# OpenRouter LLM Model Pricing Scraper (`jungle_synthesizer/openrouter-llm-model-pricing-scraper`) Actor Fetch the full OpenRouter model catalog with pricing, context length, capabilities, and supported parameters as structured data. Returns all 350+ LLMs with prompt/completion/cache prices, modalities, tokenizer details, knowledge cutoffs, and per-provider routing links. - **URL**: https://apify.com/jungle\_synthesizer/openrouter-llm-model-pricing-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 ## OpenRouter LLM Model Pricing Scraper Fetches the full OpenRouter model catalog — pricing, context length, capabilities, and supported parameters — as structured JSON. One API call returns everything. OpenRouter publishes a public endpoint that lists every model it routes to: pricing in USD per token, context windows, supported parameters like `tools` and `structured_outputs`, modalities, tokenizer details, and knowledge cutoffs. This actor pulls that data and formats it into clean records you can actually use for model selection, cost estimation, and compatibility filtering. ### What It Returns One record per model. Fields that matter: | Field | Type | Description | |-------|------|-------------| | `model_id` | string | Provider/model slug (e.g. `openai/gpt-4o`) | | `canonical_slug` | string | Versioned slug with datestamp | | `name` | string | Display name | | `provider` | string | Model provider (openai, anthropic, meta-llama, etc.) | | `context_length` | integer | Maximum context window in tokens | | `max_completion_tokens` | integer | Maximum output tokens | | `modality` | string | Input/output modality (e.g. `text+image->text`) | | `input_modalities` | string | Pipe-delimited list of input types | | `output_modalities` | string | Pipe-delimited list of output types | | `prompt_price_usd_per_token` | number | Price per input token in USD | | `completion_price_usd_per_token` | number | Price per output token in USD | | `input_cache_read_price_usd_per_token` | number | Cached input price per token | | `input_cache_write_price_usd_per_token` | number | Cache write price per token | | `image_price_usd_per_image` | number | Per-image pricing (where applicable) | | `supported_parameters` | string | Pipe-delimited list (temperature, tools, structured_outputs, reasoning, ...) | | `tokenizer` | string | Tokenizer type | | `instruct_type` | string | Fine-tuning format | | `knowledge_cutoff` | string | Training data cutoff date | | `expiration_date` | string | Deprecation date for legacy models | | `endpoints_url` | string | URL to per-provider routing data | | `profile_url` | string | Model page on openrouter.ai | | `scraped_at` | string | ISO timestamp of the run | Most scrapers skip cache-read pricing. This one does not — and cache pricing is the cost lever most teams are actually watching in 2025-26. ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | 500 | Maximum records to return. Leave blank to get all 350+ models. | No API key required. No proxy needed. The OpenRouter catalog is a public endpoint. ### Usage Point it at the defaults and run. The whole catalog comes back in under 5 seconds. Set `maxItems` if you only need a sample — useful for testing downstream pipelines before a full pull. ```json { "maxItems": 50 } ```` ### When to Use This - Programmatic model selection by capability, context length, or price - LLM cost estimation and budgeting across providers - Tracking model deprecations via `expiration_date` - Filtering by `supported_parameters` to find models that support `tools`, `structured_outputs`, or `reasoning` - Pairing with competitor pricing data for FinOps dashboards The catalog updates regularly as OpenRouter adds new models. Run it on a schedule to keep your data current. ### Data Notes - Pricing fields are `null` when a model is free or pricing is not yet set - `supported_parameters` lists vary significantly by model — not all models support `tools` or `structured_outputs` - `knowledge_cutoff` is often `null` for third-party models where OpenRouter does not publish the information - Array fields (modalities, parameters) are returned as pipe-delimited strings for flat schema compatibility *** Questions or issues? Use the feedback fields in the input form or contact us at . # 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 models to return. Leave blank to fetch all models (350+). ## 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 } ``` # 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 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/openrouter-llm-model-pricing-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, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/openrouter-llm-model-pricing-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 }' | apify call jungle_synthesizer/openrouter-llm-model-pricing-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/openrouter-llm-model-pricing-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenRouter LLM Model Pricing Scraper", "description": "Fetch the full OpenRouter model catalog with pricing, context length, capabilities, and supported parameters as structured data. Returns all 350+ LLMs with prompt/completion/cache prices, modalities, tokenizer details, knowledge cutoffs, and per-provider routing links.", "version": "0.1", "x-build-id": "T7bN6P1aJA8i1bDBe" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~openrouter-llm-model-pricing-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-openrouter-llm-model-pricing-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~openrouter-llm-model-pricing-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-openrouter-llm-model-pricing-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~openrouter-llm-model-pricing-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-openrouter-llm-model-pricing-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": { "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 models to return. Leave blank to fetch all models (350+).", "default": 500 } } }, "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 } } } } } } } } } } ```