# OpenRouter Models Scraper (`automation-lab/openrouter-models-scraper`) Actor Scrapes all AI models from the OpenRouter platform including pricing, context length, capabilities, and supported parameters. - **URL**: https://apify.com/automation-lab/openrouter-models-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 ## OpenRouter Models Scraper ### What does OpenRouter Models Scraper do? **OpenRouter Models Scraper** extracts the complete catalog of AI models available on [OpenRouter](https://openrouter.ai) — no API key, no login, and no coding required. Run the actor and get structured data for every model including **pricing, context length, supported parameters, modalities, and provider details**. The actor calls OpenRouter's public REST API directly, returning all models in a single request. No browser automation, no pagination headaches, no Playwright overhead. Every model is returned as a clean JSON record ready to export to CSV, Google Sheets, or any downstream system. All platform costs — compute and storage — are included in the pay-per-model price. No proxy costs since the API is fully public. --- ### Who is it for? **🤖 AI developers and prompt engineers** - Compare pricing across all available LLMs before choosing a model for your application - Track which models support tool use, reasoning, or specific output modalities - Monitor new model releases and deprecations automatically with scheduled runs **📊 Data analysts and researchers** - Build datasets tracking LLM pricing trends over time - Analyze the competitive landscape of AI model providers - Study context window sizes, tokenizer distributions, and modality support across the industry **💰 Cost optimization teams** - Find the cheapest model that meets your context length and capability requirements - Monitor price changes across providers to optimize API spend - Compare prompt vs completion costs to identify cost-effective alternatives **🏢 AI product managers and strategists** - Track which providers are launching new models and how quickly - Monitor the competitive landscape for multimodal, reasoning, and specialized models - Build internal dashboards showing model availability and pricing across OpenRouter --- ### Why use OpenRouter Models Scraper? - **No API key required** — OpenRouter's model catalog is fully public - **Single HTTP request** — fetches all models in one call, no pagination or scrolling - **Zero proxy cost** — no browser automation or residential proxies needed - **Pay-per-event pricing** — pay only for models actually extracted, not idle compute - **Filter by provider** — extract only models from specific providers like OpenAI, Anthropic, or Google - **Filter by modality** — narrow results to text-only, multimodal, or image generation models - **Runs in the cloud** — no local setup, no dependencies, no maintenance - **Schedule and automate** — set recurring runs to track model catalog changes over time - **Export anywhere** — JSON, CSV, Excel, Google Sheets, or push via API and webhook --- ### What data can you extract? | Model info | Field | Description | |---|---|---| | Model ID | `modelId` | OpenRouter's unique model identifier (e.g., `openai/gpt-4o`) | | Canonical slug | `canonicalSlug` | Versioned slug for the model | | Name | `name` | Human-readable model name | | Description | `description` | Model description from the provider | | Provider | `provider` | The model provider (e.g., `openai`, `anthropic`, `google`) | | Created date | `createdAt` | ISO 8601 timestamp when the model was added | | Hugging Face ID | `huggingFaceId` | Linked Hugging Face model ID (if available) | | Capabilities | Field | Description | |---|---|---| | Context length | `contextLength` | Maximum context window in tokens | | Modality | `modality` | Input/output type (e.g., `text->text`, `text+image->text`) | | Input modalities | `inputModalities` | Accepted input types (text, image, etc.) | | Output modalities | `outputModalities` | Supported output types | | Tokenizer | `tokenizer` | Tokenizer used by the model | | Supported parameters | `supportedParameters` | List of API parameters the model accepts | | Max completion tokens | `maxCompletionTokens` | Maximum output tokens per request | | Pricing | Field | Description | |---|---|---| | Prompt price per token | `promptPricePerToken` | Cost per input token in USD | | Completion price per token | `completionPricePerToken` | Cost per output token in USD | | Cache read price per token | `cacheReadPricePerToken` | Cost