# DeepInfra Models Scraper (`automation-lab/deepinfra-models-scraper`) Actor Scrape all ML inference models from DeepInfra including model ID, type, creator, pricing (input/output token costs), context window size, and other metadata. - **URL**: https://apify.com/automation-lab/deepinfra-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, 0 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 ## DeepInfra Models Scraper ### What does DeepInfra Models Scraper do? **DeepInfra Models Scraper** extracts the complete catalog of ML inference models available on [DeepInfra](https://deepinfra.com/models) — no API key, no login, and no coding required. Run the actor and get structured data for every model including **pricing per million tokens, context window sizes, model type, creator/organization, and model metadata**. The actor fetches DeepInfra's public models page using a single HTTP request and extracts the embedded `__NEXT_DATA__` JSON. No browser automation, no Playwright, no proxy required. Every model is returned as a clean JSON record ready to export to CSV, Google Sheets, or any downstream pipeline. Use this actor to monitor DeepInfra model pricing changes, compare inference costs against other providers (Groq, Fireworks, Together AI, OpenRouter), or automate competitive analysis of fast-inference LLM platforms. --- ### Who is it for? **🤖 AI developers and backend engineers** - Find the exact model ID and current pricing before integrating DeepInfra into your application - Verify context window sizes and supported modalities for planning token budgets - Automate checks for new model releases or pricing changes **📊 ML researchers and data scientists** - Track DeepInfra pricing trends over time by scheduling recurring runs - Compare input vs output token prices across text generation, embedding, and multimodal models - Build datasets for competitive pricing analysis across inference providers **💰 Cost optimization and FinOps teams** - Compare DeepInfra token prices to optimize your inference provider selection - Monitor price changes across hundreds of hosted models - Benchmark DeepInfra rates against Together AI, Groq, Fireworks, and other fast-inference providers **🏢 AI product managers and strategists** - Track which new models DeepInfra adds (Llama, DeepSeek, Qwen, Gemma, and more) - Monitor when models are deprecated and what they are replaced by - Build dashboards comparing DeepInfra's inference pricing to the broader market --- ### Why use DeepInfra Models Scraper? - **No API key required** — DeepInfra's models catalog is fully public - **Single HTTP request** — fetches all 290+ models in one call with zero JS rendering - **Zero proxy cost** — no browser automation or residential proxies needed - **Covers all model types** — text generation, embeddings, text-to-image, text-to-speech, automatic speech recognition, text-to-video, reranker, and more - **Structured pricing fields** — separate fields for input/output token prices in both cents-per-token and USD-per-million-tokens formats - **Filter by type** — narrow results to just the model types you need - **Deprecated model tracking** — optionally include deprecated models with `replacedBy` field to track model lineage - **Pay-per-event pricing** — pay only for models extracted, not idle compute time - **Schedule and automate** — run daily or weekly to track pricing and model changes over time - **Export anywhere** — JSON, CSV, Excel, Google Sheets, or push via API and webhook --- ### What data does it extract? For each model on DeepInfra, the actor extracts: | Field | Description | |-------|-------------| | `modelId` | Full model identifier (e.g., `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`) | | `name` | Display name (e.g., `DeepSeek-V4-Pro`) | | `owner` | Creator organization (e.g., `deepseek-ai`, `meta-llama`) | | `modelType` | Model category (`text-generation`, `embeddings`, `text-to-image`, etc.) | | `description` | Model description | | `pricing.type` | Pricing structure type (`tokens`, `input_tokens`) | | `pricing.centsPerInputToken` | Input price in cents per token | | `pricing.centsPerOutputToken` | Output price in cents per token | | `pricing.inputPriceUsdPerMillionTokens` | Input price in USD per million tokens | | `pricing.outputPriceUsdPerMillionTokens` | Output price in USD per million tokens | | `pricing.cachedInputRateMultiplier` | Discount multiplier for cached inputs | | `maxTokens` | Context window size in tokens | | `tags` | Feature tags (`tools`, `json`, `reasoning`, `structured-output`, etc.) | | `quantization` | Model quantization (e.g., `fp4`, `fp8`) | | `isPartner` | Whether this is a DeepInfra partner model | | `isDeprecated` | Whether the model is deprecated | | `replacedBy` | Model ID of the replacement (for deprecated models) | | `modelUrl` | Direct URL to the model page on DeepInfra | | `scrapedAt` | ISO 8601 timestamp of when the data was scraped | --- ### How much does it cost to scrape DeepInfra models? DeepInfra Models Scraper uses **pay-per-event pricing**: | Event | BRONZE price | FREE price | |-------|-------------|-----------| | Run started (one-time) | $0.005 | $0.005 | | Per model extracted | $0.001 | $0.00115 | Subscribers