# Chatbot Arena Scraper (`automation-lab/chatbot-arena-scraper`) Actor Scrapes the Chatbot Arena (arena.ai) leaderboard to extract LLM model rankings, Elo scores, confidence intervals, vote counts, and category-specific ratings from human preference battles. - **URL**: https://apify.com/automation-lab/chatbot-arena-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 ## Chatbot Arena Scraper Scrapes the [Chatbot Arena](https://arena.ai) leaderboard to extract LLM model rankings, Elo scores, confidence intervals, vote counts, pricing, and context window sizes from human preference battles. --- ### What does Chatbot Arena Scraper do? Chatbot Arena (arena.ai) is the most widely cited independent benchmark for comparing LLMs, based on millions of anonymous human votes in side-by-side model battles. This actor extracts the full leaderboard data across all 10 categories: - **Text** — general text generation - **Code** — code generation - **Vision** — multimodal/image understanding - **Document** — document processing - **Text-to-Image** — image generation - **Image Edit** — image editing - **Search** — web search integration - **Text-to-Video** — video generation - **Image-to-Video** — video from images - **Video Edit** — video editing --- ### Who is it for? **AI engineers and ML teams** selecting models for production systems. Compare hundreds of LLMs by Elo score, pricing, and context window to find the optimal model for your latency, cost, and quality requirements. **Product managers and CTOs** making build-vs-buy decisions around AI capabilities. Track how providers like OpenAI, Anthropic, Google, and Meta rank against each other across text, code, vision, and image generation. **AI researchers and academics** who need standardized, machine-readable benchmark data for papers, reports, and meta-analyses. Export clean JSON or CSV directly from the dataset — no manual data entry from screenshots. **Data engineers and automation builders** feeding model performance data into dashboards, Slack bots, or automated model-selection pipelines. Schedule regular runs to keep your internal data warehouse current. **Investors and analysts** tracking the competitive landscape of the AI industry. Monitor which providers are gaining or losing ground in human preference rankings across different modalities. --- ### Why scrape Chatbot Arena? Chatbot Arena is the gold standard for LLM evaluation. Unlike static benchmarks, it uses real human preferences from blind side-by-side comparisons. The Elo scoring system provides a reliable, continuously updated ranking that reflects actual user experience. Key advantages of using this actor: - **All 10 categories** — text, code, vision, document, image generation, video, search, and editing - **Structured data** — clean JSON with provider, license, Elo score, confidence interval, votes, pricing, and context window - **Fast and lightweight** — HTTP + Cheerio parsing, no browser needed. Runs in under 5 seconds at 256MB memory - **API-first** — integrate directly into CI/CD pipelines, dashboards, or automation workflows - **Batch processing** — scrape all categories in a single run, get 1,000+ model rankings at once - **Scheduled runs** — track ranking trends over time with daily or weekly schedules --- ### Output data Each result includes: | Field | Type | Description | |-------|------|-------------| | `rank` | integer | Position on the leaderboard | | `model` | string | Model name (e.g. `claude-opus-4-7-thinking`) | | `provider` | string | Company or provider (e.g. `Anthropic`, `Google`, `OpenAI`) | | `license` | string | License type (`Proprietary`, `Open`, `MIT`, `Apache 2.0`, etc.) | | `score` | string | Elo score with confidence interval (e.g. `1503 ±8`) | | `eloRating` | number | Numeric Elo rating | | `confidenceInterval` | string | CI range (e.g. `±8`, `+19/-19`) | | `votes` | string | Total number of human votes | | `pricePer1MTokens` | string | Price per 1M tokens, input/output (e.g. `$5 / $25`) | | `contextLength` | string | Context window size (e.g. `1M`, `200K`) | | `category` | string | Leaderboard category | | `url` | string | Source leaderboard URL | | `scrapedAt` | string | ISO 8601 timestamp | --- ### How much does it cost to scrape Chatbot Arena leaderboard data? Pricing uses **Pay Per Event (PPE)** — you only pay for what you actually extract, with no minimum charge beyond the start fee. | Event | Cost | |-------|------| | Actor start | $0.005 (once per run) | | Per model result | $0.0005 | **Example costs:** - Scrape top 20 text models → ~$0.015 - Scrape all text models (~340) → ~$0.175 - Scrape all 10 categories (~1,000+ models) → ~$0.505 **Free plan estimate:** Apify's free plan includes $5/month in platform credits, enough to run this actor daily on the text category at no charge. --- ### How to use #### Using the Apify Console (no code) 1. Go to [Chatbot Arena Scraper](https://apify.com/automation-lab/chatbot-arena-scraper) on the Apify Store 2. Click **Try for free** 3. Select the **categories** you want to scrape (default: `text`) 4. Set **Max results per category** (default: all models) 5. Click **Start** and wait for the run to finish (typically under 10 seconds) 6. Click **Export** to download results as JSON, CSV, or Excel #### Typical workflow 1. Choose your categories of interest (e.g., `text` and `code` for LLM selection) 2. Run the actor to get current rankings 3. Export to Google Sheets or your database 4. Schedule weekly runs to track trends over time --- ### Input parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `categories` | string[] | No | `["text"]` | Which leaderboard categories to scrape. Options: `text`, `code`, `vision`, `document`, `text-to-image`, `image-edit`, `search`, `text-to-video`, `image-to-video`, `video-edit`. | | `maxResults` | integer | No | 0 (all) | Maximum number of models to extract per category. Set to `0` for all models. | | `proxyConfiguration` | object | No | Apify Proxy | Proxy settings. Uses Apify Proxy by default. | #### Supported categories | Category | URL Path | Typical models | Description | |----------|----------|---------------|-------------| | `text` | `/leaderboard/text` | ~340 | General text generation and conversation | | `code` | `/leaderboard/code` | ~65 | Code generation and programming tasks | | `vision` | `/leaderboard/vision` | ~90 | Multimodal image understanding | | `document` | `/leaderboard/document` | ~40 | Document processing and analysis | | `text-to-image` | `/leaderboard/text-to-image` | ~55 | Image generation from text prompts | | `image-edit` | `/leaderboard/image-edit` | ~25 | Image editing and manipulation | | `search` | `/leaderboard/search` | ~30 | Web search integration | | `text-to-video` | `/leaderboard/text-to-video` | ~30 | Video generation from text | | `image-to-video` | `/leaderboard/image-to-video` | ~20 | Video generation from images | | `video-edit` | `/leaderboard/video-edit` | ~10 | Video editing | --- ### Output example ```json { "rank": 1, "model": "claude-opus-4-7-thinking", "provider": "Anthropic", "license": "Proprietary", "score": "1503 ±8", "eloRating": 1503, "confidenceInterval": "±8", "votes": "4,924", "pricePer1MTokens": "$5 / $25", "contextLength": "1M", "category": "text", "url": "https://arena.ai/leaderboard/text", "scrapedAt": "2026-04-23T19:01:06.184Z" } ```` **Image generation category output** (no pricing/context fields): ```json { "rank": 1, "model": "gpt-image-2 (medium)", "provider": "OpenAI", "license": "Proprietary", "score": "1507 ±9", "eloRating": 1507, "confidenceInterval": "±9", "votes": "15,391", "pricePer1MTokens": null, "contextLength": null, "category": "text-to-image", "url": "https://arena.ai/leaderboard/text-to-image", "scrapedAt": "2026-04-23T19:01:06.184Z" } ``` *** ### Tips and tricks - Scraping all 10 categories in a single run gives you a comprehensive cross-domain view of model capabilities. - Schedule weekly runs to build a historical dataset of how model rankings evolve over time. - Use the `provider` field to filter results for a specific company (e.g., only Anthropic or OpenAI models). - The `votes` field indicates how statistically reliable a ranking is — models with more votes have more stable Elo scores. - Compare `eloRating` and `pricePer1MTokens` to identify the best price-performance models. - Image and video categories (`text-to-image`, `image-edit`, `text-to-video`, `image-to-video`, `video-edit`) do not include `pricePer1MTokens` or `contextLength` — these fields will be `null`. *** ### Integrations #### Google Sheets — live model comparison dashboard Connect the actor's dataset to Google Sheets via the [Apify Google Sheets integration](https://apify.com/apify/google-sheets). Schedule daily runs to keep a live spreadsheet of model rankings that your team can filter by provider, category, or score range. #### Slack — ranking change alerts Use the [Apify Slack integration](https://apify.com/apify/slack) to post a summary when rankings change. For example, notify your `#ai-models` channel when a new model enters the top 10 in any category. #### Webhooks — feed your internal API Configure an Apify webhook to POST results to your internal model registry or dashboard API after each run. Use the structured JSON output to update your model selection logic automatically. #### n8n / Make / Zapier — automated workflows Connect this actor to 5,000+ apps via automation platforms. Example: run the scraper weekly, compare results with the previous week's data, and email a summary of ranking changes to your AI team lead. #### Airtable — model comparison database Export results to Airtable to build a searchable model comparison database with filtering by provider, license type, price range, and Elo score. *** ### API usage #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_API_TOKEN' }); const run = await client.actor('automation-lab/chatbot-arena-scraper').call({ categories: ['text', 'code'], maxResults: 20, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); for (const item of items) { console.log(`#${item.rank} ${item.model} (${item.provider}) — Elo: ${item.eloRating}, Votes: ${item.votes}`); } ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient(token="YOUR_APIFY_API_TOKEN") run = client.actor("automation-lab/chatbot-arena-scraper").call(run_input={ "categories": ["text", "code"], "maxResults": 20, }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(f"#{item['rank']} {item['model']} ({item['provider']}) — Elo: {item['eloRating']}, Votes: {item['votes']}") ``` #### cURL ```bash ## Start the actor run curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~chatbot-arena-scraper/runs?token=YOUR_APIFY_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "categories": ["text"], "maxResults": 20 }' ## Get results (replace DATASET_ID from the response above) curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_APIFY_API_TOKEN" ``` *** ### Use with Claude AI (MCP) This actor is available as a tool in Claude AI through the Model Context Protocol (MCP). Add it to Claude Desktop, Cursor, Windsurf, or any MCP-compatible client. #### Setup for Claude Code ```bash claude mcp add --transport http apify "https://mcp.apify.com" ``` #### Setup for Claude Desktop, Cursor, or VS Code Add this to your MCP config