Model Routing

The 'model routing' approach of using multiple models in parallel and dynamically selecting the most suitable model based on task type is a mature practice in enterprise-level AI engineering

Enterprises adopting model routing and cascading strategies can reduce overall costs by 60–80% while maintaining user experience.

模型路由层可根据任务复杂度和服务负载动态分配请求，简单任务用轻量模型，复杂任务在容量充足时调用旗舰模型

The inference cost savings from migrating to small models may be partially offset by the additional compute and latency of a more complex retrieval pipeline, so end-to-end cost, latency, and quality metrics must be measured empirically.

A single complex agent task may involve dozens of model calls; the cost of a single task using a flagship model can reach several dollars, while a Flash-tier model may compress the cost to less than one-tenth.

LLM large model nodes configure instructions through Prompt Engineering, determining the role and style in which the model outputs content.

The mainstream model routing solutions fall into three categories: rule-based routing, lightweight classifier routing, and cascading routing.

Mixing models of different costs according to task importance corresponds to the engineering practice in the industry known as LLM Routing or Model Cascading

The model routing system dynamically selects the most suitable underlying model based on task type, complexity, cost or latency requirements, which can avoid single-model-vendor lock-in but brings management issues such as consistency of model behavior, differences in output formats, and cross-model security and compliance.

The key to cost optimization has shifted from 'which model to choose' to 'how to distribute requests', and the reasonable approach is routing: hand about 90% of calls that don't need frontier intelligence to cheap models, and route the remaining 10% of requests that need frontier reasoning to high-end models.

Model routing brings new complexity, including maintaining multiple sets of prompts, handling output format differences across different models, and monitoring quality metrics for each routing branch.

A human-specified model override still has to pass the fitness check; if someone forces a small model onto a very large task, the router should refuse rather than execute.

The router's three core questions, in order, are: which models are capable of completing the task, which is the cheapest among the qualified models, and is there budget left to run it.

In model routing, fitness filtering (capability, context window, tool support) must be performed before price sorting: first exclude the models that cannot do the job, then pick the cheapest among those that can.

On the enterprise side, routing adds a compliance dimension, where data residency, audit logs, and vendor lock-in all require the orchestration layer to be decoupled from models.

The concept of model routing was not pioneered by Google; early practices appeared in multi-model API gateways (such as OpenRouter, LiteLLM), with cost and latency as the core motivations.

After 2024, as the Claude, Gemini, and GPT series gradually diverged in capability, model routing was upgraded from a money-saving trick to an architectural decision, because different models each have shortcomings in code, reasoning, long context, and tool calling.

Routing high-frequency, low-difficulty tasks to lightweight models comes at the cost of introducing extra routing judgment and context-passing overhead, and poor routing strategies may cause rework.

SLICE can make statistically guaranteed inferences about the model routing behavior of large language model APIs, and can be used to audit the behavior of many commercial APIs that route requests to different models in the background.

23% of organizations have already built model routing capabilities to flexibly dispatch across different models and control costs.