Automatically select the best LLM model and think mode based on task type, context size, and response confidence. Cut API costs 40-70% by routing simple tasks to fast models and complex tasks to capable ones.
text
User message → Task Classifier → Model Router → Best Model → Response
↓
Low confidence? → Escalate to stronger model
yaml
# config.yaml
models:
casual_chat:
model: qwen3-14b
think: false
coding:
model: qwen-coder
think: true
reasoning:
model: deepseek-r1
think: true
long_context:
model: glm-5.1
think: false
python
from router import get_router
router = get_router()
result = await router.route("Write a Python web scraper")
# → Routes to qwen-coder with think=True
Task TypeWithout RoutingWith RoutingSavingsCasual chatGPT-4 ($0.03/1K)Qwen3-14B (local)~100%CodingGPT-4 ($0.03/1K)Qwen-Coder (local)~95%Hard reasoningGPT-4 ($0.03/1K)DeepSeek-R1 (local)~90%
MIT