How to Reduce LLM API Costs by 82% with Smart Routing

Smart Routing significantly reduces LLM API costs by matching model capability to task complexity, resulting in an 82% cost reduction compared to using GPT-5.1 for every request, with only a minor quality decrease of 0.08 points. This strategy involves routing simpler tasks to less expensive models while reserving premium models for more complex requests, effectively optimizing resource use and reducing unnecessary expenses. By implementing methods like prompt caching, provider fallbacks, and batch APIs, further savings can be achieved by minimizing repeated input costs, avoiding retries, and optimizing asynchronous processing. The benchmark demonstrated that Smart Routing is particularly cost-effective for mixed workloads, achieving substantial savings without a significant drop in quality, whereas using a single premium model for all requests leads to inefficient spending.

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