2026-09-17, updated 2026-10-26, ChatGPT · GEO · Fundamentals

Best ChatGPT scraper APIs for 2027: answers and citations

To get what ChatGPT tells users, with the pages it cites, as JSON, there are three routes: a scraper API that collects and parses the consumer answer, OpenAI’s developer API with its web search tool, or your own collector. Only the first and third return the consumer answer. Scraper APIs differ in which parts of the answer they parse (citations, the searches ChatGPT ran, shopping cards, ads), how you target a market, and how you pay.

Facts about each vendor come from its own public pages, linked and checked on 2026-10-26; “not documented”, “not stated” and “none described” mean not found on those pages on that date. Re-check prices before you commit.

Comparison

Two terms used below:

• Citation: a source the answer links to, in the order the interface shows it. A mention is a brand named in the text without a link. See mentions vs citations.
• Query fan-out: the rewritten web searches ChatGPT runs before answering; its cited sources come from what those searches return. See query fan-out.

What to measure first

Decide the metrics before comparing vendors. Each depends on a specific part of the response:

1. Mention rate: does the answer name your brand? Needs clean answer text, which every option provides.
2. Citation share: is your domain among the sources, and at what position? Needs a sources array in display order, not URLs pulled out of the markdown.
3. Retrieval analysis, the searches that fed the answer, needs the fan-out queries. Without them you see which pages won but not on which query.
4. Commerce visibility, whether a product card showed your SKU, at what price and from which merchant, needs parsed shopping blocks.
5. Market variation: does the answer differ in Germany, or in Texas? Needs country, and sometimes sub-country, targeting.

For the first metric almost any option works, including a cheap actor. Metrics three to five narrow the list to a few vendors.

The options

Bright Data ChatGPT Scraper

Bright Data sells AI scrapers as part of its Web Scraper API. Its product page lists ChatGPT output fields including prompt, answer HTML, answer text, links attached, citations, recommendations and country, with sibling scrapers for Google AI Mode, Perplexity, Gemini and Bing Copilot. Its AI scrapers documentation says ChatGPT supports citations, follow-up prompts and detection of shopping and map results, and takes a web_search input with a web_search_triggered output confirming whether a search ran. The same page marks Grok as currently unavailable.

A synchronous /scrape call is meant for 1 input per request and returns HTTP 202 with a snapshot_id if collection takes over 1 minute; an asynchronous /trigger handles batch jobs, with delivery by API, webhook or cloud storage.

Pricing (checked 2026-10-26): 5K records a month free, $1.50 per 1K records pay as you go, a Scale plan at $499 a month with 384,000 records and $1.30 per 1K beyond. Only successfully delivered records are billed.

Each engine is a separate scraper with its own output fields, so you write a mapping per engine, and async jobs return snapshots you collect later.

Fits data teams already on Bright Data, or buyers who want bulk datasets delivered to storage.

DataForSEO LLM Scraper

DataForSEO’s AI Optimization API contains four products; two are relevant. LLM Scraper collects ChatGPT and Gemini results as users see them. LLM Responses calls the official model APIs of ChatGPT, Gemini, Claude and Perplexity, which measures something different (see the OpenAI section below).

The ChatGPT LLM Scraper live advanced endpoint takes a keyword, a location, a language and an optional force_web_search, and returns markdown, sources (the references cited), search_results (everything retrieved), fan_out_queries, brand_entities, and typed items including chat_gpt_products, chat_gpt_local_businesses and chat_gpt_ad.

Pricing (checked 2026-10-26): per results page, $0.0012 in the standard queue (up to 45 minutes), $0.0024 in the priority queue (up to 5 minutes), $0.004 live (up to 90 seconds). The AI optimization page states a $50 minimum top-up and that credits never expire; new accounts get $1 of trial credit.

Its $0.0012 standard-queue price comes with fan-out, brand entities and products in the schema. The scraper side covers two engines. That price is a queue with turnaround in minutes; live use is $0.004. The response follows DataForSEO’s SERP-style item model (rank_group, rank_absolute), which suits SEO tooling and needs mapping elsewhere.

Fits SEO platforms already built on DataForSEO that want ChatGPT and Gemini data priced per results page and can tolerate queue latency.

Oxylabs Web Scraper API

The ChatGPT target docs describe source: "chatgpt" with a prompt (under 4,000 characters), search: true for web search (default false), geo_location for the country, and parse: true for structured output. Parsed fields: response_text, markdown_text, markdown_json, citations, links, shopping_products (with offers), ads and raw_response. The docs list no fan-out field. Results use Oxylabs’ push-pull model with an optional callback_url. The docs have separate targets for Perplexity and Gemini, and the pricing page also lists Google AI Mode.

