What normally changes
- Base URL
- API key
- Public model name
What still needs testing
- Tool-call arguments and validation
- Streaming event behavior
- Token usage fields
- Model-specific context and output limits
When this is a good fit
Use this path when an existing service already depends on the OpenAI client interface and the team wants to evaluate supported Qwen models with minimal integration churn.
When it is not enough
Compatibility is not identical model behavior. Applications that rely on exact response formatting, provider-specific endpoints or undocumented edge cases need a proper regression test suite.
from openai import OpenAI
client = OpenAI(
api_key="qh_live_...",
base_url="http://localhost:4000/v1",
)
response = client.chat.completions.create(
model="qwen3.7-plus",
messages=[{"role": "user", "content": "Explain this error log."}],
)
print(response.choices[0].message.content)Common questions
Is the API compatible with the OpenAI SDK?
The public gateway follows the OpenAI-compatible chat completions pattern. Confirm model-specific features in the live catalog and documentation before production rollout.
Where do model availability and prices come from?
Public model pages read the active catalog exposed by the service. A model is not presented as available merely because its name appears in an article.
Are Alipay and USDT recharge active?
They are planned for the international version, but the collection module is not enabled in this phase. The site must not ask users to transfer funds until a real order and crediting workflow is available.
Can I limit spending by project or API key?
The existing product supports project and key controls, including budget and rate constraints. Exact fields and enforcement behavior are documented in the console and API documentation.