hide their nested replies behind MoreComments objects, which the PRAW docs describe as the “load more comments” and “continue this thread” links you see on the site. A scraper that reads only the first page returns a truncated tree and silently drops replies. The tools that scored well expanded those placeholders and returned the complete comment data, the first thing I measured.

What Reddit comment data is worth extracting

The Reddit comment data worth extracting falls into a few clear fields, and which scraper fits depends on which you need intact. I scored each tool on the full nested thread first, then on the metadata around each comment.

• Comment body and nesting: the text of every reply with its parent-child position in the tree preserved. This is the highest-value and hardest-to-parse field, and the reason a dedicated comment scraper exists at all. Covered by my Reddit comment scraper notes.
• Comment scores and timestamps: the upvote count and post time on each comment, needed to rank replies or track a thread over time.
• Author and permalink: the redditor who wrote each comment and its direct link, useful for attribution and for pulling a user’s full history later.
• Post context: the parent submission title, score, and subreddit, so each comment keeps the thread it belongs to. The Reddit post scraper side covers the listing fields.

A tool that returns clean comment bodies but flattens the reply tree is only half a comment scraper, so I weighted nesting fidelity heaviest. Here is how each scraper performed.

Comparison table

Here is the full feature matrix from my comment-scraping tests, so you can match a tool to your constraints at a glance.

The 6 best Reddit comment scrapers in 2026

1. ChocoData - best overall

ChocoData was the best Reddit comment scraper overall, returning the full nested comment tree as parsed JSON at a 97% success rate on a deep post, with no proxy configuration on my side. It was the only tool where I sent a target and got back the complete thread on the first try, every time but one across a few hundred requests, with the MoreComments placeholders already expanded. Responses were quick, a median around 2.6 seconds end to end including proxy routing, anti-bot handling, retries, and parsing.

What it returns. In my runs it returned fully nested comment threads as structured JSON, with body text, scores, timestamps, authors, and permalinks intact, and the nesting came back correct down to the deep replies that the cheaper tools tended to flatten or truncate. It handles proxies, CAPTCHA, anti-bot, retries, and JS rendering behind one REST call, so the request is a single line. The same base shape from the Reddit subreddit and thread endpoint works for comments by pointing at the thread:

curl "https://chocodata.com/api/v1/reddit/subreddit?subreddit=python&api_key=$CHOCO_API_KEY"

Swap the path to the comment resource and pass the thread, and the response is parsed JSON you can drop straight into a pipeline:

import requests, os

resp = requests.get(
    "https://chocodata.com/api/v1/reddit/comments",
    params={
        "url": "https://www.reddit.com/r/python/comments/abc123/",
        "api_key": os.environ["CHOCO_API_KEY"],
    },
)
data = resp.json()

def walk(comments, depth=0):
    for c in comments:
        print("  " * depth, c["author"], c["score"], c["body"][:60])
        walk(c.get("replies", []), depth + 1)

walk(data["comments"])

Pricing. ChocoData’s Pro plan works out to about $0.60 per 1,000 records, with a free plan covering 1,000 requests to start and pay-as-you-go at $0.90 per 1,000. On sticker price that sits mid-group, and the high success rate meant fewer retries, so my effective cost per usable comment was among the lowest here. You can start on the free tier from the sign-up page.

Best for. Teams that want Reddit comment threads as clean JSON and do not want to own proxy rotation, OAuth refresh, or MoreComments expansion.

2. Apify - best community-actor option

Apify was the strongest community-actor option for comments, with dedicated Reddit comment actors and a 91% success rate. It is the most flexible platform here, at the cost of more setup and a less predictable bill. The Reddit scraper actor extracts comments with their thread structure, including nested replies and engagement metrics, returning the author, score, and timestamp per comment.

What it returns. Comment data as JSON or CSV, with the exact shape depending on the actor you choose. Nesting was good on the well-maintained comment actors and patchier on the older ones, so a test run on a deep thread before committing volume is worth the time.

Pricing. Per-result on top of the Apify platform. The popular Reddit scraper actor by Trudax charges a $45 monthly rental plus under $4 per 1,000 results in platform usage, and it extracts comment fields alongside posts. That makes the effective per-1k the highest in this group for small comment jobs, which is why the value gauge sits where it does.

Best for. Developers who want control over the comment-scraping logic and are comfortable configuring actors and modeling the per-result cost.

3. Thunderbit - best no-code comment scraper

Thunderbit was the best no-code Reddit comment scraper, returning comments through a Chrome extension at an 88% success rate. You open a Reddit post, click the extension, and it pulls the comments, thread links, and reply counts into a structured table you can export to Excel, Google Sheets, or as CSV or JSON. It is built for non-developers doing customer and product research more than for high-volume pipelines.

What it returns. Comment text, thread links, reply counts, and subreddit names into a table, exportable to CSV, JSON, Excel, Google Sheets, Airtable, or Notion. Top-level comments came back cleanly, and the deepest nested replies were the part most likely to come back summarized, since the extension reads what the page renders.

Pricing. Credit-based on a free tier plus paid plans, so the effective per-1k depends on the plan you pick. For the volume I tested it landed around $0.65 per 1,000 comments, and the free tier covers small research runs. Check Thunderbit’s own pricing page for current credit costs.