on higher tiers (SILVER through DIAMOND) pay progressively less per model extracted. **Typical run cost (BRONZE):** - Full catalog (~190 non-deprecated models): ~$0.195 (190 × $0.001 + $0.005 start) - All models including deprecated (~292): ~$0.297 (292 × $0.001 + $0.005 start) There is no proxy cost — DeepInfra's models page is publicly accessible. --- ### How to use DeepInfra Models Scraper 1. Go to the actor page and click **Try for free** 2. Configure any optional filters (model type, include deprecated) 3. Click **Start** and wait a few seconds 4. Download results as JSON, CSV, or Excel — or connect to your pipeline via webhook No API key or login is required. --- ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `modelType` | String | _(all)_ | Filter to a specific model type. Options: `text-generation`, `embeddings`, `text-to-image`, `text-to-speech`, `automatic-speech-recognition`, `text-to-video`, `reranker`, `zero-shot-image-classification` | | `includeDeprecated` | Boolean | `false` | When `true`, includes deprecated models in the output | | `maxRequestRetries` | Integer | `3` | Number of retry attempts for failed HTTP requests | --- ### Output example ```json { "modelId": "deepseek-ai/DeepSeek-V4-Pro", "name": "DeepSeek-V4-Pro", "owner": "deepseek-ai", "modelType": "text-generation", "description": "DeepSeek V4 Pro is an MoE model with 1.6T total parameters (49B active) and a 1M-token context window. Built for advanced reasoning, coding, and long-running agent tasks.", "pricing": { "type": "tokens", "centsPerInputToken": 0.000174, "centsPerOutputToken": 0.000348, "inputPriceUsdPerMillionTokens": 1.74, "outputPriceUsdPerMillionTokens": 3.48, "cachedInputRateMultiplier": 0.08333333 }, "maxTokens": 65536, "tags": ["structured-output", "openai", "tools", "json", "reasoning", "featured"], "quantization": "fp4", "isPartner": false, "isDeprecated": false, "replacedBy": null, "modelUrl": "https://deepinfra.com/deepseek-ai/DeepSeek-V4-Pro", "scrapedAt": "2026-04-25T08:40:00.000Z" } ```` *** ### Tips and best practices 💡 **Schedule for price monitoring** — DeepInfra regularly adds models and updates pricing. Schedule weekly runs and compare datasets to track changes automatically. 💡 **Use `modelType` to narrow results** — Filter by `text-generation` for LLMs, `embeddings` for vector search models, or `automatic-speech-recognition` for Whisper-style models. 💡 **Check `cachedInputRateMultiplier`** — Many models offer cached context pricing. A multiplier of `0.08` means you pay only 8% of the normal input price for cached tokens. 💡 **Monitor deprecated models** — Enable `includeDeprecated` and check `replacedBy` to track model lineage and plan migrations before models are removed. 💡 **Combine with other model scrapers** — Pair this actor with [Groq Models Scraper](https://apify.com/automation-lab/groq-models-scraper), [OpenRouter Models Scraper](https://apify.com/automation-lab/openrouter-models-scraper), or [Fireworks AI Scraper](https://apify.com/automation-lab/fireworks-ai-scraper) to build a comprehensive cross-provider pricing dashboard. 💡 **Use `tags` for capability filtering** — The `tools` tag means the model supports function calling, `reasoning` means chain-of-thought reasoning is enabled, and `structured-output` means JSON schema output is supported. *** ### Integrations **Google Sheets** — Export results directly to Google Sheets to build a living pricing dashboard. Schedule daily runs to track changes. **Zapier / Make** — Connect the actor's webhook to Zapier or Make to trigger alerts when new models appear or prices change. **Apify API** — Fetch results programmatically using the Apify API. Store results in a named dataset for easy comparison across runs. **Database export** — Use the JSON export to load model data into PostgreSQL, BigQuery, or any other database for long-term trend analysis. *** ### API usage You can trigger this actor via the Apify API and retrieve results programmatically. **cURL** ```bash curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~deepinfra-models-scraper/runs" \ -H "Authorization: Bearer YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"modelType": "text-generation"}' ``` **Node.js** (`npm install apify-client`) ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/deepinfra-models-scraper').call({ modelType: 'text-generation', includeDeprecated: false, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Extracted ${items.length} models`); console.log(items[0]); ``` **Python** (`pip install apify-client`) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("automation-lab/deepinfra-models-scraper").call(run_input={ "modelType": "text-generation", "includeDeprecated": False, }) items = client.dataset(run["defaultDatasetId"]).list_items().items print(f"Extracted {len(items)} models") print(items[0]) ``` *** ### Use with Claude AI (MCP) This actor is compatible with the [Apify MCP Server](https://apify.com/apify/actors-mcp-server), which lets you run it directly from Claude AI, Cursor, VS Code, or any other MCP-compatible client — no coding required. **Claude Code** (terminal) ```bash claude mcp add --transport http https://mcp.apify.com?tools=automation-lab/deepinfra-models-scraper ``` **Claude Desktop / Cursor / VS Code** — add to your MCP config JSON: ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com?tools=automation-lab/deepinfra-models-scraper", "headers": { "Authorization": "Bearer YOUR_APIFY_API_TOKEN" } } } } ``` **Example prompts:** - *"Run DeepInfra Models Scraper and give me all text-generation models under $1 per million input tokens."