Preference-aligned routing decouples task detection (e.g., code generation, image editing, Q&A) from LLM assignment. This approach captures the preferences developers establish when testing and evaluating LLMs on their domain-specific workflows and tasks. So, rather than relying on an automatic router trained to beat abstract benchmarks like MMLU or MT-Bench, developers can dynamically route requests to the most suitable model based on internal evaluations — and easily swap out the underlying moodel for specific actions and workflows. This is powered by our 1.5B Arch-Router LLM [2]. We also published our research on this recently[3]
Modal-aliases provide semantic, version-controlled names for models. Instead of using provider-specific model names like gpt-4o-mini or claude-3-5-sonnet-20241022 in your client you can create meaningful aliases like "fast-model" or "arch.summarize.v1". This allows you to test new models, swap out the config safely without having to do code-wide search/replace every time you want to use a new model for a very specific workflow or task.
Model-literals (nothing new) lets you specify exact provider/model combinations (e.g., openai/gpt-4o, anthropic/claude-3-5-sonnet-20241022), giving you full control and transparency over which model handles each request.
P.S. we routinely get asked why we didn't build semantic/embedding models for routing use cases or use some form of clustering technique. Clustering/embedding routers miss context, negation, and short elliptical queries, etc. An autoregressive approach conditions on the full context, letting the model reason about the task and generate an explicit label that can be used to match to an agent, task or LLM. In practice, this generalizes better to unseen or low-frequency intents and stays robust as conversations drift, without brittle thresholds or post-hoc cluster tuning.
[1] https://github.com/katanemo/archgw [2] https://huggingface.co/katanemo/Arch-Router-1.5B [2] https://arxiv.org/abs/2506.16655
