> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deploy/supported-models/qwen3-5.md).

# Qwen3.5

Qwen3.5 is Alibaba's vision-language model family. It accepts an image and a text prompt and returns a text response. Two pretrained checkpoints are available:

| Alias          | Parameters |
| -------------- | ---------- |
| `qwen3_5-0.8b` | 0.8B       |
| `qwen3_5-2b`   | 2B         |

## Using Qwen 3.5 VL via Workflows

Qwen 3.5 VL is available as a preconfigured [Workflow](/workflows/what-is-workflows.md) on the "Open-Source Models" tab of the Models page. Select "Qwen VL", choose a model variant and prompt, then click "Test API" to fork the Workflow into your Workspace and start running inference.

The Workflow uses the unified `qwen_vlm@v1` block, which supports multiple Qwen VL generations:

| Model            | Parameters |
| ---------------- | ---------- |
| Qwen 3.5 VL 0.8B | 0.8B       |
| Qwen 3.5 VL 2B   | 2B         |
| Qwen 3 VL 2B     | 2B         |
| Qwen 2.5 VL 7B   | 7B         |

## Using Qwen 3.5 VL via Inference SDK

{% hint style="info" %}
Direct Inference SDK calls to Qwen3.5 require a [Dedicated Deployment](/deploy/dedicated-deployments.md) or [self-hosted Inference](https://inference.roboflow.com/). For hosted access, use the Workflow path described above.
{% endhint %}

### Code sample

Install the [Inference SDK](https://inference.roboflow.com/):

```bash
pip install inference-sdk
```

Set `api_url` to your Dedicated Deployment URL or a local Inference server. Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable.

```python
import os
import urllib.request
from inference_sdk import InferenceHTTPClient

image_url = "https://media.roboflow.com/notebooks/examples/dog.jpeg"
image_path = "dog.jpeg"
urllib.request.urlretrieve(image_url, image_path)

client = InferenceHTTPClient(
    api_url="https://your-deployment.roboflow.cloud",
    api_key=os.getenv("API_KEY"),
)
result = client.infer_lmm(
    image_path,
    model_id="qwen3_5-2b",
    prompt="Describe this image briefly.",
    max_new_tokens=128,
)
print(result["response"])
```

The code above prints the model response to the terminal:

```
This is a close-up, eye-level shot of a blue beagle dog being carried on the shoulders of a person. The dog has its tongue lolling out in a relaxed, happy expression, looking slightly upward and to the right. Its fur is a vibrant, solid blue with white markings on its chest and muzzle.
```

{% hint style="info" %}
Set `api_url` to match your deployment target:

* `http://localhost:9001` for a local [Inference](https://inference.roboflow.com/) server.
* Your [Dedicated Deployment](/deploy/dedicated-deployments.md) URL for a private endpoint.
  {% endhint %}

You can train your own Qwen3.5 checkpoint on Roboflow and call it by its `workspace/project/version` identifier.

{% embed url="<https://www.youtube.com/watch?v=Y8CR6IzLDaw>" %}


---

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