# Vercel Ai Gateway Models Scraper (`bigibyte/vercel-ai-gateway-models-scraper`) Actor

Collect public LLM models and pricing data into clean datasets. Filter by provider, check context windows and pricing details, and export as JSON, CSV, or Excel for easy comparison.

- **URL**: https://apify.com/bigibyte/vercel-ai-gateway-models-scraper.md
- **Developed by:** [BigiByte](https://apify.com/bigibyte) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Vercel AI Gateway Models Scraper

![Vercel AI Gateway Models Scraper cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/vercel-ai-gateway-models-scraper-cover.png)

BigiByte brings you the Vercel AI Gateway Models Scraper. a specialized extraction utility designed to effortlessly collect comprehensive language model inventories, capabilities, and detailed pricing structures. Whether you are conducting competitive research, tracking context windows, or building pricing comparison tables, this tool retrieves clean, structured datasets in JSON, CSV, or Excel formats with minimal friction.

### What it does

This actor reads the available model records from the gateway catalog, allowing developers, product managers, and AI researchers to inspect provider offerings, context limits, tags, and pricing parameters within a single unified dataset. The crawler fetches structured model catalogs and caps the output cleanly based on your custom item limit.

### Modes

- **All models**: Fetches every available model from the complete catalog, with optional provider filtering.

### Input example

```json
{
  "mode": "models",
  "ownedBy": ["openai", "anthropic"],
  "maxItems": 50,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "requestTimeoutSecs": 30
}

```

### Output example

```json
{
  "modelId": "alibaba/qwen-3-14b",
  "modelObject": "model",
  "created": 1755815280,
  "released": 1745798400,
  "ownedBy": "alibaba",
  "modelName": "Qwen3-14B",
  "modelSummary": "Qwen3 is the latest generation of large language models in Qwen series.",
  "contextWindow": 40960,
  "maxTokens": 16384,
  "modelKind": "language",
  "zdr": "all",
  "noTraining": "all",
  "tags": ["reasoning", "tool-use", "structured-output", "open-weights"],
  "supportedSpecifications": ["v2", "v3", "v4"],
  "modalities": {"input": ["text"], "output": ["text"]},
  "supportedParameters": ["max_tokens", "temperature", "stop"],
  "temperature": true,
  "reasoningOptions": [{"type": "toggle"}],
  "knowledge": "2025-04",
  "pricing": {"input": "0.00000012", "output": "0.00000024"},
  "scrapedAt": "2026-10-26T00:00:00+00:00"
}

```

The overview table presents key attributes including modelId, modelObject, created, released, ownedBy, modelName, and modelSummary.

### Use cases

- **Model discovery**: Explore providers, architectures, and model variants available across the ecosystem
- **Pricing comparison**: Extract granular input and output pricing fields for cost modeling and spreadsheet analysis
- **Capability filtering**: Filter and organize tags such as reasoning or tool-use for tailored shortlists
- **Context planning**: Verify contextWindow and maxTokens specifications before building integrations
- **Catalog monitoring**: Set up regular collection runs to track newly released models or deprecations over time

### Pricing and Usage Limits

- **Cost-effective extraction**: Runs efficiently with adjustable request delays and timeout settings to match your throughput requirements.
- **Adjustable batching**: Control the maximum item count per run to fit within your resource budget.

### FAQ

**Who is this for?**

Engineers, data analysts, and AI researchers who need structured model catalogs in spreadsheets or automated pipelines.

**What formats can I export?**

JSON, CSV, Excel, and all standard dataset export options supported by the platform.

**Can I run this on a schedule?**

Yes, you can configure recurring runs to automatically refresh your model datasets.

**How do I filter by specific providers?**

Simply specify one or more provider slugs (such as `openai` or `anthropic`) in the filter settings.

### Related tools

If you plan to map or join these rows, a geocoding pass or a quick CSV cleanup are the usual next steps. BigiByte also builds scrapers for other open-data portals when you need them as a separate job.
Need another open dataset, a scheduled monitor, or a custom pipeline? BigiByte builds custom scrapers and APIs: <https://bigibyte.com>

### Trademark Notice

All product names, logos, and brands mentioned are property of their respective owners. Their use is strictly for identification purposes and does not imply official endorsement, sponsorship, or partnership with BigiByte.

### Disclaimer

This Actor is an independent utility developed by BigiByte and is not officially associated with or endorsed by Vercel. Users are solely responsible for ensuring compliance with applicable platform terms, policies, and rate limits.

### Is web scraping legal?

This actor extracts publicly accessible data without bypassing paywalls, or technical security layers. While collecting public web data is widely permitted, compliance depends on how data is gathered and used:

- **Personal data**: When collecting identifiers like emails, phone numbers, or social handles, ensure compliance with applicable data protection laws such as GDPR or CCPA.
- **Copyrighted content**: Public text, images, and visual assets are often protected by intellectual property rights. Avoid directly duplicating or re-distributing full creative works.
- **Platform terms & rate limits**: Practice responsible data extraction by avoiding aggressive traffic spikes, adhering to rate limits, and respecting published platform policies.
- **Disclaimer**: This summary serves informational purposes only and does not constitute formal legal counsel. Seek professional legal advice for specific compliance queries.
- For a detailed guide, see Apify's legal overview: [Is web scraping legal?](https://blog.apify.com/is-web-scraping-legal/)

# Actor input Schema

## `mode` (type: `string`):

What to scrape. Models mode fetches the public Vercel AI Gateway model catalog.

## `ownedBy` (type: `array`):

Optional provider filter matching owned_by (case-insensitive). Leave empty for all providers.

## `maxItems` (type: `integer`):

Stop after this many successful rows. Hard max is 1000.

## `requestDelaySecs` (type: `number`):

Seconds to wait between requests.

## `userAgent` (type: `string`):

User-Agent header sent with each request.

## `timeoutSecs` (type: `integer`):

Overall run budget in seconds.

## `requestTimeoutSecs` (type: `integer`):

Per-request timeout in seconds.

## `proxyConfiguration` (type: `object`):

Optional proxy. Leave off unless you need it.

## Actor input object example

```json
{
  "mode": "models",
  "ownedBy": [],
  "maxItems": 100,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

Dataset rows in the overview table.

## `dataset` (type: `string`):

Full dataset for this run.

## `items` (type: `string`):

Dataset items as JSON.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "ownedBy": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("bigibyte/vercel-ai-gateway-models-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "ownedBy": [] }

# Run the Actor and wait for it to finish
run = client.actor("bigibyte/vercel-ai-gateway-models-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "ownedBy": []
}' |
apify call bigibyte/vercel-ai-gateway-models-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bigibyte/vercel-ai-gateway-models-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/nXQ88NzKagihrYaoh/builds/4YJwFcvPUVhoarOss/openapi.json