How Difficulty-Based LLM Routing Can Cut $100k Monthly Inference Bills by Up to 60%

Companies spending $100,000 a month on large language model inference can significantly reduce costs by routing requests to cheaper models based on task difficulty, according to a cost analysis published by LLM Gateway. The model assumes a representative request of 2,000 input tokens and 500 output tokens, with per-request costs ranging from $0.0045 for Claude Haiku to $0.0225 for Claude Opus. Under an aggressive routing scenario where 60% of traffic shifts to the cheapest model, projected net savings reach around $59,500 per month, while even a conservative mix yields roughly 36% savings. A classifier that determines request difficulty costs approximately $0.0001 per call, making it cost-effective as long as at least 1.2% of routed traffic can be downgraded to a cheaper model. The analysis cautions that quality is not guaranteed when pushing requests to lower-tier models, and that real-world traffic mixes will vary, so organizations are advised to measure their own request difficulty distribution rather than rely on illustrative figures.