Qwen-VL

Run any Qwen vision model - natively or via OpenRouter.

Run any Qwen vision-language model - natively on Roboflow infrastructure or via OpenRouter.

You can specify arbitrary text prompts or predefined ones, the block supports the following types of prompt:

• 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

🛠️ Backend selection

• Native (Roboflow) - small Qwen-VL models (0.8B–7B) run on the same infrastructure as your other Roboflow models. Lower latency. Recommended for tasks like OCR, captioning, and visual question answering.

• OpenRouter - large hosted Qwen models (9B–397B) reached via OpenRouter. Defaults to a Roboflow-managed API key and bills your Roboflow credits. Paste your own sk-or-... key in the api_key field to bypass Roboflow billing. Recommended for structured tasks that benefit from larger models (classification, object-detection, structured-answering).

The model_version dropdown lists every supported variant; each is bound to one backend. A validator catches mismatches between your selected backend and model.

🔒 Privacy filter (OpenRouter only)

• 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.

Type identifier

Use the following identifier in step "type" field: roboflow_core/qwen_vlm@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 (only used when backend=openrouter). Defaults to Roboflow's managed key. Provide your own sk-or-... key to call OpenRouter directly without Roboflow billing..	✅
privacy_level	str	Provider privacy filter (only used when backend=openrouter). 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	Sampling temperature (only used when backend=openrouter). The native Qwen-VL runtime doesn't accept a temperature knob. Range 0.0-2.0 - higher = more random / "creative" generations..	✅
max_concurrent_requests	int	Maximum number of OpenRouter requests to run in parallel for a batch of images (only used when backend=openrouter). The native backend processes images sequentially. If unset, falls back to the global Workflows Execution Engine default. Restrict this if you hit OpenRouter rate limits..	❌
backend	str	Where to run inference. Native = Roboflow infrastructure. OpenRouter = large hosted Qwen models via OpenRouter..	❌
model_version	str	Native Qwen-VL variant. Pick a pre-trained model or Fine-tuned model to use a Qwen3 fine-tune from your workspace..	✅
fine_tuned_model_id	str	Fine-tuned Qwen3-VL model from your workspace, in workspace/version form..	✅
openrouter_model_version	str	OpenRouter-hosted Qwen variant..	✅
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the Qwen model.	✅
enable_thinking	bool	Enable Qwen3.5-VL's reasoning mode, where the model emits thinking tokens before its answer. The reasoning trace is returned in the thinking output. Only the Qwen 3.5 VL 2B checkpoint (and Qwen3-VL fine-tunes derived from it) supports this; ignored elsewhere..	❌
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 Qwen-VL in version v1 has.

Input and output bindings

• input

  • api_key (Union[ROBOFLOW_MANAGED_KEY, secret, string]): OpenRouter API key (only used when backend=openrouter). Defaults to Roboflow's managed key. Provide your own sk-or-... key to call OpenRouter directly without Roboflow billing..

  • temperature (float): Sampling temperature (only used when backend=openrouter). The native Qwen-VL runtime doesn't accept a temperature knob. Range 0.0-2.0 - higher = more random / "creative" generations..

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

  • model_version (string): Native Qwen-VL variant. Pick a pre-trained model or Fine-tuned model to use a Qwen3 fine-tune from your workspace..

  • fine_tuned_model_id (Union[roboflow_model_id, string]): Fine-tuned Qwen3-VL model from your workspace, in workspace/version form..

  • openrouter_model_version (string): OpenRouter-hosted Qwen variant..

  • prompt (string): Text prompt to the Qwen 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.

  • thinking (string): String value.

Example JSON definition

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/qwen_vlm@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",
	    "backend": "<block_does_not_provide_example>",
	    "model_version": "Qwen 3.5 VL 2B",
	    "fine_tuned_model_id": "your-workspace/3",
	    "openrouter_model_version": "Qwen 3.6 27B",
	    "task_type": "<block_does_not_provide_example>",
	    "prompt": "my prompt",
	    "enable_thinking": "<block_does_not_provide_example>",
	    "output_structure": {
	        "my_key": "description"
	    },
	    "classes": [
	        "class-a",
	        "class-b"
	    ]
	}