# ARC Prize Leaderboard Scraper (`automation-lab/arcprize-leaderboard-scraper`) Actor Scrapes ARC Prize leaderboard data (ARC-AGI-1/2/3 benchmarks) for all AI models including scores, costs, providers, and rankings - **URL**: https://apify.com/automation-lab/arcprize-leaderboard-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## ARC Prize Leaderboard Scraper > Extract AI model rankings, scores, costs, and metadata from the ARC Prize leaderboard β€” covering all ARC-AGI-1, ARC-AGI-2, and ARC-AGI-3 benchmark datasets. ### πŸ“– What does it do? The **ARC Prize Leaderboard Scraper** extracts structured benchmark data from [arcprize.org/leaderboard](https://arcprize.org/leaderboard), the official leaderboard for the ARC Prize competition β€” the leading benchmark for measuring progress toward artificial general intelligence (AGI). Give it a list of benchmark versions (v1, v2, v3) and it returns every model's performance data including scores, costs per task, provider information, model types, and release dates. **What you get for each leaderboard entry:** - Model name and display label - Provider/organization name - ARC-AGI benchmark version and dataset - Score (0–1 accuracy) - Cost per task (v1/v2) or total evaluation cost (v3) - Model type (Base LLM, CoT, Custom, etc.) - Model group/family - Release date --- ### πŸ‘₯ Who is it for? #### πŸ”¬ AI researchers and academics Tracking the frontier of AI capabilities? Use this actor to collect time-series data on how different model families progress on ARC-AGI benchmarks without manually scraping the leaderboard. #### πŸ“Š Data scientists and analysts Building dashboards comparing LLM capabilities, cost-efficiency frontiers, or vendor performance? Get structured, queryable JSON output for all models across all benchmark versions. #### πŸ€– AI product teams and investors Monitoring competitor model performance on the hardest reasoning benchmarks, tracking cost-efficiency trends, or building automated capability-tracking pipelines. #### πŸ“° AI journalists and content creators Writing about AGI progress? Pull fresh leaderboard data programmatically to power articles, newsletters, or automated reports. #### 🏫 Educators and course creators Teaching AI capabilities and limitations? Use live leaderboard data in lectures, assignments, and demos. --- ### πŸš€ Why use it? - **Direct JSON endpoint** β€” ARC Prize exposes clean public JSON endpoints; no HTML parsing or browser automation needed - **All benchmark versions** β€” covers ARC-AGI-1, ARC-AGI-2, and ARC-AGI-3 in a single run - **Structured output** β€” fully typed fields, consistent schema across versions - **Always fresh** β€” fetches live data from arcprize.org on every run - **Low cost** β€” pure HTTP requests, runs in under 30 seconds with minimal compute --- ### πŸ“Š Data fields extracted | Field | Type | Description | |-------|------|-------------| | `version` | string | Benchmark version: `v1`, `v2`, or `v3` | | `datasetId` | string | Internal dataset identifier (e.g., `v1_Semi_Private`) | | `datasetDisplayName` | string | Human-readable dataset name (ARC-AGI-1, ARC-AGI-2, ARC-AGI-3) | | `modelId` | string | Unique model identifier | | `modelDisplayName` | string | Human-readable model name | | `modelType` | string \| null | Model type: Base LLM, CoT, Custom, CoT + Synthesis, etc. | | `modelGroup` | string \| null | Model family/group name | | `providerId` | string | Provider identifier (e.g., `Anthropic`, `OpenAI`) | | `providerDisplayName` | string | Human-readable provider name | | `score` | number | Accuracy score (0–1, where 1.0 = 100%) | | `costPerTask` | number \| null | Cost in USD per task solved (v1/v2); null for v3 | | `totalCost` | number \| null | Total evaluation cost in USD (v3); null for v1/v2 | | `modelReleaseDate` | string \| null | Model release date (ISO 8601) | | `display` | boolean | Whether this entry is shown on the public leaderboard | | `resultsUrl` | string | URL to detailed results (if available) | | `leaderboardUrl` | string | URL to the ARC Prize leaderboard | --- ### πŸ’° How much does it cost? This scraper uses **Pay-Per-Event (PPE) pricing** β€” you only pay for entries actually extracted. | What you pay for | Cost | |-----------------|------| | Run started (one-time) | $0.005 | | Per leaderboard entry extracted | $0.0005 | **Example costs:** - All 3 benchmark versions (~300 entries total): ~$0.155 - One benchmark version (~100–150 entries): ~$0.055–0.080 - Monthly monitoring run (weekly, all versions): ~$0.62/month --- ### βš™οΈ Input configuration | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `datasets` | array | `["v1","v2","v3"]` | Which benchmark versions to scrape | | `includeHidden` | boolean | `false` | Include entries not shown on public leaderboard | | `maxRequestRetries` | integer | `3` | Retry attempts for failed HTTP requests | --- ### πŸ“‹ Example input Scrape all three ARC-AGI benchmark leaderboards: ```json { "datasets": ["v1", "v2", "v3"], "includeHidden": false } ```` Scrape only ARC-AGI-2 including hidden entries: ```json { "datasets": ["v2"], "includeHidden": true } ``` *** ### πŸ“€ Example output ```json { "version": "v1", "datasetId": "v1_Semi_Private", "datasetDisplayName": "ARC-AGI-1", "modelId": "Claude 3.7", "modelDisplayName": "Claude 3.7", "modelType": "Base LLM", "modelGroup": null, "providerId": "Anthropic", "providerDisplayName": "Anthropic", "score": 0.136, "costPerTask": 0.058, "totalCost": null, "modelReleaseDate": "2025-02-24T00:00:00.000Z", "display": true, "resultsUrl": "", "leaderboardUrl": "https://arcprize.org/leaderboard" } ``` *** ### πŸ› οΈ How to use Follow these steps to get leaderboard data from the Apify Store: 1. **Open the actor** β€” go to [ARC Prize Leaderboard Scraper](https://apify.com/automation-lab/arcprize-leaderboard-scraper) on the Apify Store and click **Try for free**. 