Multi-Model Routing: Sending the Right Prompt to the Right Model

[chawkian]

Not all prompts are created equal — and neither are LLMs. At AIWave, we've been experimenting with intelligent model routing and it's been a game-changer.

The idea is simple: classify the user's intent, then route to the best model for that task.

User message → Intent classifier → Route:
  - Coding → DeepSeek-v4
  - Creative writing → Claude 4
  - Translation → GPT-5
  - Simple Q&A → Cheapest available model

Results so far:

• 45% cost reduction by routing simple queries to smaller models
• Better quality on specialized tasks (no one model wins everywhere)
• Faster responses for the 80% of queries that don't need a frontier model

Our router is dead simple — it's just another cheap LLM call (<$0.001) that outputs a category label. The overhead is negligible.

We've open-sourced the routing logic in the AIWave Python SDK if you want to try it.

How are you handling model selection? Static, rules-based, or something more sophisticated? Let's compare notes. 🔀