# Taco Bell Menu Prices by Location: Compare Every Store (`precious_bathmat/taco-bell-menu-prices`) Actor

Taco Bell menu prices for any US city, ZIP code or store, compared across locations: a price index per store, the cheapest Taco Bell nearby, signature item prices and the items with the biggest price gaps. No login, no proxy.

- **URL**: https://apify.com/precious\_bathmat/taco-bell-menu-prices.md
- **Developed by:** [Mariam Ahmed](https://apify.com/precious_bathmat) (community)
- **Categories:** E-commerce, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 store reports

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## 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

## Taco Bell Menu Prices by Location: Compare Every Store

Get **Taco Bell menu prices for any US city, ZIP code or store**, and see **how much they differ from one location to the next**. Each store gets a **price index** (100 = the typical store in your run), its **rank among the stores near you**, the price of **13 signature items** (Crunchwrap Supreme, Chalupa Supreme, Crunchy Taco, Baja Blast...), its cheapest and priciest items, and the items it charges **more or less than typical** for. You also get **every menu item of every store** and an **item-by-item comparison** of where each one is cheapest.

No login, no API key, no proxy.

### What does this Taco Bell menu scraper do?

```
Austin, TX · 3 nearest stores

 #030140  201 E. Oltorf       index  90.2   1 of 3   cheapest nearby
          Crunchwrap Supreme $5.99   Crunchy Taco $1.99   131 items
 compared with New York (index ~110): Crunchwrap Supreme $8.19
```

1. Enter **places** ("Austin, TX", "90017", or "latitude, longitude") and/or **store numbers**.
2. Choose how many of the **nearest stores** to price per place (1 to 15) and how far to look.
3. The Actor reads each store's own online menu from tacobell.com and returns **one report per store**, **every menu item**, and **each item's lowest, typical and highest price** across the stores.

### Who is it for?

- **Franchisees and restaurant operators**: see what nearby Taco Bells and your own stores charge, item by item
- **Competitor and pricing analysts**: benchmark a menu against Taco Bell city by city
- **Journalists and researchers**: fast-food inflation and price differences between states and cities
- **Deal and coupon sites, food apps**: current prices for any location
- **Fans on a budget**: find the cheapest Taco Bell near you

### What data do you get?

**One report per store** (the dataset):

| Field | What it tells you |
|---|---|
| `priceIndex`, `itemsCompared` | Price level against the typical store in the run (110 = 10% dearer), and over how many shared items |
| `rankNearby`, `cheapestNearby` | Where the store ranks among the stores found for the same place |
| `signatureItems` | Crunchwrap Supreme, Chalupa Supreme, Crunchy Taco, Soft Taco, Doritos Locos Taco, Cheesy Gordita Crunch, Burrito Supreme, Bean Burrito, Mexican Pizza, Nachos BellGrande, Chicken Quesadilla, Cinnamon Twists, Baja Blast |
| `dearerThanTypical`, `cheaperThanTypical` | The five items furthest above and below the typical price |
| `cheapestItems`, `priciestItems`, `medianItemPrice` | The menu's price range |
| `categories` | Items, lowest, median and highest price per category (Tacos, Burritos, Drinks...) |
| `address`, `city`, `state`, `zip`, `latitude`, `longitude`, `phone`, `distanceMiles` | Where the store is |
| `openNow`, `hoursToday`, `driveThru`, `delivery`, `breakfast`, `happierHours` | Opening and service details |

**Every menu item** (`MENU_ITEMS_CSV`): store, item, category, type (food, drink, combo, party pack), price, calories, availability, link, and `vsTypicalPercent` against the typical store.

**Item comparison** (`ITEM_COMPARISON_CSV`): for each item, the number of stores selling it, the lowest price and where, the typical price, the highest price and where, and the gap in dollars and percent.