per cached input token (if supported) | | Metadata | Field | Description | |---|---|---| | Is moderated | `isModerated` | Whether the model applies content moderation | | Knowledge cutoff | `knowledgeCutoff` | Training data cutoff date (if provided) | | Expiration date | `expirationDate` | Model deprecation date (if scheduled) | | Model URL | `modelUrl` | Direct link to the model's OpenRouter page | | Scraped at | `scrapedAt` | ISO 8601 timestamp when the data was collected | **22 fields total** per model record. --- ### How much does it cost to scrape OpenRouter models? This actor uses **pay-per-event** pricing — you pay only for models actually extracted. No monthly subscription. All platform costs are included. | | Free ($5 credit) | Starter ($29/mo) | Scale ($199/mo) | Business ($999/mo) | |---|---|---|---|---| | **Run start fee** | $0.005 | $0.00475 | $0.00425 | $0.00375 | | **Per model** | $0.0000738 | $0.0000642 | $0.0000501 | $0.0000385 | | **All ~350 models** | ~$0.031 | ~$0.027 | ~$0.022 | ~$0.017 | **Real-world cost examples (Free plan):** | Task | Models | Estimated cost | |---|---|---| | Full catalog dump (all ~350 models) | 350 | ~$0.031 | | Single provider (e.g., Anthropic, ~15 models) | 15 | ~$0.006 | | Daily monitoring (scheduled, 30 days) | 10,500 | ~$0.92 | The **free $5 credit** included with every new Apify account covers roughly **160 full catalog scrapes** before you need to add payment. --- ### How to scrape OpenRouter models 1. Open [OpenRouter Models Scraper](https://apify.com/automation-lab/openrouter-models-scraper) on Apify Store 2. Click **Try for free** — sign in or create a free Apify account 3. Optionally set a **provider filter** (e.g., `anthropic`) or leave empty for all models 4. Optionally set a **modality filter** to narrow results 5. Click **Save & Start** 6. Wait for the run to finish (typically 5–10 seconds) 7. Click **Export** to download your data as JSON, CSV, or Excel **Example inputs for different scenarios:** Full catalog (all models): ```json { "maxResults": 0 } ```` Only Anthropic models: ```json { "filterProvider": "anthropic" } ``` Only multimodal models, limited to 20: ```json { "filterModality": "text+image->text", "maxResults": 20 } ``` *** ### Input parameters | Parameter | Type | Default | Description | |---|---|---|---| | `filterProvider` | string | — | Only return models from a specific provider (e.g., `openai`, `anthropic`, `google`, `deepseek`). Leave empty for all. | | `filterModality` | string | — | Only return models matching a modality (e.g., `text->text`, `text+image->text`). Leave empty for all. | | `maxResults` | integer | 0 | Maximum number of models to return. Set to 0 or leave empty for all models. | *** ### Output examples Each model is saved as one record in the dataset: ```json { "modelId": "anthropic/claude-sonnet-4", "canonicalSlug": "anthropic/claude-sonnet-4-20250514", "huggingFaceId": null, "name": "Anthropic: Claude Sonnet 4", "createdAt": "2025-05-14T00:00:00.000Z", "description": "Claude Sonnet 4 is Anthropic's latest mid-tier model...", "contextLength": 200000, "modality": "text+image->text", "inputModalities": ["text", "image"], "outputModalities": ["text"], "tokenizer": "Claude", "instructType": null, "promptPricePerToken": 0.000003, "completionPricePerToken": 0.000015, "cacheReadPricePerToken": 0.0000003, "topProviderContextLength": 200000, "maxCompletionTokens": 64000, "isModerated": false, "supportedParameters": ["temperature", "top_p", "tools", "tool_choice", "max_tokens", "stop", "reasoning"], "knowledgeCutoff": null, "expirationDate": null, "provider": "anthropic", "modelUrl": "https://openrouter.ai/anthropic/claude-sonnet-4", "scrapedAt": "2026-04-24T14:00:00.000Z" } ``` *** ### Tips for best results - **Schedule daily runs** to track pricing changes, new model launches, and deprecations over time. OpenRouter updates its catalog frequently. - **Use provider filters** to reduce output size and cost when you only need data for specific providers. - **Compare with previous runs** by joining on `modelId` to detect price changes, new models, or removed models between snapshots. - **Export to Google Sheets** for easy sharing with your team — Apify's native integration appends new rows automatically. - **Runs complete in seconds** — this actor makes a single HTTP request, so there's no need to increase timeouts or memory. *** ### Integrations OpenRouter Models Scraper connects with 5,000+ apps through Apify's built-in integration layer. **📊 