* - *"Use DeepInfra Models Scraper to find models that support function calling (look for 'tools' in the tags field) and sort by output price."* - *"Run DeepInfra Models Scraper and compare embedding model prices — show modelId, owner, and inputPriceUsdPerMillionTokens."* *** ### Legality and terms of service DeepInfra's models catalog is publicly accessible without a login. This actor only scrapes data that is freely available on deepinfra.com/models. Always review [DeepInfra's Terms of Service](https://deepinfra.com/terms) before using scraped data commercially. *** ### FAQ **Does it require a DeepInfra API key?** No. The models page is publicly accessible without authentication. **How many models does it return?** DeepInfra currently lists 292 models total, ~190 of which are non-deprecated. The count grows as DeepInfra adds new models. **Can I filter by model type?** Yes. Use the `modelType` input parameter to restrict results to a specific category such as `text-generation` or `embeddings`. **How often should I run it?** Weekly runs are sufficient for most use cases. For production pricing monitoring, daily runs are recommended. **Does it use proxies?** No. DeepInfra's public page is accessible without proxies, keeping costs minimal. *** ### Related scrapers - [Groq Models Scraper](https://apify.com/automation-lab/groq-models-scraper) — Groq inference models with speed benchmarks and rate limits - [OpenRouter Models Scraper](https://apify.com/automation-lab/openrouter-models-scraper) — 300+ models from OpenRouter with unified pricing - [Fireworks AI Scraper](https://apify.com/automation-lab/fireworks-ai-scraper) — Fireworks AI inference platform model catalog - [Artificial Analysis Scraper](https://apify.com/automation-lab/artificial-analysis-scraper) — Cross-provider AI benchmark and pricing comparisons # Actor input Schema ## `modelType` (type: `string`): Filter results to a specific model type. Leave empty to return all model types. Valid values: text-generation, embeddings, text-to-image, text-to-speech, automatic-speech-recognition, text-to-video, reranker, zero-shot-image-classification. ## `includeDeprecated` (type: `boolean`): When enabled, deprecated models are included in the output. Deprecated models are excluded by default. ## `maxRequestRetries` (type: `integer`): Number of retry attempts for failed HTTP requests. ## Actor input object example ```json { "modelType": "", "includeDeprecated": false, "maxRequestRetries": 3 } ``` # 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 = { "modelType": "", "includeDeprecated": false, "maxRequestRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/deepinfra-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 = { "modelType": "", "includeDeprecated": False, "maxRequestRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/deepinfra-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 '{ "modelType": "", "includeDeprecated": false, "maxRequestRetries": 3 }' | apify call automation-lab/deepinfra-models-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/deepinfra-models-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DeepInfra Models Scraper", "description": "Scrape all ML inference models from DeepInfra including model ID, type, creator, pricing (input/output token costs), context window size, and other metadata.", "version": "0.1", "x-build-id": "DkhgRDcahKRn2v3zs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~deepinfra-models-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-deepinfra-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~deepinfra-models-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-deepinfra-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~deepinfra-models-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-deepinfra-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "modelType": { "title": "Model type filter", "enum": [ "", "text-generation", "embeddings", "text-to-image", "text-to-speech", "automatic-speech-recognition", "text-to-video", "reranker", "zero-shot-image-classification" ], "type": "string", "description": "Filter results to a specific model type. Leave empty to return all model types. Valid values: text-generation, embeddings, text-to-image, text-to-speech, automatic-speech-recognition, text-to-video, reranker, zero-shot-image-classification.", "default": "" }, "includeDeprecated": { "title": "Include deprecated models", "type": "boolean", "description": "When enabled, deprecated models are included in the output. Deprecated models are excluded by default.", "default": false }, "maxRequestRetries": { "title": "Max request retries", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of retry attempts for failed HTTP requests.", "default": 3 } } }, "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 } } } } } } } } } } ```