# Ai Model Pricing (`pink_fence/ai-model-pricing`) Actor Scrape live AI model pricing from OpenAI, Anthropic, Google Gemini and Mistral in one run. Input and output price per 1M tokens, context window size and more. Perfect for cost tracking and n8n workflows. - **URL**: https://apify.com/pink\_fence/ai-model-pricing.md - **Developed by:** [Moritz Knopp](https://apify.com/pink_fence) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AI Model Pricing Scraper This Apify Actor scrapes **live AI model pricing** from the official websites of OpenAI, Anthropic, Google Gemini, and Mistral — all in a single run. It returns structured data including input and output price per 1 million tokens, context window size, and a timestamp, so you always have up-to-date pricing without manually checking four different websites. --- ### Input Parameters | Field | Type | Required | Default | Description | |-------------|-----------------|----------|--------------------------------------------------|-----------------------------------------------------------------------------| | `providers` | Array of strings | No | `["openai", "anthropic", "gemini", "mistral"]` | Which providers to scrape. Omit this field to scrape all four providers. | **Accepted values for `providers`:** `"openai"`, `"anthropic"`, `"gemini"`, `"mistral"` --- ### Output Fields Each item pushed to the dataset represents one AI model and contains: | Field | Type | Description | |---------------------------|---------|--------------------------------------------------------------------| | `provider` | String | Provider name, e.g. `"OpenAI"` | | `modelName` | String | Human-readable model name, e.g. `"gpt-4o"` | | `modelId` | String | API identifier of the model, e.g. `"gpt-4o-2024-08"` | | `inputPricePer1MTokens` | Number | Cost in USD per 1 million input tokens, e.g. `5.00` | | `outputPricePer1MTokens` | Number | Cost in USD per 1 million output tokens, e.g. `15.00` | | `contextWindow` | String | Maximum context window size, e.g. `"128k"` | | `lastScraped` | String | ISO 8601 timestamp of when the data was collected | --- ### Example Input ```json { "providers": ["openai", "anthropic"] } ```` To scrape all four providers, send an empty input or omit the field entirely: ```json {} ``` *** ### Example Output ```json [ { "provider": "OpenAI", "modelName": "gpt-4o", "modelId": "gpt-4o-2024-08", "inputPricePer1MTokens": 5.00, "outputPricePer1MTokens": 15.00, "contextWindow": "128k", "lastScraped": "2026-05-10T12:00:00.000Z" }, { "provider": "Anthropic", "modelName": "claude-3-5-sonnet-20241022", "modelId": "claude-3-5-sonnet-20241022", "inputPricePer1MTokens": 3.00, "outputPricePer1MTokens": 15.00, "contextWindow": "200k", "lastScraped": "2026-05-10T12:01:34.000Z" }, { "provider": "Google Gemini", "modelName": "gemini-1.5-pro", "modelId": "gemini-1.5-pro", "inputPricePer1MTokens": 3.50, "outputPricePer1MTokens": 10.50, "contextWindow": "1M", "lastScraped": "2026-05-10T12:02:58.000Z" } ] ``` *** ### Use Case Ideas - **Cost optimisation** — Compare prices across providers and programmatically pick the cheapest model that meets your quality threshold. - **Price alerts** — Schedule this Actor to run daily and trigger a notification (via [Make](https://make.com), [n8n](https://n8n.io), or [Zapier](https://zapier.com)) whenever a price changes. - **AI budget tracking** — Feed pricing data into a spreadsheet or dashboard to forecast monthly LLM spend based on token usage. - **Competitive analysis** — Track how providers adjust prices over time and spot market trends. - **Automation integrations** — Connect the dataset to n8n, Make, or Zapier to automatically update internal pricing tables, Notion databases, or Slack channels whenever new data is scraped. *** ### Running Locally ```bash ## Install dependencies npm install ## Run the Actor npm start ``` Set your `APIFY_TOKEN` environment variable if you want data pushed to the Apify cloud dataset. Without it the Actor writes data locally to `./storage/datasets/default/`. *** ### Tech Stack - [Node.js](https://nodejs.org/) 18+ - [Apify SDK](https://docs.apify.com/sdk/js/) — Actor lifecycle, input/output handling - [Crawlee](https://crawlee.dev/) — `PlaywrightCrawler` for JS-rendered pages - [playwright-extra](https://github.com/berstend/puppeteer-extra/tree/master/packages/playwright-extra) + [stealth plugin](https://github.com/berstend/puppeteer-extra/tree/master/packages/puppeteer-extra-plugin-stealth) — avoid bot detection # Actor input Schema ## `providers` (type: `array`): List of providers to scrape. Options: openai, anthropic, gemini, mistral. Leave empty to scrape all. ## Actor input object example ```json { "providers": [ "openai", "anthropic", "gemini", "mistral" ] } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("pink_fence/ai-model-pricing").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("pink_fence/ai-model-pricing").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call pink_fence/ai-model-pricing --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pink_fence/ai-model-pricing", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ai Model Pricing", "description": "Scrape live AI model pricing from OpenAI, Anthropic, Google Gemini and Mistral in one run. Input and output price per 1M tokens, context window size and more. Perfect for cost tracking and n8n workflows.", "version": "0.0", "x-build-id": "XPtuy2IMfgEdYZSL4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pink_fence~ai-model-pricing/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pink_fence-ai-model-pricing", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/pink_fence~ai-model-pricing/runs": { "post": { "operationId": "runs-sync-pink_fence-ai-model-pricing", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/pink_fence~ai-model-pricing/run-sync": { "post": { "operationId": "run-sync-pink_fence-ai-model-pricing", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "providers": { "title": "Providers", "type": "array", "description": "List of providers to scrape. Options: openai, anthropic, gemini, mistral. Leave empty to scrape all.", "items": { "type": "string" }, "default": [ "openai", "anthropic", "gemini", "mistral" ] } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```