# LMArena LLM Leaderboard Scraper (`jungle_synthesizer/lmarena-llm-leaderboard-scraper`) Actor Scrape the LMArena (Chatbot Arena) ELO leaderboard — ranks, ratings, vote counts, and confidence intervals across all arena variants (text, code, vision, document, image, video, and more). Returns one row per model per leaderboard variant. - **URL**: https://apify.com/jungle\_synthesizer/lmarena-llm-leaderboard-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **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. 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 ## LMArena LLM Leaderboard Scraper Scrape the [LMArena Chatbot Arena](https://arena.ai/leaderboard) ELO leaderboard — the most widely cited blind-evaluation ranking for large language models. Every major model launch references its Chatbot Arena ELO position. This actor returns one row per model per leaderboard variant, covering all 11 arena categories in a single run. ### What You Get Each record contains: - **Leaderboard variant** — arena slug (`text`, `code`, `vision`, `document`, `text-to-image`, `image-edit`, `image-to-code`, `search`, `text-to-video`, `image-to-video`, `video-to-video`) - **Rank** — current position with 95% confidence interval bounds (`rank_upper`, `rank_lower`) - **Model name** and **organization** - **ELO score** with confidence interval low/high - **Vote count** — number of blind battles used to compute this rating - **License** — Proprietary, Apache-2.0, etc. - **Context window tokens** - **Input/output pricing** (per million tokens, where publicly available) - **Profile URL** — link to official model announcement - **Scraped at** — ISO 8601 timestamp ### Usage #### Basic run (all arenas, default 10 records) No configuration needed — just run with default settings to get a sample of the leaderboard. #### Full leaderboard Set `maxItems` to a high number (e.g., 1000) to get all ~900+ entries across all arenas. #### Filter by arena Use the `arenas` input to limit results to specific variants: ```json { "maxItems": 500, "arenas": ["text", "code", "vision"] } ```` **Available arena slugs:** - `text` — overall text/conversation benchmark (the main leaderboard) - `code` — coding tasks - `vision` — image understanding - `document` — document Q\&A - `text-to-image` — image generation - `image-edit` — image editing - `image-to-code` — screenshot-to-code - `search` — search tasks - `text-to-video` — video generation - `image-to-video` — image-to-video - `video-to-video` — video editing ### Input Schema | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum total records to return | | `arenas` | array | `[]` (all) | Arena slugs to include. Empty = return all arenas | ### Output Sample ```json { "leaderboard_variant": "text", "rank": 1, "rank_upper": 1, "rank_lower": 4, "model_name": "claude-opus-4-6-thinking", "organization": "Anthropic", "elo_score": 1502.17, "elo_confidence_low": 1497.91, "elo_confidence_high": 1506.43, "vote_count": 34186, "license": "Proprietary", "context_window_tokens": 1000000, "input_price_per_million": 5, "output_price_per_million": 25, "profile_url": "https://www.anthropic.com/news/claude-opus-4-6", "scraped_at": "2026-06-01T15:05:33.265Z" } ``` ### Technical Notes - **Single HTTP request** — all arena data is embedded in the page's server-side rendered response (~4.6 MB). No pagination, no API calls. - **No proxy required** — the site serves datacenter IPs without blocking. - **Fast** — typical run completes in under 30 seconds. - The leaderboard updates daily as new arena battles are processed. ### Use Cases - Track ELO rankings over time for specific models or organizations - Compare models across different task categories (code vs. vision vs. overall) - Feed into model-routing or evaluation pipelines - Monitor competitive positioning for newly launched models - Research and academic benchmarking datasets # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of records to scrape. Each leaderboard entry across all arena variants counts as one record. ## `arenas` (type: `array`): Limit results to specific arena slugs (e.g. \["text", "code", "vision"]). Leave empty to return all arenas. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "arenas": [] } ``` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "arenas": [] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/lmarena-llm-leaderboard-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "arenas": [], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/lmarena-llm-leaderboard-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "arenas": [] }' | apify call jungle_synthesizer/lmarena-llm-leaderboard-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/lmarena-llm-leaderboard-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LMArena LLM Leaderboard Scraper", "description": "Scrape the LMArena (Chatbot Arena) ELO leaderboard — ranks, ratings, vote counts, and confidence intervals across all arena variants (text, code, vision, document, image, video, and more). Returns one row per model per leaderboard variant.", "version": "0.1", "x-build-id": "Y0YmQcrCajLRxDVGp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~lmarena-llm-leaderboard-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-lmarena-llm-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/jungle_synthesizer~lmarena-llm-leaderboard-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-lmarena-llm-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/jungle_synthesizer~lmarena-llm-leaderboard-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-lmarena-llm-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", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape. Each leaderboard entry across all arena variants counts as one record." }, "arenas": { "title": "Arena Filter", "type": "array", "description": "Limit results to specific arena slugs (e.g. [\"text\", \"code\", \"vision\"]). Leave empty to return all arenas.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```