Key takeaways
Most China LLM vendors expose OpenAI-compatible chat endpoints. You keep one SDK and swap `base_url` + `api_key` + `model`. Compatibility is not 100%—test JSON mode, tool_choice, streaming tool deltas, and error shapes before production. Swift Horse indexes public labels; vendor docs win on edge cases.
Minimal client pattern
Store per-vendor config: base_url, key, default model → initialize OpenAI client → chat.completions.create → log request_id / latency / tokens. Keep vendor quickstarts handy: DeepSeek, Qwen, GLM, Kimi articles on this site under /en/articles/.
What to test before launch
Streaming on/off → tools/function calling → empty/malformed tool args → rate-limit retries → bilingual prompts → cost at real volume (/en/articles/china-llm-api-pricing-2026). Agent stacks: /en/articles/china-llm-agent-tool-calling-2026. Latency failover: /en/articles/china-llm-latency-failover-2026.
Next steps on Swift Horse
Access overview /en/articles/access-china-llm-api-overseas → vendor quickstarts → vs ChatGPT /en/articles/china-llm-vs-chatgpt-2026 → catalog /en/models.
FAQ
Can I use the official OpenAI Python SDK with Chinese LLMs?
Yes for many vendors by setting base_url and api_key. Validate tools and streaming on each model ID.
Does OpenAI-compatible mean identical behavior?
No. Expect differences in tool schemas, JSON strictness, and error payloads.
How do I switch vendors without rewriting the app?
Abstract a provider config map and route by env or feature flag; keep the same chat interface.
Where are vendor-specific quickstarts?
DeepSeek, Qwen, GLM, and Kimi quickstarts live under /en/articles/ on Swift Horse.