OpenRouter Models → Google Sheets** Automatically export every run's model catalog into a shared spreadsheet. Track pricing trends and new model launches in a living dashboard without touching a spreadsheet manually. **💬 OpenRouter Models → Slack alerts** Connect via Make or Zapier: when a run finishes, post a summary (total models, new models since last run, price changes) to your Slack channel for instant visibility. **⚡ OpenRouter Models → Make / Zapier workflows** Feed model data into cost optimization dashboards, internal tooling databases, or comparison engines. Trigger the workflow on a schedule or via webhook after each run completes. **🔁 Scheduled monitoring** Set Apify to run daily and build a time-series dataset of model availability and pricing. Detect trends, track provider growth, and alert on deprecations automatically. **🔔 Webhooks for real-time processing** Configure a run-finish webhook to POST the dataset URL to your own API endpoint. Your backend receives fresh model data the moment the scraper completes. *** ### API usage Run OpenRouter Models Scraper programmatically from any language or CI/CD pipeline. **Node.js** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/openrouter-models-scraper').call({ filterProvider: 'anthropic', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Found ${items.length} models`); console.log(items[0]); ``` **Python** ```python from apify_client import ApifyClient client = ApifyClient(token='YOUR_API_TOKEN') run = client.actor('automation-lab/openrouter-models-scraper').call(run_input={ 'filterProvider': 'anthropic', }) items = list(client.dataset(run['defaultDatasetId']).iterate_items()) print(f"Found {len(items)} models") print(items[0]) ``` **cURL** ```bash curl -X POST "https://api.apify.com/v2/acts/automation-lab~openrouter-models-scraper/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"filterProvider": "anthropic"}' ``` Fetch results after the run completes: ```bash curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json" ``` Find your API token at [apify.com/account/integrations](https://apify.com/account/integrations). See the full [Apify API documentation](https://docs.apify.com/api/v2) for webhooks, scheduling, and pagination. *** ### Use with AI agents via MCP OpenRouter Models Scraper is available as a tool for AI assistants that support the [Model Context Protocol (MCP)](https://docs.apify.com/platform/integrations/mcp). Add the Apify MCP server to your AI client — this gives you access to all Apify actors, including this one: #### Setup for Claude Code ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/openrouter-models-scraper" ``` #### Setup for Claude Desktop, Cursor, or VS Code Add this to your MCP config file: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com?tools=automation-lab/openrouter-models-scraper" } } } ``` Your AI assistant will use OAuth to authenticate with your Apify account on first use. #### Example prompts Once connected, try asking your AI assistant: - "Use automation-lab/openrouter-models-scraper to get all available models and find the cheapest one with at least 128k context" - "Scrape all OpenRouter models and compare pricing between OpenAI and Anthropic models" - "Get the full OpenRouter model catalog and list all models that support tool use" Learn more in the [Apify MCP documentation](https://docs.apify.com/platform/integrations/mcp). *** ### Is it legal to scrape OpenRouter models? OpenRouter's model catalog is **public data** accessed via their public REST API — no authentication, no login, no terms-of-service bypass required. The API endpoint (`/api/v1/models`) is documented and intended for public consumption. Automation Lab's actors are built for **ethical, responsible data collection**: - Only public, non-authenticated API endpoints are accessed - No rate limits are abused — the actor makes a single HTTP request per run - No private data, credentials, or non-public content is ever extracted **You are responsible for how you use the data.** Before using scraped model data for commercial purposes, review: - [OpenRouter's Terms of Service](https://openrouter.ai/terms) - Your jurisdiction's laws on automated data collection For research, comparison, and monitoring purposes, accessing OpenRouter's public API is standard practice. See Apify's blog post on [web scraping legality](https://blog.apify.com/is-web-scraping-legal/) for a broader overview. *** ### FAQ **How many models does OpenRouter have?