Beyond Single Models: Automating LLM Selection Through Intent Classification

Single LLM architectures force unnecessary friction on users and miss inherent capability tradeoffs—no single model excels at coding, reasoning, and search equally. By using intent classification to automatically route queries to the best-suited model (Llama for coding, Qwen for reasoning, Gemini for search), this approach eliminates manual model selection while optimizing for both performance and cost. The key innovation is zero-latency rule-based classification instead of LLM inference, proving that intelligent routing doesn't require expensive compute.