Show HN: Arch-Router – 1.5B model for LLM routing by preferences, not benchmarks

33 adilhafeez 8 7/1/2025, 5:13:11 PM
Hi HN — we're the team behind Arch (https://github.com/katanemo/archgw), an open-source proxy for LLMs written in Rust. Today we're releasing Arch-Router (https://huggingface.co/katanemo/Arch-Router-1.5B), a 1.5B router model for preference-based routing, now integrated into the proxy. As teams integrate multiple LLMs - each with different strengths, styles, or cost/latency profiles — routing the right prompt to the right model becomes a critical part of the application design. But it's still an open problem. Most routing systems fall into two camps:

- 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

Comments (8)

jgant13 · 27m ago
Solid. Can you show us when to use this vs. say OpenRouter? The performance seems strong for sure. TIA.
sparacha · 4h ago
Hi HN! I am one of the co-authors of the paper. If there are any questions about our approach, I would love to answer them.
jedisct1 · 58m ago
I tried to use it to rate the difficulty level of coding tasks (for InferSwitch, an LLM router), but it performed far worse than Qwen2.5-Coder-7B (but sure, 1.5B vs 7B)
sparacha · 43m ago
Can you share more about your evaluation setup? I would love to see the specific usage pattern as we have tested our model against smaller LLMs and foundational models and our results show things differently. Of course, routing policies should follow best practices here: https://docs.archgw.com/guides/llm_router.html

Nonetheless, super curious to learn more and see what we may be able to improve. This is technically not a classifier model - its a usage prediction model (feels like a classifier, but not quite in terms of intended usage)

cotran2 · 32m ago
According to the post, the model is fine-tuned for routing to different tasks/domains. Classifying difficulty level is probably not the intended use case.
tmaly · 2h ago
do you think it would be possible to quantize this model and still get good results?
sparacha · 2h ago
yes - we have already published a quantized version here: https://huggingface.co/katanemo/Arch-Router-1.5B.gguf. The performance difference with a quant version is negligible. I'll run another analysis and update the thread shortly
sparacha · 32m ago
Overall performance degrades from 93.17 -> 92.99 with a quantized version