Intelligent model routing claims to cut inference costs by up to 85% while retaining roughly 95% of GPT-4-level performance, by sending each query to the smallest model that can actually handle it (Zylos Research). Model routing is the practice of automatically directing an AI query to a different model, or a different size within the same model family, based on how complex the task actually is, rather than sending every query to one fixed model regardless of what it needs.
Why Model Routing Exists
Agentic workflows use five to thirty times more tokens per completed task than a single chat exchange, since a multi-step workflow might plan, call tools, and check its own output along the way. Sending every one of those steps to a frontier model gets expensive fast, and much of that work doesn’t need frontier-level reasoning to begin with. Routing treats “which model handles this” as a per-query decision instead of a fixed setting applied to everything.
Who Is Building Model Routing?
RouteLLM, a research project out of LMSYS and Berkeley, and the vLLM Semantic Router, released in January 2026, are the leading open-source routing frameworks. On the commercial side, Martian and Not Diamond specialize in automatic model selection, and OpenRouter uses Not Diamond to power its “auto” routing option. OpenRouter itself provides a unified API to more than 500 models from over 60 providers, serving more than 250,000 applications and 4.2 million users globally as of 2026 (Aiprosol’s LLM Gateway Index).
From Add-On to Core Infrastructure
Enterprises are increasingly building “mixture-of-models” and agentic architectures where routing is treated as core infrastructure rather than a bolt-on cost optimization applied after the fact (Braintrust). That shift matters because it changes how teams design AI systems from the start: instead of picking one model and building around it, they design for multiple models from day one and let routing logic decide which one handles a given request.
Commercient’s Agentic AI platform builds this same idea directly into how agents are configured, rather than treating it as a separate routing layer bolted on afterward. Different agents already working against a business’s ERP and CRM data can each point at a different model depending on the task: a routine data lookup handled by a lower-cost model, a judgment call on a sales opportunity handled by a stronger one. Switching which model an agent uses doesn’t require redefining its roles, permissions, or knowledge base, and usage is billed transparently by token so a model swap doesn’t come with a surprise bill.
See how Commercient’s Model Flexibility lets you assign the right model to each agent task. Book a free demo.
Frequently Asked Questions
What is model routing in AI? Model routing is the practice of automatically sending each query to whichever AI model, often a different size within the same family or a different provider entirely, can handle that task at the lowest cost. Simple queries route to smaller, cheaper models while complex ones route to more capable, more expensive ones.
How much can model routing save on AI costs? Research from projects like the vLLM Semantic Router suggests routing can cut inference costs by as much as 85% while retaining roughly 95% of the performance of a single, always-on frontier model, by matching query complexity to model size rather than defaulting every query to the most capable option.
What tools handle model routing today? Open-source frameworks like RouteLLM and the vLLM Semantic Router handle routing directly, while commercial services like Martian, Not Diamond, and OpenRouter, which uses Not Diamond to power its “auto” routing option, offer routing as a hosted layer across hundreds of models from dozens of providers.
Is model routing the same as just using one model for everything? No. A single-model setup applies one model’s cost and capability profile to every task regardless of complexity. Routing treats model choice as a per-query decision, so a routine lookup and a complex reasoning task can each go to a different, appropriately sized model.
How does Commercient support model routing for AI agents? Commercient’s Model Flexibility capability lets different agents run on different models depending on the task, and lets teams switch which model an agent uses without rebuilding its roles, permissions, or knowledge base, with usage billed transparently by token.
