Conclusion
- A three-provider stack is safer than betting production on one cheap endpoint.
- Route by task type: cheap routine calls first, stronger or alternative models only after validation failure.
- Log cost per successful task, not only per-token price.
- A gateway is worthwhile when you need one key, fallback policy, and spend attribution.
What to do next
- Define task classes: chat, coding, extraction, long context, and agent tool use.
- Choose a primary route and fallback for each task class.
- Normalize prompts and output validators so providers can be compared fairly.
- Record token spend, latency, retries, invalid JSON, and accepted result rate.
- Move routing rules into config or OpenLLMAPI before launch.
Recommended paths
| Provider | Free / credits | Best for |
|---|---|---|
| DeepSeek | Credits/pricing vary | Low-cost reasoning and coding baseline |
| Qwen | Signup credits vary | Long context, Chinese, coding, Alibaba Cloud users |
| Zhipu GLM | Signup tokens vary | Domestic fallback and GLM-specific workflows |
| SiliconFlow | Free/open routes vary | China-direct multi-model testing |
| OpenLLMAPI | Trial varies | Managed routing, fallback, and budget logs |
Global developer checklist
- Confirm whether signup, billing, and API keys work from your country before writing production code.
- Prefer OpenAI-compatible endpoints when you may need to switch models, regions, or providers later.
- Test free credits with a real smoke prompt and record latency, error shape, streaming behavior, and quota burn.
- Keep at least one fallback route for provider outages, model deprecations, and regional access changes.
Production handoff
Route DeepSeek, Qwen, and GLM from one endpoint
Use one compatible key to test routes, fallback failures, and attribute LLM spend by app, user, or agent run.
FAQ
Which should be primary?
Pick the model that passes your most common task at the lowest accepted cost. Many teams test DeepSeek or Qwen first, then keep GLM as fallback.
Do I need all three?
No. Use one provider if your workload is simple. Add providers when uptime, quality variance, or regional access requires it.
How do I compare fairly?
Use the same prompts, temperature, validators, and acceptance tests, then compare accepted output cost.
Can one SDK handle all three?
Often yes through OpenAI-compatible endpoints or a gateway, but test streaming, JSON mode, and tool-call behavior.