# OpenRouter AI Models Directory Scraper (`klondikeking/openrouter-models-scraper`) Actor Extract structured data for all AI models available on OpenRouter, including pricing, capabilities, context lengths, and supported parameters. Perfect for comparing LLM options and building model catalogs. - **URL**: https://apify.com/klondikeking/openrouter-models-scraper.md - **Developed by:** [Pierrick McD0nald](https://apify.com/klondikeking) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 model extracteds 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 ## OpenRouter AI Models Directory Scraper — Extract LLM Pricing & Capabilities The OpenRouter AI Models Directory Scraper extracts structured data for every AI model available on the OpenRouter platform. Whether you are building a model comparison tool, monitoring price changes across providers, or cataloging LLM capabilities for your organization, this Actor delivers clean, structured data with zero configuration. OpenRouter aggregates hundreds of large language models and multimodal AI providers behind a single API. Keeping track of which models support vision, audio, or tool calling — and what each costs per token — is a manual, time-consuming process. This Actor automates that entirely. ### Use Cases - **LLM Comparison Dashboards** — Build internal or public tools that let users filter models by context length, modality, or price. - **Price Monitoring** — Track how per-token pricing evolves across providers and alert stakeholders when cheaper alternatives appear. - **Model Catalogs** — Populate your product with up-to-date model metadata including supported parameters, tokenizers, and moderation flags. - **Capacity Planning** — Understand max completion tokens and context windows to match the right model to your workload. ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `maxItems` | Number | No | Maximum models to return (default: 100, max: 500) | | `includePricing` | Boolean | No | Include per-token pricing data (default: true) | | `includeArchitecture` | Boolean | No | Include modality, tokenizer, and I/O types (default: true) | | `proxyConfiguration` | Object | No | Proxy configuration for requests | ### Output The Actor outputs a dataset with the following fields for each model: ```json { "id": "anthropic/claude-3.5-sonnet", "name": "Anthropic: Claude 3.5 Sonnet", "description": "Claude 3.5 Sonnet raises the industry bar...", "context_length": 200000, "modality": "text+image->text", "input_modalities": ["text", "image"], "output_modalities": ["text"], "tokenizer": "Claude", "instruct_type": null, "prompt_price": "0.000003", "completion_price": "0.000015", "image_price": "0.000004", "audio_price": null, "web_search_price": null, "max_completion_tokens": 8192, "is_moderated": false, "supported_parameters": ["max_tokens", "temperature", "top_p", "tools"], "knowledge_cutoff": "2024-04", "expiration_date": null, "created": 1718304000, "hugging_face_id": null } ```` ### Pricing Pay per event: **$0.001 per model extracted**. A typical run extracting 100 models costs $0.10. ### Limitations - Data reflects the current state of the OpenRouter public API. Deprecated models may still appear until they are removed by OpenRouter. - Pricing fields are returned as strings and represent per-token costs in USD. - The Actor makes a single API call to OpenRouter. Rate limits are respected through built-in retry logic. ### FAQ **Q: Do I need an OpenRouter API key?** A: No. The models directory endpoint is public and requires no authentication. **Q: How often is the data updated?** A: The Actor fetches live data on every run. Run it on a schedule to keep your catalog current. **Q: Can I filter models by provider?** A: The Actor returns all models. Filter the resulting dataset by the `id` prefix (e.g., `anthropic/`, `openai/`) in your downstream pipeline. ### Changelog - **v1.0.0** — Initial release. Extracts model metadata, pricing, and architecture from the OpenRouter public API. # Actor input Schema ## `maxItems` (type: `integer`): Limit the number of models returned (default: 100, max: 500) ## `includePricing` (type: `boolean`): Include per-token pricing information for each model ## `includeArchitecture` (type: `boolean`): Include modality, tokenizer, and input/output modalities for each model ## `proxyConfiguration` (type: `object`): Configure proxy settings for the scraper ## Actor input object example ```json { "maxItems": 50, "includePricing": true, "includeArchitecture": true, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `dataset` (type: `string`): Dataset containing extracted OpenRouter model data ## `stats` (type: `string`): Key-value store with extraction statistics # 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 = { "maxItems": 50, "includePricing": true, "includeArchitecture": true, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("klondikeking/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 = { "maxItems": 50, "includePricing": True, "includeArchitecture": True, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("klondikeking/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 '{ "maxItems": 50, "includePricing": true, "includeArchitecture": true, "proxyConfiguration": { "useApifyProxy": true } }' | apify call klondikeking/openrouter-models-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=klondikeking/openrouter-models-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenRouter AI Models Directory Scraper", "description": "Extract structured data for all AI models available on OpenRouter, including pricing, capabilities, context lengths, and supported parameters. Perfect for comparing LLM options and building model catalogs.", "version": "1.0", "x-build-id": "xeJTiTu8zC72JVhKd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/klondikeking~openrouter-models-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-klondikeking-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/klondikeking~openrouter-models-scraper/runs": { "post": { "operationId": "runs-sync-klondikeking-openrouter-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/klondikeking~openrouter-models-scraper/run-sync": { "post": { "operationId": "run-sync-klondikeking-openrouter-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxItems": { "title": "Maximum models to extract", "minimum": 1, "maximum": 500, "type": "integer", "description": "Limit the number of models returned (default: 100, max: 500)", "default": 100 }, "includePricing": { "title": "Include pricing data", "type": "boolean", "description": "Include per-token pricing information for each model", "default": true }, "includeArchitecture": { "title": "Include architecture details", "type": "boolean", "description": "Include modality, tokenizer, and input/output modalities for each model", "default": true }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxy settings for the scraper" } } }, "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 } } } } } } } } } } ```