2. **Configure input** β€” in the **Input** tab, choose which benchmark versions (`v1`, `v2`, `v3`) to scrape and whether to include hidden entries. 3. **Run** β€” click **Start** and wait for the actor to finish (typically under 30 seconds). 4. **Download results** β€” go to the **Dataset** tab to view extracted entries. Export as JSON, CSV, or JSONL using the **Export** button, or fetch via the Apify API. 5. **Schedule recurring runs** (optional) β€” click **Schedule** to run automatically (e.g., weekly) and always have fresh leaderboard data. 6. **Connect to downstream tools** β€” use the [Apify integrations](https://apify.com/integrations) to send data to Google Sheets, Slack, Webhooks, or any HTTP endpoint after each run. *** ### πŸ”— Integrations Connect ARC Prize Leaderboard Scraper to your existing tools and workflows: #### Google Sheets Use the [Apify β†’ Google Sheets integration](https://apify.com/integrations/google-sheets) to automatically append fresh leaderboard data to a spreadsheet after each run. Ideal for building live-updating dashboards or sharing data with your team. #### Slack notifications Trigger a Slack message whenever the leaderboard updates (e.g., a new model breaks a top-10 score). Wire up the [Apify β†’ Slack integration](https://apify.com/integrations/slack) in the **Integrations** tab. #### Webhooks After each run completes, fire a webhook to any HTTP endpoint β€” your own backend, a Zapier/Make workflow, or an n8n automation. Configure in the actor's **Integrations** tab β†’ **Webhook**. #### Apify API + Python / Node.js Embed leaderboard scraping in your own data pipeline using the [Apify Python client](https://docs.apify.com/api/client/python) or [Node.js client](https://docs.apify.com/api/client/js). See the **API usage examples** section below. #### Make (Integromat) / Zapier Use Apify's native [Make](https://www.make.com/en/integrations/apify) and [Zapier](https://zapier.com/apps/apify/integrations) connectors to route leaderboard data into spreadsheets, databases, or notification services without writing code. #### AI agents via MCP Expose live benchmark data to Claude, Cursor, or VS Code AI features β€” see the **MCP integration** section below. *** ### πŸ”§ Technical details - **Architecture**: Pure HTTP, no browser needed - **Source**: `arcprize.org/media/data/leaderboard/{v1,v2,v3}.json` - **Typical runtime**: < 30 seconds - **Memory**: 256 MB - **Rate limits**: Public JSON endpoints, no rate limiting observed *** ### πŸ€– MCP integration (Claude, Cursor, VS Code) Use this actor as a live data source inside AI coding assistants via the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp). #### Claude Code (terminal) Install the Apify MCP server into Claude Code with one command: ```bash claude mcp add apify -- npx -y @apify/mcp-server@latest ``` Then set your API token: ```bash export APIFY_API_KEY=your_apify_api_token ``` In any Claude conversation you can then ask: - *"Run the arcprize-leaderboard-scraper actor and show me the top 10 models by score on ARC-AGI-2."* - *"Show me all models from Anthropic on ARC-AGI-1 with their scores, sorted by score descending."* - *"Which models have scores above 50% on ARC-AGI-2? Run the scraper and filter the results."* #### Claude Desktop Add the Apify MCP server to `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows): ```json { "mcpServers": { "apify": { "command": "npx", "args": ["-y", "@apify/mcp-server@latest"], "env": { "APIFY_API_KEY": "your_apify_api_token" } } } } ``` #### Cursor / VS Code Add to your MCP settings (`.cursor/mcp.json` or VS Code MCP config): ```json { "mcpServers": { "apify": { "command": "npx", "args": ["-y", "@apify/mcp-server@latest"], "env": { "APIFY_API_KEY": "your_apify_api_token" } } } } ``` Once configured, your AI assistant can call `run_actor` with actor ID `automation-lab/arcprize-leaderboard-scraper` and input like `{"datasets": ["v1","v2","v3"]}` to fetch live leaderboard data mid-conversation. *** ### πŸ€” FAQ β€” Frequently asked questions **What is ARC-AGI?** The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) is a benchmark created by FranΓ§ois Chollet to measure general reasoning abilities β€” tasks that require pattern recognition and abstract reasoning rather than knowledge retrieval. **What's the difference between ARC-AGI-1, -2, and -3?** - ARC-AGI-1: Original 2020 benchmark; many top models now exceed 85% accuracy - ARC-AGI-2: Harder 2025 version; current best models score under 30% - ARC-AGI-3: Hardest 2025/2026 version; frontier models score under 1% **Why are some entries hidden?