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.
New to AI Endpoints? This guide walks you through everything you need to get an access token, call AI models, and integrate AI APIs into your apps with ease.
Start tutorialExplore what AI Endpoints can do. This guide breaks down current features, future roadmap items, and the platform's core capabilities so you know exactly what to expect.
Start tutorialRunning into issues? This guide helps you solve common problems on AI Endpoints, from error codes to unexpected responses. Get quick answers, clear fixes, and helpful tips to keep your projects running smoothly.
Start tutorialLearn how to use Structured Output with OVHcloud AI Endpoints.
Start tutorialLearn how to use Function Calling with OVHcloud AI Endpoints.
Start tutorialLearn how to use OVHcloud AI Endpoints Virtual Models.
Start tutorial