Pricing (checked 2026-10-26, pricing page): $5 of welcome credits toward any product; Starter is $30 a month. ChatGPT results cost $1.50 per 1K on the first 4 plan columns, so Starter covers up to 20K ChatGPT results.

Citations, products and ads come in one parsed result, with callbacks. Web search is off unless you set search, which changes what you measure if you forget it. Plans are subscriptions with rates that vary by target.

Fits teams already using Oxylabs for other scraping who want AI engines under the same contract.

SearchApi

SearchApi’s ChatGPT API, per its page, returns the reply as Markdown and typed text blocks, reference links with title, URL, source, snippet and publish date, inline citations mapped by character position, the web search queries ChatGPT ran, and entity cards. It also lists Perplexity, Gemini, Bing Copilot, Google AI Mode and Google AI Overview APIs.

Pricing (checked 2026-10-26, pricing): 100 free requests; Developer is $40 a month for 10,000 searches ($4 per 1K), falling to $1 per 1K at the 5M tier. The ChatGPT page says you pay only for successful answers.

Requests are simple GETs at a flat per-search price, with fan-out and positional citations. The ChatGPT page did not describe shopping cards or ads (checked 2026-10-26), and tiers commit you to a monthly volume.

Fits teams already using SearchApi for Google results who want ChatGPT under the same key.

Apify Store actors

Apify is a marketplace, so “ChatGPT scraper” covers several actors with different owners and prices. Apify’s own ChatGPT Search Scraper returns text and sources (each with a section: citations, attached or more) and takes a countryCode; its page lists no fan-out or product fields. It is priced from $3.00 per 1,000 searches per event, lower on higher Apify subscriptions. Community actors exist too; Korado Labs’ ChatGPT Search Scraper charges $0.15 per answer and adds brand mention checks. Apify’s free plan includes $5 of monthly platform credit; Starter is $19 a month.

Actors are quick to try from a UI, integrate with Zapier, Make and Google Sheets, and bill per result. Each actor has its own schema, maintainer and failure modes, so switching actors means remapping fields, and prices vary widely: the two ChatGPT actors above are $3.00 and $150.00 per 1,000 answers.

Fits prototypes, one-off studies and no-code teams.

ScrapingBee ChatGPT API

ScrapingBee’s ChatGPT API returns results_markdown, results_text, results_json, llm_model, citations (title, URL, text and description), ads and, with search=true, search_queries. It takes country_code for geolocation. Its documentation says it is “not able to return citations or ads 100% of the time”.

Pricing (checked 2026-10-26): 15 credits per successful call; 1,000 free trial credits; Hobby is $19 a month for 75,000 credits, or 5,000 ChatGPT calls.

It is convenient if you already use ScrapingBee. Its documentation listed no shopping or product fields (checked 2026-10-26), and answers without citations lower citation share unless you filter them. ScrapingBee has been part of the Oxylabs group since June 2025 and says it keeps operating as a separate product and entity (Proxyway, ScrapingBee, checked 2026-10-26).

Fits teams already on ScrapingBee who can accept some answers without citations.

AnswerLine

AnswerLine has one endpoint per engine. POST /v1/monitor/chatgpt takes a prompt (up to 2,000 characters) and a country and returns the ChatGPT answer parsed into fields.

curl -X POST https://api.answerline.dev/v1/monitor/chatgpt \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "What is the best CRM for a 20-person agency?",
    "country": "US",
    "include": { "markdown": true, "shopping": true }
  }'

Fields under result, per the API reference:

• text and, when requested, markdown.
• sources[]: each with position, url, label, description, datePublished, and footnote for sources that appear only in the sources list.
• citationPills[]: the inline citation chips, one entry per pill-and-source pair, grouped by citationPillId.
• searchQueries[]: the query fan-out field, free with include.searchQueries; it comes back empty on the answers served today.
• inlineProducts[] and shoppingCards[], when include.shopping is true. inlineProducts[] carries the products from ChatGPT’s shopping search, with price, link and merchant offers; shoppingCards[] comes back empty today.
• ads[]: sponsored units with a rendered flag, when include.ads is true; it comes back empty on the answers served today.
• entities[] (the brands and products the answer names, with type and name) and map[].

ChatGPT decides on its own whether to search; disableWebSearch changes nothing today, and provenance.webSearchTriggered is true when the answer cites sources. ChatGPT answers from 213 countries; a US state is refused with a 400.

A ChatGPT task costs $0.0006 at the Starter tier ($0.42–$0.60 per 1,000 across tiers). markdown, searchQueries, ads, shopping, rawResponse and include.html, a link to a copy of the ChatGPT page, are free. A new account’s 100 free requests cover 100 ChatGPT tasks, every tier’s prices are on /pricing, and failed requests are not charged (pricing docs).

