@heyshrutimishra: Most LLM routers are static rules; OrcaRouter is a router that learns. It embeds every prompt, scores it against past p…
Summary
OrcaRouter is a learning-based LLM router that dynamically routes prompts to appropriate models based on quality, cost, speed, and reliability, improving over time with production traffic.
Similar Articles
@amitiitbhu: New article: LLM Routing Read here: https://outcomeschool.com/blog/llm-routing…
A tutorial blog post explaining LLM Routing — the practice of directing user queries to the most appropriate LLM based on cost, latency, and quality. Covers routing strategies, anatomy of an LLM router, and comparisons with Mixture of Experts.
I built LEMoE: A stateless, lightweight Mixture of Experts (MoE) router for local LLMs. Open-source and looking for feedback!
LEMoE is an open-source, stateless Mixture of Experts (MoE) router that acts as an API proxy to route prompts to specialized LLMs, featuring cascading contextual routing and silent self-correction.
The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers
This paper identifies a 'routing plateau' phenomenon where diverse LLM routing methods converge to similar accuracy, far below the oracle, due to a predictability bottleneck that limits query-specific routing. It then shows that larger datasets, stronger encoders, and fine-tuning can help break through this plateau.
Routing agent work across 4 LLM tiers: orchestrator, advisor, deep reasoning, premier
The author shares a practical 4-tier LLM routing stack for agent work, where a fast orchestrator handles most requests and only escalates to expensive models when deep reasoning is required, significantly improving cost and interactivity.
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Researchers from National Taiwan University propose replacing fixed translation-based prompting strategies in multilingual LLMs with lightweight learned classifiers that route each instance to either native or translation-based prompting. Their analysis across 10 languages and 4 benchmarks shows no single strategy is universally optimal, with translation benefiting low-resource languages most, and the learned routing achieving statistically significant improvements over fixed strategies.