Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support. With this model, reasoning can be disabled by using \"/no_think\" in your prompts.
First install the requests library:
pip install requests
Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>
If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.
Finally, run the following python code:
import os
import requests
# You can use the model dedicated URL
url = "https://qwen-3-32b.endpoints.kepler.ai.cloud.ovh.net/api/openai_compat/v1/chat/completions"
# Or our unified endpoint for easy model switching with optimal OpenAI compatibility
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
"max_tokens": 512,
"messages": [
{
"content": "Explain gravity to a 6 years old",
"role": "user"
}
],
"model": "Qwen3-32B",
"temperature": 0,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
# Handle response
response_data = response.json()
# Parse JSON response
choices = response_data["choices"]
for choice in choices:
text = choice["message"]["content"]
# Process text and finish_reason
print(text)
else:
print("Error:", response.status_code, response.text)
The Qwen3-32B API is compatible with the openai specification.
First install the openai library:
pip install openai
Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>
Finally, run the following python code:
import os
from openai import OpenAI
# You can use the model dedicated URL
url = "https://qwen-3-32b.endpoints.kepler.ai.cloud.ovh.net/api/openai_compat/v1"
# Or our unified endpoint for easy model switching with optimal OpenAI compatibility
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1
client = OpenAI(
base_url=url,
api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)
def chat_completion(new_message: str) -> str:
history_openai_format = [{"role": "user", "content": new_message}]
return client.chat.completions.create(
model="Qwen3-32B",
messages=history_openai_format,
temperature=0,
max_tokens=1024
).choices.pop().message.content
if __name__ == '__main__':
print(chat_completion("Explain gravity for a 6 years old"))
When using AI Endpoints, the following rate limits apply:
If you exceed this limit, a 429 error code will be returned.
If you require higher usage, please get in touch with us to discuss increasing your rate limits.
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