Named products, ordered sources and citation pills come in one response, with the same request and response conventions across ChatGPT, Gemini, Perplexity, Copilot, Grok, AI Mode, Google Search and Google News. Async batches take up to 500 tasks with webhooks and idempotency keys. Credits are per request, so a very large one-off census costs more than the cheapest per-page queues above.

Fits teams building AI-visibility, GEO or commerce products that need the parsed answer across several assistants.

OpenAI’s API with web search

OpenAI’s web search tool (web_search in the Responses API) returns url_citation annotations with the URL, title and location of each cited source, a sources list of every URL retrieved, and the search actions with their queries, and it accepts a user_location with country, city, region and timezone. Pricing is $10 per 1,000 calls plus search content tokens at model rates.

It fits generating answers inside your own application. For measuring what ChatGPT tells people, three differences matter:

1. You pick the model, instructions and tool settings, so the output describes your configuration, not the app a buyer uses.
2. The guide (checked 2026-10-26) describes no shopping cards, product carousels or ads, where commerce visibility is decided in the app.
3. Citation presentation (which sources are grouped in which inline chip, in what order) is an interface decision; the API docs describe citations as annotations on the model’s own text.

The provider APIs comparison goes through the differences field by field.

Building it yourself

A home-grown collector means collection in every market you report on, a queue, a parser for the answer, retries, and monitoring for the day the interface changes. The build vs buy breakdown itemizes the cost. Build only if collection is your product, or if you need something no vendor offers, such as logged-in personas with memory.

How to choose

1. Do you need the consumer answer? For visibility measurement, yes, which rules out model APIs.
2. Which fields will you compute on? Citation share needs ordered sources, retrieval analysis needs fan-out, commerce needs parsed products. Check each vendor’s schema for the exact field.
3. How many engines? If Perplexity, Gemini, Copilot and Google AI surfaces are on your roadmap, one contract across them saves a mapping layer per engine.
4. Set the latency you can accept. Queues are cheapest. Daily monitoring rarely needs live calls; agents and interactive tools do.
5. How do you target markets? Country is common; city, state or coordinates are not.
6. What happens on failure? Look for success-only billing, idempotency and webhooks. A daily job that double-runs or drops tasks corrupts trend lines.
7. Price at your volume with your flags: prompt count × markets × frequency, including add-ons.

Cost example

200 prompts, one market, collected daily for 30 days, with citations and fan-out: 6,000 ChatGPT answers a month. List prices checked 2026-10-26, before taxes, volume discounts and coupons. Recurring monthly free allowances (Bright Data’s 5K records, Apify’s $5 credit) are netted; one-time trials (SearchApi’s 100 searches, ScrapingBee’s 1,000 credits, Oxylabs’ $5 welcome credits, AnswerLine’s 100 free requests) are not, since they do not renew:

The lowest line is not necessarily the lowest total cost once you add the engineering time to normalize each vendor’s schema, the cost of a missed day when a queue backs up, and a possible second vendor for the next engine. Budgeting across engines and cadences is covered in AI monitoring cost planning.

Daily ChatGPT collection on AnswerLine

For a daily prompt set, submit async tasks in batches and receive results on a webhook:

import os, datetime, requests

API = "https://api.answerline.dev"
HEADERS = {"Authorization": f"Bearer {os.environ['API_KEY']}"}
today = datetime.date.today().isoformat()

prompts = ["best CRM for agencies", "HubSpot vs Pipedrive for small teams"]
tasks = [
    {
        "taskType": "CHATGPT",
        "payload": {"prompt": p, "country": "US"},
        "idempotencyKey": f"crm-{i}-US-{today}",
        "webhook": {"url": "https://example.com/hooks/answers"},
    }
    for i, p in enumerate(prompts)
]

r = requests.post(f"{API}/v1/async/task/batch", json=tasks, headers=HEADERS, timeout=30)
r.raise_for_status()
for item in r.json()["results"]:
    if not item["success"]:
        print(item["index"], item["error"]["code"])

Each finished task is POSTed to the webhook; for a COMPLETED task the ChatGPT result is at response.result. Idempotency keys derived from prompt, market and day make a retried submission safe. Verify the webhook signature before trusting the body.

Pitfalls that skew ChatGPT data

• Answers without search. Some APIs only search when asked, and ChatGPT decides on its own. An answer with no sources showed no search: compute citation share over answers with sources and report the share without.
• One run per prompt. ChatGPT answers vary run to run, so track rates over repeated runs; see AI answer volatility.
• Treating markdown links as citations. Links pulled from text lose display order and grouping. Use the structured sources.
• Ignoring commerce signals. With include.shopping, inlineProducts[] carries the products from ChatGPT’s shopping search, and entities[] lists the products an answer names; see ChatGPT shopping cards.
• Assuming every field is always filled. Optional fields such as fan-out can be empty for a given answer; store which fields were present with each result.

Field-by-field detail is on the ChatGPT engine page, and the quickstart covers a first request.

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