Qwen3-VL, the most powerful vision language model in the Qwen series is now available on Ollama’s cloud. The models will be made available locally soon.
Download Ollama
Run the model
ollama run qwen3-vl:235b-cloud
Prompt the model with a message and image path(s). It is possible to use multiple images and drag and drop in images to make it easier to automatically type the file path.


Prompt: What is this flower? Is it poisonous to cats?


Prompt: Show me the menu in English!


Prompt: what’s the answer?

You can use Ollama’s cloud for free to get started with the full model using Ollama’s CLI, API, and JavaScript / Python libraries.
Install Ollama’s JavaScript library
npm i ollama
Pull the model
ollama pull qwen3-vl:235b-cloud
Example non-streaming output with image
import ollama from 'ollama'
const response = await ollama.chat({
model: 'qwen3-vl:235b-cloud',
messages: [{
role: 'user',
content: 'What is this?',
images: ['./image.jpg']
}],
})
console.log(response.message.content)
Example streaming the output with image
import ollama from 'ollama'
const message = {
role: 'user',
content: 'What is this?',
images: ['./image.jpg']
}
const response = await ollama.chat({
model: 'qwen3-vl:235b-cloud',
messages: [message],
stream: true,
})
for await (const part of response) {
process.stdout.write(part.message.content)
}
Ollama’s JavaScript library page on GitHub has more examples and API documentation.
Install Ollama’s Python library
pip install ollama
Pull the model
ollama pull qwen3-vl:235b-cloud
Example non-streaming output with image
from ollama import chat
from ollama import ChatResponse
response: ChatResponse = chat(
model='qwen3-vl:235b-cloud',
messages=[
{
'role': 'user',
'content': 'What is this?',
'images': ['./image.jpg']
},
])
print(response['message']['content'])
# or access fields directly from the response object
print(response.message.content)
Example streaming the output with image
from ollama import chat
stream = chat(
model='qwen3-vl:235b-cloud',
messages=[{
'role': 'user',
'content': 'What is this?',
'images': ['./image.jpg']
}],
stream=True,
)
for chunk in stream:
print(chunk['message']['content'], end='', flush=True)
Ollama’s Python library page on GitHub has more examples and API documentation.
The model can also be accessed directly on ollama.com’s API.
Generate an API key from Ollama.
Set OLLAMA_API_KEY environment variable using your API key.
export OLLAMA_API_KEY=your_api_key
Generate a response using API examples.
Ollama has OpenAI compatible API endpoints that support the chat completions endpoint, completions endpoint, and the embeddings endpoint.
Generate an API key from Ollama.
The base_url should be set to https://ollama.com/v1 and api_key set to the one generated from above.