** As of April 2026, OpenRouter lists approximately 350 models. The catalog grows regularly as new providers and models are added. **How much does it cost to scrape all OpenRouter models?** Approximately $0.18 total: $0.005 for the run start fee plus ~$0.175 for ~350 models at $0.0005 each. The free Apify plan includes $5 in credits — enough for roughly 27 full catalog scrapes. **How fast is the scraper?** Very fast — typically 5–10 seconds per run. The actor makes a single HTTP request to OpenRouter's API, which returns all models in one response. No pagination, no browser rendering, no waiting. **Can I filter models by provider?** Yes. Set `filterProvider` to any provider slug (e.g., `openai`, `anthropic`, `google`, `deepseek`, `meta-llama`) to get only their models. **How do I track pricing changes over time?** Schedule the actor to run daily and export results to Google Sheets or a database. Compare runs by joining on `modelId` to detect price changes between snapshots. **What if the API changes?** The actor calls OpenRouter's stable public API (`/api/v1/models`). If the response format changes, the actor will be updated. Report issues on the actor's Apify page. *** ### Related scrapers Looking for more AI and data tools? Explore other actors from Automation Lab: - [Google Maps Reviews Scraper](https://apify.com/automation-lab/google-maps-reviews-scraper) — extract reviews from Google Maps business listings - [LinkedIn Profile Scraper](https://apify.com/automation-lab/linkedin-profile-scraper) — scrape public LinkedIn profile data - [TikTok Comments Scraper](https://apify.com/automation-lab/tiktok-comments-scraper) — extract comments from TikTok videos - [YouTube Scraper](https://apify.com/automation-lab/youtube-scraper) — scrape YouTube channels, videos, and comments - [Reddit Scraper](https://apify.com/automation-lab/reddit-scraper) — scrape Reddit posts and comments by subreddit or keyword # Actor input Schema ## `filterProvider` (type: `string`): Only return models from a specific provider (e.g., 'openai', 'anthropic', 'google', 'deepseek'). Leave empty for all providers. ## `filterModality` (type: `string`): Only return models matching a modality (e.g., 'text->text', 'text+image->text'). Leave empty for all modalities. ## `maxResults` (type: `integer`): Maximum number of models to return. Leave empty or set to 0 for all models. ## Actor input object example ```json {} ``` # 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 = { "filterProvider": "", "filterModality": "", "maxResults": 0 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/openrouter-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "filterProvider": "", "filterModality": "", "maxResults": 0, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/openrouter-models-scraper").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 '{ "filterProvider": "", "filterModality": "", "maxResults": 0 }' | apify call automation-lab/openrouter-models-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/openrouter-models-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenRouter Models Scraper", "description": "Scrapes all AI models from the OpenRouter platform including pricing, context length, capabilities, and supported parameters.", "version": "0.1", "x-build-id": "7yR4ex6iJ2YxdYr0i" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~openrouter-models-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-openrouter-models-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~openrouter-models-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-openrouter-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~openrouter-models-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-openrouter-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "filterProvider": { "title": "Filter by provider", "type": "string", "description": "Only return models from a specific provider (e.g., 'openai', 'anthropic', 'google', 'deepseek'). Leave empty for all providers." }, "filterModality": { "title": "Filter by modality", "enum": [ "", "text->text", "text+image->text", "text+image->text+image" ], "type": "string", "description": "Only return models matching a modality (e.g., 'text->text', 'text+image->text'). Leave empty for all modalities." }, "maxResults": { "title": "Max results", "minimum": 0, "type": "integer", "description": "Maximum number of models to return. Leave empty or set to 0 for all models." } } }, "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 } } } } } } } } } } ```