Gemini scraper API

What the Gemini API returns

In our study Gemini cited more pages per answer than ChatGPT or Google's AI, so its answers are long and its source lists longer. Each one comes back already read: the brands in order, every citation typed, and the raw response behind it.

Gemini vs ChatGPT: how they answer buyers

Gemini and ChatGPT are the two assistants people ask directly, and they build their answers differently. In our study of 2,381 answers to 300 software buyer questions:

Gemini and ChatGPT across 300 buyer questions

	Gemini	ChatGPT
Sources per answer	14.7	10.0
Cited pages that were roundups, comparisons or reviews	95%	41%
YouTube citations	63 of 8,628	1 of 5,986
Reddit citations	84 of 8,628	3 of 5,986
Cited sites shared by two answers to the same question	35%	50%

Gemini leans almost entirely on roundups and changes its sources more from one answer to the next, so a brand that isn't on the lists Gemini reads rarely gets named, and one Gemini answer is an even smaller sample than one ChatGPT answer. Read the study.

One question, three different answers

We asked “best CRM for early-stage startups” on each engine from the US on the same morning. Each card is what the API returned: how the answer opens, the brands it named in order, and the first sites it cited.

Where each answer ranks each brand (1 = named first; – = not named)

Brand	Gemini	ChatGPT	Google AI Mode
Salesforce	1 (to avoid)	– not named	– not named
Attio	2	1	1
Lightfield	3	– not named	– not named
Ahoy	4	– not named	– not named
HubSpot	5	2	2
Pipedrive	6	3	3
Streak	7	– not named	5
Zoho CRM	– not named	4	– not named
Close	– not named	5	4
Folk	– not named	– not named	6

All three name Attio, HubSpot and Pipedrive, but Gemini opens by naming Salesforce as the kind of CRM not to buy, puts HubSpot fifth, and is the only one to recommend two AI-native CRMs, Lightfield and Ahoy. It shares just two sources, attio.com and hubspot.com, with ChatGPT, and the same two with AI Mode.

The Gemini app's answer vs the Gemini API

Google's Gemini API, even with grounding in Google Search turned on, is a model you call as a developer: you choose the model, the prompt and the settings. Buyers ask the Gemini app. This API returns the app's answer, which is what they actually read.

What each one measures

	Gemini API with grounding	Gemini app, through aivisibilityskill
What you get	A model's reply to your prompt	The answer people see in the Gemini app
Sources	Grounding links you parse yourself	Every citation, in order, with its source type
Brands named	You parse them yourself	Listed with rank, recommendation and sentiment
Other engines	Gemini only	The same schema for 8 engines
Proof	None kept	Raw response with its SHA-256 hash

Track brand mentions in Gemini

Because Gemini's sources shift so much between answers, track it by asking repeatedly and counting. Put a brand, its competitors and its buyers' questions in a project with a daily or weekly schedule, then read how often Gemini names the brand (with error bars), each competitor's share, the sites Gemini cites and the history, from /v1/projects/{id}/visibility, /competitors, /sources and /history. A webhook tells your server when each run is done. Gemini and Google AI Mode often disagree, so most trackers watch both.

Call the Gemini API from TypeScript or Python

The SDKs wrap the same request, retry safely without charging twice, and wait for the answers for you. Free sandbox keys (aiv_test_…) return realistic sample answers, so you can build the whole feature before spending a credit.

TypeScript (npm install @aivisibilityskill/sdk)

import { Aiv } from "@aivisibilityskill/sdk";

const aiv = new Aiv({ apiKey: process.env.AIV_KEY! });

const job = await aiv.checks.create({
  questions: ["best CRM for early-stage startups"],
  engines: ["gemini"],
  locale: "en-US",
});
const done = await aiv.jobs.wait(job.id);

for (const answer of done.answers ?? []) {
  console.log(answer.brands?.map((b) => `${b.rank}. ${b.name}`));
  console.log(answer.citations?.map((c) => c.domain));
}

Python (pip install aivisibilityskill)

import os
from aivisibilityskill import Aiv

aiv = Aiv(api_key=os.environ["AIV_KEY"])

job = aiv.checks.create(
    ["best CRM for early-stage startups"],
    engines=["gemini"],
    locale="en-US",
)
done = aiv.jobs.wait(job["id"])

for answer in done["answers"]:
    print([(b["rank"], b["name"]) for b in answer["brands"]])
    print([c["domain"] for c in answer["citations"]])

For thousands of questions, send them as one batch of up to 20,000 answers and get one webhook when it's done. Full guides: SDKs, API reference, OpenAPI 3.1 spec.

Prefer a ready-made tool? llm-brand-tracker on GitHub tracks your brand against competitors on all 8 engines every week, free on GitHub Actions. Free and MIT licensed.

Gemini API pricing

You pay per answer, in credits. Pick the speed per request. A dollar buys 1,000 credits on a pack; monthly and volume plans include more. An answer reused from a recent identical one (max_age_hours) costs half, and an answer that fails costs nothing.

Every account gets the same API and every engine, starting with 500 free credits and no card. Volume plans start at $500 a month for 750,000 credits, with invoicing and a 99.5% uptime commitment: see volume plans.

Countries and languages

Ask Gemini from 240 countries and territories, in the buyer's language, with a locale like en-US, de-DE, pt-BR or ja-JP. ChatGPT covers 192. GET /v1/locales lists every supported pair for free, and a pair that isn't supported is refused before anything is charged.