### Example: five cities, 29 September 2026

One run, **13 stores and 1,673 menu items in 31 seconds**:

| Store | Price index | Crunchwrap Supreme | Crunchy Taco |
|---|---|---|---|
| Larned, KS (#031068) | **80.9** | $5.69 | $1.89 |
| Austin, TX (#030140) | 90.2 | $5.99 | $1.99 |
| Chicago, IL (#036215) | 99.8 | $8.49 | $1.99 |
| Los Angeles, CA (#003108) | 99.9 | $7.29 | $1.99 |
| New York, NY (#035828) | 109.0 | $8.19 | $2.99 |
| New York, NY (#035336) | 111.4 | $8.19 | $2.99 |

The same Crunchwrap Supreme cost **$5.69 to $8.49**, and across the 164 items sold at two or more of these stores, the typical gap between cheapest and dearest store was **45%**. Even stores in the same city differ: the three nearest Los Angeles stores ranged from index 99.9 to 107.2.

### Pricing

**$0.01 per store report** and **$0.0005 per menu item** saved.

One store with its full menu (about 130 items) costs about **$0.075**; turn off "Also save every menu item" and it is **$0.01 per store**. The five-city run above cost **$0.97** with all 1,673 items.

### Good to know

- **Prices are Taco Bell's online ordering prices** for pickup at that store, before tax. Delivery apps usually charge more.
- **The price index compares the stores in your run with each other**, not with a national average. Price two or more stores; add a few cities to get a wider baseline.
- **US stores only.** Stores that do not take online orders have no menu and are listed in the run summary.
- **A place near a store already priced** (two places in the same city) reuses that store instead of pricing it twice.
- **Sauce packets** swing by hundreds of percent over a few cents, so items under $1 are listed last in the comparison.
- **Place names** are looked up on OpenStreetMap; add the state to short names ("Springfield, IL").

### Input

| Field | Meaning |
|---|---|
| **Places** | US cities, addresses, ZIP codes, or "latitude, longitude" |
| **Store numbers** | Optional Taco Bell store numbers, e.g. 031068 |
| **Stores per place** | 1 to 15 nearest stores |
| **Maximum distance (miles)** | 1 to 50 |
| **Also save every menu item** | Adds the menu items file; charged per item |

### Integrations

Export to JSON, CSV, Excel or Google Sheets, or connect through the Apify API, webhooks, Make, Zapier and n8n. **Schedule it weekly** to track price changes at your stores and your competitors'.

# Actor input Schema

## `locations` (type: `array`):

US cities, addresses or ZIP codes ("Austin, TX", "90017"), or "latitude, longitude". The nearest Taco Bell stores to each place are priced.

## `storeNumbers` (type: `array`):

Optional. Taco Bell store numbers to price as well, e.g. 031068.

## `storesPerLocation` (type: `integer`):

How many of the nearest stores to price for each place, 1 to 15.

## `maxDistanceMiles` (type: `integer`):

Skip stores further than this from the place, 1 to 50 miles.

## `includeItems` (type: `boolean`):

Saves the full menu of every store (item, category, price, calories, price against the typical store) as CSV and JSON. Charged per item.

## Actor input object example

```json
{
  "locations": [
    "Los Angeles, CA",
    "New York, NY"
  ],
  "storesPerLocation": 3,
  "maxDistanceMiles": 10,
  "includeItems": true
}
```

# Actor output Schema

## `reports` (type: `string`):

Price index, rank nearby, signature item prices, cheapest and priciest items, per store.

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

Every item on every store's menu, with its price against the typical store.

## `comparison` (type: `string`):

For each item: lowest, typical and highest price, and at which store.

## `summary` (type: `string`):

Source, problems and the limits of the data.

# 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 = {
    "locations": [
        "Los Angeles, CA",
        "New York, NY"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("precious_bathmat/taco-bell-menu-prices").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 = { "locations": [
        "Los Angeles, CA",
        "New York, NY",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("precious_bathmat/taco-bell-menu-prices").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 '{
  "locations": [
    "Los Angeles, CA",
    "New York, NY"
  ]
}' |
apify call precious_bathmat/taco-bell-menu-prices --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,precious_bathmat/taco-bell-menu-prices"
        }
    }
}
```

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/6OWYCeHgJqC0qyH5F/builds/ZxE0XFRWwguWFdG5J/openapi.json