file: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com" } } } ``` #### Example prompts - "Get the top 20 LLMs from Chatbot Arena and compare their Elo scores and pricing." - "Which open-source models rank highest on the Chatbot Arena code leaderboard?" - "Scrape the Chatbot Arena vision and text-to-image leaderboards and show me which providers dominate each category." Learn more in the [Apify MCP documentation](https://docs.apify.com/platform/integrations/mcp). *** ### Legality This actor scrapes publicly available leaderboard data from arena.ai. The data is publicly accessible without authentication, and the actor makes standard HTTP requests at a reasonable rate (one request per category). The scraped data consists of aggregated benchmark results (model rankings, scores, vote counts) that are freely shared by the Chatbot Arena project for public use. No personal data or copyrighted content is collected. For more information about web scraping legality, see the [Apify web scraping legality guide](https://blog.apify.com/is-web-scraping-legal/). *** ### FAQ **Q: How often is the Chatbot Arena leaderboard updated?** A: The leaderboard updates continuously as users submit new votes through arena.ai battles. Rankings can shift daily, especially for newer models with fewer votes. Schedule regular runs to track changes. **Q: Why are `pricePer1MTokens` and `contextLength` null for some categories?** A: Image and video categories (text-to-image, image-edit, text-to-video, image-to-video, video-edit) do not include pricing or context window columns on arena.ai. These fields are only available for text-based categories (text, code, vision, document, search). **Q: The actor returned 0 results for a category. What happened?** A: This can happen if arena.ai temporarily changes their HTML structure, if the category page is down, or if your proxy was blocked. Check the run log for error messages. Try running again — transient network issues are usually resolved on retry. **Q: Can I scrape all categories at once?** A: Yes. Set `categories` to an empty array or include all 10 categories in the list. A full scrape across all categories typically extracts 700-1,000+ models and completes in under 30 seconds. **Q: What does the confidence interval (e.g., ±8) mean?** A: The confidence interval reflects the statistical uncertainty in the Elo rating. A smaller interval (e.g., ±3) means the ranking is more stable and based on many votes. A larger interval (e.g., ±20) means the model has fewer votes and its ranking may shift significantly with new data. *** ### Related actors - [YouTube Transcript Scraper](https://apify.com/automation-lab/youtube-transcript) — Extract transcripts and captions from YouTube videos in bulk - [Google Ads Scraper](https://apify.com/automation-lab/google-ads-scraper) — Scrape Google Ads results for competitive intelligence - [Trustpilot Reviews Scraper](https://apify.com/automation-lab/trustpilot) — Extract reviews and ratings from Trustpilot business pages - [Domain Availability Checker](https://apify.com/automation-lab/domain-availability-checker) — Check domain name availability in bulk - [IBAN Validator](https://apify.com/automation-lab/iban-validator) — Validate and parse IBAN numbers in batch # Actor input Schema ## `categories` (type: `array`): Which leaderboard categories to scrape. Leave empty to scrape all categories. ## `maxResults` (type: `integer`): Maximum number of models to extract per category. Set to 0 for all models. ## `proxyConfiguration` (type: `object`): Select proxies to be used. ## Actor input object example ```json { "categories": [ "text" ], "maxResults": 20, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "categories": [ "text" ], "maxResults": 20, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/chatbot-arena-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 = { "categories": ["text"], "maxResults": 20, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/chatbot-arena-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 '{ "categories": [ "text" ], "maxResults": 20, "proxyConfiguration": { "useApifyProxy": true } }' | apify call automation-lab/chatbot-arena-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/chatbot-arena-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Chatbot Arena Scraper", "description": "Scrapes the Chatbot Arena (arena.ai) leaderboard to extract LLM model rankings, Elo scores, confidence intervals, vote counts, and category-specific ratings from human preference battles.", "version": "0.1", "x-build-id": "KiX1vq0M55knVi5A0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~chatbot-arena-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-chatbot-arena-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~chatbot-arena-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-chatbot-arena-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~chatbot-arena-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-chatbot-arena-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "categories": { "title": "Leaderboard categories", "type": "array", "description": "Which leaderboard categories to scrape. Leave empty to scrape all categories.", "items": { "type": "string", "enum": [ "text", "code", "vision", "document", "text-to-image", "image-edit", "search", "text-to-video", "image-to-video", "video-edit" ] }, "default": [ "text" ] }, "maxResults": { "title": "Max results per category", "minimum": 0, "type": "integer", "description": "Maximum number of models to extract per category. Set to 0 for all models.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to be used.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```