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arXiv · 2508.12491

Cost-Aware Contrastive Routing for LLMs

Reza Shirkavand·Shangqian Gao·Peiran Yu·Heng Huang
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Abstract

We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive model profiling, assume a fixed set of experts, or use inefficient trial-and-error strategies. We introduce Cost-Spectrum Contrastive Routing (CSCR), a lightweight framework that maps both prompts and models into a shared embedding space to enable fast, cost-sensitive selection. CSCR uses compact, fast-to-compute logit footprints for open-source models and perplexity fingerprints for black-box APIs. A contrastive encoder is trained to favor the cheapest accurate expert within adaptive cost bands. At inference time, routing reduces to a single k-NN lookup via a FAISS index, requiring no retraining when the expert pool changes and enabling microsecond latency. Across multiple benchmarks, CSCR consistently outperforms baselines, improving the accuracy-cost tradeoff by up to 25%, while generalizing robustly to unseen LLMs and out-of-distribution prompts.

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Reza Shirkavand, Shangqian Gao, Peiran Yu, Heng Huang. 2025-11-24. Cost-Aware Contrastive Routing for LLMs. https://arxiv.org/abs/2508.12491

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