- Embedding-based routers use intent classifiers — label a prompt as “support,” “SQL,” or “math,” then route to a matching model. This works for simple tasks but breaks down in real conversations. Users shift topics mid-conversation, task boundaries blur, and product changes require retraining classifiers.
- Performance-based routers pick models based on benchmarks like MMLU or MT-Bench, or based on latency or cost curves. But benchmarks often miss what matters in production: domain-specific quality or subjective preferences like “Will legal accept this clause?”
Arch-Router takes a different approach: route by preferences written in plain language. You write rules like “contract clauses → GPT-4o” or “quick travel tips → Gemini Flash.” The router maps the prompt (and conversation context) to those rules using a lightweight 1.5B autoregressive model. No retraining, no fragile if/else chains. We built this with input from teams at Twilio and Atlassian. It handles intent drift, supports multi-turn conversations, and lets you swap in or out models with a one-line change to the routing policy. Full details are in our paper (https://arxiv.org/abs/2506.16655), but here's a snapshot:
Specs:
- 1.5B params — runs on a single GPU (or CPU for testing)
- No retraining needed — point it at any mix of LLMs
- Cost and latency aware — route heavy tasks to expensive models, light tasks to faster/cheaper ones
- Outperforms larger closed models on our conversational routing benchmarks (details in the paper)
Links:
- Arch Proxy (open source): https://github.com/katanemo/archgw
- Model + code: https://huggingface.co/katanemo/Arch-Router-1.5B
- Paper: https://arxiv.org/abs/2506.16655
