Multi-Model Routing: Smart Model Selection with ChinaWHAPI
Different user questions should route to different models. This article covers how to automatically select the optimal model based on task type, balancing quality and cost.
Why Model Routing Is Needed
Different models excel at different tasks and have varying prices. Smart routing can use cheap models for simple questions and powerful models for complex ones, controlling costs while maintaining quality.
Routing Strategies
Rule-based routing is the simplest: determine task type from question keywords, then route to the corresponding model.
def route_model(question: str) -> str:
if any(k in question for k in ["prove", "reasoning", "derive", "analyze"]):
return "deepseek-r1"
if any(k in question for k in ["code", "function", "bug", "fix"]):
return "qwen3-coder-plus"
if len(question) > 2000:
return "kimi-k2.6"
return "qwen3.5-flash" # cheap and fastCost Savings Example
Assuming 10,000 calls/day, 80% using low-cost models ($0.1/1K calls) and 20% using premium models ($2/1K calls), this saves 90%+ compared to using GPT-4 for everything.
Implementation Notes
Routing itself has latency — consider caching; route multi-turn conversations to the same model; regularly evaluate routing effectiveness and adjust rules.