Many applications handle requests of wildly different difficulty: some are simple format conversions, others need multi-step reasoning. Sending everything to the strongest model may give the best answers but costs the most; sending everything to a small model saves money but often answers poorly. LLM routing is the "traffic control" layer that balances the two.
1. Why routing
Models differ a lot in capability and price. A simple classification task can be handled reliably by a small model, while complex code refactoring or long-chain reasoning often needs a large model. If every request goes to the same model, you either overpay for easy tasks or under-serve hard ones. The core idea of routing is to pick a suitable model dynamically based on the request difficulty and type.
2. How routing decides
There are several common approaches. First, rule-based: split traffic by keywords, length, or whether the text contains code, which is simple but inflexible. Second, classifier-based: train a lightweight model to predict which model a request should go to, which is very fast. Third, similarity-based: compare the request with historical samples and follow the nearest neighbors decision. Fourth, draft-based: let a cheap model produce a first attempt, then decide whether to escalate to a stronger model.
3. Typical form
In practice, routing usually sits as a middle layer between the application and the model APIs. It receives a request, judges difficulty, selects a model, and returns the result. Some implementations use a cascade: let a small model try first, then use confidence scores or validation rules to judge whether the answer is reliable, and hand the work to a large model if it is not.
4. Benefits and costs
The benefits are direct: with overall quality kept acceptable, steering many easy requests to cheaper models can significantly lower average cost and latency. The cost is an extra layer of judgment. Routing itself can be wrong, and misrouting a hard question to a weak model hurts answer quality. So routing policies should be continuously evaluated on real traffic rather than set by guesswork.
5. Practice points
First, define the quality floor before talking about saving money, otherwise it is easy to trade experience for cost. Second, keep a fallback path so requests sent to weak models can escalate when confidence is low. Third, log routing decisions so you can later analyze which traffic was misrouted. Fourth, refresh rules and classifiers with new data regularly, because both model capabilities and prices keep changing.
Summary
LLM routing turns the coarse one-model-for-every-request practice into on-demand allocation. It does not create intelligence or improve a single model ability, but it decides how much compute and money a request spends. For cost-sensitive applications at scale, this traffic control layer often yields more real savings than switching models.