OpenRouter

Run any OpenRouter model by pasting its model slug.

Run any vision-language model available on OpenRouter by pasting its model slug into the model_id field - e.g. openai/gpt-4o-mini, anthropic/claude-3.5-sonnet, google/gemini-2.5-pro, qwen/qwen3.6-27b.

This is the generic escape hatch for OpenRouter - when you want a model that doesn't have a dedicated block (Qwen-VL, Kimi, Gemma, Llama Vision) and you want to try it out without waiting for a new block to be added.

The block supports the standard VLM task-type surface:

• Open Prompt (unconstrained) - Use any prompt to generate a raw response

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Visual Question Answering (visual-question-answering) - Model answers the question you submit in the prompt

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Single-Label Classification (classification) - Model classifies the image content as one of the provided classes

• Multi-Label Classification (multi-label-classification) - Model classifies the image content as one or more of the provided classes

• Unprompted Object Detection (object-detection) - Model detects and returns the bounding boxes for prominent objects in the image

• Structured Output Generation (structured-answering) - Model returns a JSON response with the specified fields

🛠️ API key

By default the block uses the Roboflow-managed OpenRouter key and bills your Roboflow credits - no extra setup needed. To bypass Roboflow billing, paste your own sk-or-... key into the api_key field.

🔒 Privacy filter

• No data collection (default) – providers may not train on your inputs.

• Allow data collection – broader provider pool.

• Zero data retention – strictest, restricts to providers that retain nothing.

Model availability

OpenRouter exposes hundreds of models with different capabilities. Not every model supports image inputs, and some are text-only or reasoning-only. If the model can't return a visible response (e.g. a reasoning model that burns all of max_tokens on internal thinking), try increasing max_tokens or pick a different model.

Type identifier

Use the following identifier in step "type" field: roboflow_core/openrouter@v1 to add the block as a step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
api_key	str	OpenRouter API key. Defaults to Roboflow's managed key, billed in credits via Roboflow. Provide your own sk-or-... key to call OpenRouter directly without Roboflow billing..	✅
privacy_level	str	Provider privacy filter. Stricter levels reduce the pool of providers and may increase per-call cost on the managed key..	❌
max_tokens	int	Maximum number of tokens the model can generate in its response..	❌
temperature	float	Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..	✅
max_concurrent_requests	int	Number of concurrent requests for batches of images. If not given - block defaults to value configured globally in Workflows Execution Engine. Restrict if you hit rate limits..	❌
model_id	str	OpenRouter model slug, e.g. openai/gpt-4o-mini, anthropic/claude-3.5-sonnet, qwen/qwen3.6-27b. See https://openrouter.ai/models for the full list..	✅
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to send to the model..	✅
output_structure	Dict[str, str]	Dictionary with structure of expected JSON response..	❌
classes	List[str]	List of classes to be used..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Runtime compatibility

requires_internet - air-gapped / offline deployments : This block depends on a service that is not reachable from fully offline / air-gapped deployments.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds OpenRouter in version v1 has.

Input and output bindings

• input

  • api_key (Union[ROBOFLOW_MANAGED_KEY, secret, string]): OpenRouter API key. Defaults to Roboflow's managed key, billed in credits via Roboflow. Provide your own sk-or-... key to call OpenRouter directly without Roboflow billing..

  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..

  • images (image): The image to infer on..

  • model_id (string): OpenRouter model slug, e.g. openai/gpt-4o-mini, anthropic/claude-3.5-sonnet, qwen/qwen3.6-27b. See https://openrouter.ai/models for the full list..

  • prompt (string): Text prompt to send to the model..

  • classes (list_of_values): List of classes to be used..

• output

  • output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.

  • classes (list_of_values): List of values of any type.

Example JSON definition

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/openrouter@v1",
	    "api_key": "rf_key:account",
	    "privacy_level": "<block_does_not_provide_example>",
	    "max_tokens": "<block_does_not_provide_example>",
	    "temperature": "<block_does_not_provide_example>",
	    "max_concurrent_requests": "<block_does_not_provide_example>",
	    "images": "$inputs.image",
	    "model_id": "openai/gpt-4o-mini",
	    "task_type": "<block_does_not_provide_example>",
	    "prompt": "my prompt",
	    "output_structure": {
	        "my_key": "description"
	    },
	    "classes": [
	        "class-a",
	        "class-b"
	    ]
	}