** Hidden entries (display: false) include superseded models, internal test runs, or entries the ARC Prize team chose not to highlight. Enable `includeHidden: true` to see them. **How often is the leaderboard updated?** arcprize.org updates their JSON files when new evaluation results are submitted. Run this actor regularly (e.g., weekly via Apify schedules) to track changes over time. **Why does v3 use `totalCost` instead of `costPerTask`?** ARC-AGI-3 reports total evaluation cost rather than per-task cost, reflecting the full infrastructure cost of a complete evaluation run. *** ### πŸ’» API usage examples You can trigger this actor programmatically via the Apify API or SDKs. **Node.js (ApifyClient):** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/arcprize-leaderboard-scraper').call({ datasets: ['v1', 'v2', 'v3'], includeHidden: false, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` **Python (ApifyClient):** ```python from apify_client import ApifyClient client = ApifyClient('YOUR_API_TOKEN') run = client.actor('automation-lab/arcprize-leaderboard-scraper').call(run_input={ 'datasets': ['v1', 'v2', 'v3'], 'includeHidden': False, }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items) ``` **cURL:** ```bash curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~arcprize-leaderboard-scraper/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"datasets":["v1","v2","v3"],"includeHidden":false}' ``` *** ### πŸ”— Related actors - [EvalPlus Leaderboard Scraper](https://apify.com/automation-lab/evalplus-leaderboard-scraper) β€” Scrapes the EvalPlus code generation benchmark leaderboard - [AlpacaEval Leaderboard Scraper](https://apify.com/automation-lab/alpacaeval-leaderboard-scraper) β€” Scrapes the AlpacaEval instruction-following leaderboard - [LiveBench Scraper](https://apify.com/automation-lab/livebench-scraper) β€” Scrapes the LiveBench LLM benchmark leaderboard - [EQ-Bench Scraper](https://apify.com/automation-lab/eqbench-scraper) β€” Scrapes the EQ-Bench emotional intelligence benchmark leaderboard *** ### βš–οΈ Legality and terms of use This actor accesses publicly available JSON endpoints on arcprize.org β€” the same data that powers the public leaderboard website. No authentication is required, and the data is intentionally made public for research and benchmarking transparency. - No login, credentials, or bypassing of access controls is involved - The data is publicly published by the ARC Prize organization - Usage should comply with arcprize.org's terms of service - Do not use for commercial redistribution of the data without permission from the ARC Prize Foundation # Actor input Schema ## `datasets` (type: `array`): Which ARC-AGI benchmark versions to scrape. Select one or more. Defaults to all three. ## `includeHidden` (type: `boolean`): If enabled, includes leaderboard entries marked as hidden (display: false). By default only visible entries are returned. ## `maxRequestRetries` (type: `integer`): Number of retry attempts for failed HTTP requests. ## Actor input object example ```json { "datasets": [ "v1", "v2", "v3" ], "includeHidden": false, "maxRequestRetries": 3 } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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 = { "datasets": [ "v1", "v2", "v3" ], "includeHidden": false, "maxRequestRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/arcprize-leaderboard-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "datasets": [ "v1", "v2", "v3", ], "includeHidden": False, "maxRequestRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/arcprize-leaderboard-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "datasets": [ "v1", "v2", "v3" ], "includeHidden": false, "maxRequestRetries": 3 }' | apify call automation-lab/arcprize-leaderboard-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/arcprize-leaderboard-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ARC Prize Leaderboard Scraper", "description": "Scrapes ARC Prize leaderboard data (ARC-AGI-1/2/3 benchmarks) for all AI models including scores, costs, providers, and rankings", "version": "0.1", "x-build-id": "4Ja0SNgVkfQLy7j9A" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~arcprize-leaderboard-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-arcprize-leaderboard-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~arcprize-leaderboard-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-arcprize-leaderboard-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~arcprize-leaderboard-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-arcprize-leaderboard-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "datasets": { "title": "Datasets to scrape", "type": "array", "description": "Which ARC-AGI benchmark versions to scrape. Select one or more. Defaults to all three.", "default": [ "v1", "v2", "v3" ] }, "includeHidden": { "title": "Include hidden entries", "type": "boolean", "description": "If enabled, includes leaderboard entries marked as hidden (display: false). By default only visible entries are returned.", "default": false }, "maxRequestRetries": { "title": "Max request retries", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of retry attempts for failed HTTP requests.", "default": 3 } } }, "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 } } } } } } } } } } ```