By skyforbes Like many of you, I've been wrestling with the cost of using different GenAI APIs. It feels wasteful to use a powerful model like GPT-4o for a simple task that a much cheaper model like Haiku could handle perfectly.
This led me down a rabbit hole of academic research on a concept often called 'prompt routing' or 'model routing'. The core idea is to have a smart system that analyzes a prompt before sending it to an LLM, and then routes it to the most cost-effective model that can still deliver a high-quality response.
It seems like a really promising way to balance cost, latency, and quality. There's a surprising amount of recent research on this (I'll link some papers below for anyone interested).
I'd be grateful for some honest feedback from fellow developers. My main questions are:
- Is this a real problem for you? o you find yourself manually switching between models to save costs?
- oes this 'router' approach seem practical? What potential pitfalls do you see?
- If a tool like this existed, what would be most important? Low latency for the routing itself? Support for many providers? Custom rule-setting?
Genuinely curious to hear if this resonates with anyone or if I'm just over-engineering a niche problem. Thanks for your input!
Key Academic Papers on this Topic:
- Li, Y. (2025). LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned ynamic Routing. arXiv. https://arxiv.org/abs/2502.02743
- Wang, X., et al. (2025). MixLLM: ynamic Routing in Mixed Large Language Models. arXiv. https://arxiv.org/abs/2502.18482
- Ong, I., et al. (2024). RouteLLM: Learning to Route LLMs with Preference ata. arXiv. https://arxiv.org/abs/2406.18665
- Shafran, A., et al. (2025). Rerouting LLM Routers. arXiv. https://arxiv.org/html/2501.01818v1
- Varangot-Reille, C., et al. (2025). oing More with Less — Implementing Routing Strategies in Large Language Model-Based Systems: An Extended Survey. arXiv. https://arxiv.org/html/2502.00409v2
- Jitkrittum, W., et al. (2025). Universal Model Routing for Efficient LLM Inference. arXiv. https://arxiv.org/abs/2502.08773
- and others…