Routing models for cost
- Teams are routing simple requests to cheap models and complex requests to premium models to lower inference costs. - Practitioners report routing can yield roughly 2–5x savings by matching task complexity to model cost. - This economic pattern is appearing in vendor and builder conversations about Flash/Haiku lightweight options versus premium models (youtube.com).
A growing number of AI teams are sending easy prompts to cheap models and hard prompts to premium ones, turning model choice into a cost-control system. (aws.amazon.com) The basic idea is simple: a router reads the request first, then decides whether a lightweight model can handle it or whether a stronger model is needed. Amazon Web Services said in an April 9, 2025 post that organizations are increasingly using multiple large language models to optimize for cost, latency, and quality. (aws.amazon.com) Researchers at LMSYS put numbers on the tradeoff in July 2024 with RouteLLM, an open-source routing framework. They reported cost reductions of more than 85% on MT Bench, 45% on MMLU, and 35% on GSM8K while reaching 95% of GPT-4 performance, depending on the benchmark and router setup. (lmsys.org) The pricing gap that makes routing worthwhile is now visible across major vendors’ lineups. OpenAI lists GPT-5.4 at $2.50 per 1 million input tokens and $15 per 1 million output tokens, versus $0.75 and $4.50 for GPT-5.4 mini and $0.20 and $1.25 for GPT-5.4 nano. (openai.com) Anthropic shows the same ladder. Claude Haiku 4.5 starts at $1 per 1 million input tokens and $5 per 1 million output tokens, Claude Sonnet 4.6 starts at $3 and $15, and Claude Opus 4.7 starts at $5 and $25. (anthropic.com, anthropic.com, anthropic.com) Google has framed its Flash line the same way. In February 2025, Google said Gemini 2.0 Flash-Lite was its “most cost-efficient” model yet, while Gemini 2.0 Pro was aimed at coding and complex prompts; by March 6, 2026, Google’s Vertex AI docs were steering new customers from Gemini 2.0 Flash to newer 2.5 Flash models, but the Flash-versus-Pro split remained the product pattern. (developers.googleblog.com, docs.cloud.google.com) That pricing spread has changed how builders talk about deployment. Instead of asking for one “best” model, teams now ask which requests need top-tier reasoning and which can be answered by a faster, cheaper model without hurting user-visible quality. (aws.amazon.com, lmsys.org) Vendors are also pitching lightweight models for scaled workloads, not just as stripped-down demos. Anthropic says Haiku 4.5 is meant for “scaled deployments,” free-tier products, and latency-sensitive customer service agents, while OpenAI’s API page groups mini and nano variants alongside its flagship model rather than as separate products. (anthropic.com, openai.com) The router itself can be simple or elaborate. Amazon Web Services describes task-based routing and complexity-based routing, while LMSYS trains routers on preference data so they can decide when a weaker model is likely good enough before the expensive call is made. (aws.amazon.com, lmsys.org) Routing does not remove the tradeoff; it just makes it explicit. Every saved dollar depends on a classifier being good enough to spot the prompts that can safely stay on the cheap lane. (lmsys.org, aws.amazon.com) As model catalogs keep widening, the default architecture is shifting from one model per app to a stack of models with a traffic cop in front. The economics now reward teams that can tell the difference between “summarize this email” and “solve this multi-step problem” before inference starts. (openai.com, anthropic.com, aws.amazon.com)