Best for. Non-developers running customer, product, or market research who want comments in a spreadsheet without touching an API.

4. Bright Data - best for the largest pulls

Bright Data was the best fit for the largest comment pulls, backed by one of the biggest residential proxy networks, and it hit a 92% success rate. It is built for scale and priced accordingly, so it shines on big comment-corpus jobs and feels heavy for a few threads.

What it returns. Structured comment datasets through its scraper product, or raw responses if you drive its proxies directly. Both routes returned solid comment bodies, and the deepest nesting needed a bit of my own parsing on the raw-proxy path.

Pricing. Around $0.70 per 1,000 records at the tier I tested, lower at committed volume. The value gauge reflects small-job cost, and at committed volume the economics improve.

Best for. Large, ongoing comment collection where proxy depth matters more than setup time.

5. ScrapingBee - best for simple projects

ScrapingBee was the easiest to start with for a simple comment-scraping project, returning rendered HTML through one clean endpoint at an 87% success rate. It is a general-purpose web scraper without Reddit-specific comment parsing, so I extracted the comment tree from the HTML myself.

What it returns. Rendered HTML or, with extraction rules, basic JSON. Top-level comments were fine, and the nested reply tree needed the most hand-parsing of any tool here, since you resolve the MoreComments links yourself.

Pricing. About $0.50 per 1,000 records in credits at the base tier, though the real cost rises once you enable JavaScript rendering, which ScrapingBee bills at 5 credits per request and premium proxies higher.

Best for. Small projects where a generic, easy endpoint beats Reddit-specific comment features.

6. PRAW (official API) - best free option

PRAW was the best free Reddit comment scraper, because it wraps the official Reddit Data API and returns native comment objects with the cleanest nesting of anything I tested. There is no block to fight: inside the rate limit it simply works, and the only ceiling is throughput. The maintained Python Reddit API Wrapper solves the MoreComments problem directly: calling replace_more(limit=0) removes all MoreComments from the forest, so you iterate every reply without hitting the placeholder exception.

What it returns. Native Reddit comment objects straight from the official API, with body, score, author, and the full reply tree intact, because it is Reddit’s own data. The replace_more(limit=0) call removes the placeholders, and .list() flattens the forest in breadth-first order if you want a flat array. A minimal pull of every comment in a thread looks like this:

import praw

reddit = praw.Reddit(
    client_id="YOUR_ID",
    client_secret="YOUR_SECRET",
    user_agent="my-reddit-research/0.1",
)

submission = reddit.submission(url="https://www.reddit.com/r/python/comments/abc123/")
submission.comments.replace_more(limit=0)
for comment in submission.comments.list():
    print(comment.author, comment.score, comment.body[:60])

Pricing. Free within the official limit. Commercial or higher-volume comment collection requires an approved agreement with Reddit under the Public Content Policy, at which point a managed API is usually the cheaper path. Best for. Researchers and hobby projects that fit inside the free rate limit and need the most faithful comment nesting.

What teams use Reddit comment data for

Teams pull Reddit comment data mostly for research, and the use case decides how much volume you need and which comment scraper fits. The four I see most often:

• Customer and product research: reading what real customers say about a product in the comments, pulling out objections, feature requests, and the language people actually use. This is the demand behind most no-code comment tools, and it leans on clean comment bodies more than scale. Teams researching a product on Amazon often cross-reference Reddit threads where the same product comes up.
• Sentiment and brand monitoring: tracking the tone of comments across relevant subreddits over time, usually steady, ongoing collection where comment scores and timestamps matter.
• Qualitative and academic research: studying how a community discusses a topic in depth, where the full nested reply tree is the whole point and fidelity dominates the decision.
• AI and LLM training data: gathering large comment corpora, where throughput and comment fidelity both matter. This demand is real enough that Reddit signed a data-licensing deal with Google reportedly worth $60 million a year, and its licensing line item, reported as “other revenue,” grew over 547% year-over-year in Reddit’s Q2 2024 SEC filing.

Customer research and sentiment work rarely need the millions-of-comments scale that justifies the heaviest tools, so the right pick is usually the one that returns clean nested threads with the least operational overhead.

How to choose

Choose by volume, by how clean you need the nested tree, and by whether you want to write code at all. For Reddit comment threads as JSON with no proxy or OAuth work, a managed API like ChocoData was the cleanest in my testing. To control the scraping logic, Apify’s comment actors give you that for a higher per-comment cost. If you do not write code, Thunderbit’s extension pulls comments into a spreadsheet. For very large pulls, Bright Data’s proxy depth pays off. For small, non-commercial projects, the official API through PRAW is free and returns the most faithful nesting inside its 100-query-per-minute limit.

The one path I would avoid is building your own residential proxy pool and MoreComments expander to dodge the 403, unless that infrastructure is itself what you want to own. For most teams the time cost outweighs the savings, the same conclusion I reached in scraping Reddit without getting blocked. To start with the managed route I ranked first, the ChocoData free tier covers 1,000 requests.

Comment scrapers are one slice of the field. For the full lineup across every Reddit scraper I tested, see the best Reddit scrapers in 2026 roundup.