AI Model Routers: How Requests Reach the Right Model

An AI model router is software that decides which model should handle a given request rather than sending every request to the same model. A simple question might go to a fast, cheap model; a complex reasoning task might go to a more powerful one. Routing can reduce cost and improve response time across a high volume of requests. Tools like fenbs solve a different problem: keeping the shared project context available to whichever model the router selects, so the work record does not live inside a single model.

Understanding what a router does and where it stops helps you decide whether routing belongs in your setup and what else you need alongside it.

What an AI model router does

A router sits between the application sending requests and the models that handle them. When a request arrives, the router classifies it — by complexity, task type, cost target, or expected latency — and forwards it to a specific model. The calling application does not need to know which model ran; it just receives the response.

Routers are most useful in products that process a large number of diverse requests: a customer-facing AI assistant that handles simple questions and complex analysis in the same session, for example. They can also provide fallback: if a primary model is unavailable or too slow, the router sends the request to an alternative.

Common routing strategies

Cost-based routing sends routine or low-complexity requests to smaller, cheaper models and reserves larger models for requests that need more capability. A classification step estimates complexity before routing; the accuracy of that classification determines how well the strategy works.

Capability-based routing directs requests by task type rather than complexity: code generation to a coding-optimized model, document summarization to a model with a large context window, image analysis to a multimodal model. This requires the router to identify the task type reliably.

Latency-based routing prioritizes speed: real-time applications that cannot tolerate a slow response send requests to whichever capable model is fastest at that moment. This often means accepting some capability trade-off.

What routing does not solve

Routing decides which model handles a request. It does not decide what context that model receives. If a project has accumulated decisions, rules, and progress notes that the model needs to produce a useful response, those must be provided explicitly — through the system prompt, through a tool call, or through an outside record the model reads at the start of a session.

A router that sends a request to a different model than the one used in the previous session does not automatically carry over what the previous session established. Each model starts with whatever context the application explicitly provides. Durable work context — the kind that should survive sessions and model changes — needs to live outside the router.

For a broader comparison of how models differ, see how to choose an AI model. For how model apps compare on specific task types, see AI model comparison by task.

When to consider a model router

Routing is most relevant if you are building a product that sends a large volume of diverse requests programmatically, and you want to manage cost or latency at scale. It is less relevant if you work interactively in a single AI app and switch models manually based on the task at hand.

If you are a developer building on AI APIs, model routing libraries and providers offer routing as a service. Check your API provider's documentation for current routing options, since the offerings in this space change frequently.

What a router still needs from you

A well-configured router handles model selection automatically. What it does not handle is the content of the requests and the context those requests need. If your application sends requests without a system prompt, the routed model starts without any project context. If each request is independent, that may be fine. If requests build on each other across a session or across sessions, context management is a separate problem from routing.

Think of routing as handling the question "which model?" and context management as handling the question "what does this model need to know?" Both matter for a useful AI application, and solving one does not solve the other.

How to do this with fenbs

1. Create a free account on fenbs. Add your project with its goals, current tasks, and the key decisions that any model working on it should know.
2. Write the standing rules in Decisions and rules. These appear first when a connected AI app reads the project context, regardless of which model is behind it.
3. Connect the AI apps you use from /integrations. Each one reads the same fenbs project context at the start of a session.
4. At the end of each session, ask the assistant to save Where we left off so the next session — whether on the same model or a different one — starts from the current state.
5. If you use a router in a product you are building, pass the fenbs project context as part of the system prompt or as an MCP tool call so each routed request has the shared work record.

When fenbs is not the right fit

fenbs is for keeping a shared work record, not for building routing infrastructure. If your goal is to implement a low-latency routing layer for a production API, that is an application engineering problem. fenbs connects to supported AI apps at the session level and does not route requests internally. For routing infrastructure, check the API documentation for the model providers you are using.