{"id":131,"date":"2026-08-25T08:25:03","date_gmt":"2026-08-25T08:25:03","guid":{"rendered":"https:\/\/pimpbunny.co.uk\/news\/?p=131"},"modified":"2026-09-08T20:38:57","modified_gmt":"2026-09-08T20:38:57","slug":"llm-routing-guide-from-one-model-to-the-right-model","status":"publish","type":"post","link":"https:\/\/pimpbunny.co.uk\/news\/technology\/llm-routing-guide-from-one-model-to-the-right-model\/","title":{"rendered":"LLM Routing Guide: From “One Model” to “The Right Model”"},"content":{"rendered":"

An <\/span>llm router<\/span> layer sits between your application and the model market \u2014 it reads each request and decides which model answers it. When you weigh which models to move between, a mid-tier like <\/span>GPT-5.6 Terra<\/span> shows how the switching cost varies; this guide is about the adoption decisions that come before the wiring: which signals to route on, what changes once you turn routing on, and the failover chains, caching, and observability that keep it honest.<\/span><\/p>\n

Most teams don’t have a model problem \u2014 they have a single-model reflex. One flagship, one contract, one endpoint, and every request, from “summarize this email” to “refactor this codebase,” pays flagship price and flagship latency. LLM routing breaks that reflex. It treats the model catalog as a portfolio and matches each prompt to the cheapest model that can still answer it acceptably \u2014 but the savings arrive only if you get the signals, the operations, and the failure modes right.<\/span><\/p>\n

The five signals an LLM router reads<\/b><\/h2>\n

Routing is a classification problem in disguise: given this request, who answers? Every router encodes the same five signals, though implementations weight them differently.<\/span><\/p>\n

    \n
  1. Prompt difficulty.<\/b> This is the core of routing by difficulty: is the task trivial or hard? A router scores each prompt before sending it anywhere. OrcaRouter grades every prompt in under a millisecond before routing it [OURS], so the classification adds essentially no latency to the request.<\/span><\/li>\n
  2. Cost budget.<\/b> Per-token price. Models vary by orders of magnitude in price per million tokens for the same quality bar. A cost budget says: within these quality bounds, minimize price.<\/span><\/li>\n
  3. Latency budget.<\/b> How long may the answer take? Chat and voice demand a low time-to-first-token; batch and background jobs can wait seconds. A latency budget sends interactive traffic to fast models and lets slow-and-thorough models handle offline work.<\/span><\/li>\n
  4. Provider health.<\/b> Models live on providers that rate-limit, degrade, or go down. Health signals \u2014 timeout rates, error codes, queue depth \u2014 are the trigger for routing around a failing provider. The router should notice before your users do.<\/span><\/li>\n
  5. Conversation context and session state.<\/b> The newest and most underused signal. Modern routers are context-aware: they read the session, not just the single prompt \u2014 how many turns have passed, what the conversation has accumulated, how much of the prompt is a shared prefix. That turns routing from per-request to per-conversation: escalate to a stronger model when an agent goes deep, keep cheap models in charge of trivial exchanges, and route long shared prefixes straight into a cache. Session-aware, prefix-aware routing is the difference between routing that saves money on isolated prompts and routing that behaves intelligently inside a real product.<\/span><\/li>\n<\/ol>\n

    \"LLM<\/h2>\n

    Before and after: one flagship, then routing by difficulty<\/b><\/h2>\n

    The fastest way to see whether routing is worth it is to run the exercise on your own traffic: take your requests, split them into easy and hard, and price both ways. Here’s a typical assistant workload, priced before and after.<\/span><\/p>\n\n\n\n\n\n\n\n
    Request type<\/b><\/td>\nBefore: one flagship<\/b><\/td>\nAfter: routing by difficulty<\/b><\/td>\n<\/tr>\n
    Email drafts and short replies<\/span><\/td>\nflagship output price<\/span><\/td>\ncompact model<\/span><\/td>\n<\/tr>\n
    Ticket classification<\/span><\/td>\nflagship<\/span><\/td>\nsmall classifier-grade model<\/span><\/td>\n<\/tr>\n
    Ambiguous support question<\/span><\/td>\nflagship<\/span><\/td>\nmid-tier model<\/span><\/td>\n<\/tr>\n
    Multi-file code refactor<\/span><\/td>\nflagship<\/span><\/td>\nflagship \u2014 the router escalates<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

     <\/p>\n

    Before, every row is billed at flagship output rates. After, only the genuinely hard rows are. The flagship still does the hard work \u2014 the router just stops sending it the easy work. That’s the entire trick, and it’s why the grading step is the heart of a router: get difficulty wrong in one direction and quality collapses; get it wrong in the other and you’re back to paying flagship prices for everything.<\/span><\/p>\n

    The operational side: failover, caching, observability<\/b><\/h2>\n

    Routing multiplies the models you depend on, which multiplies your failure surface \u2014 unless you build the operational layer at the same time. Three pieces, none optional:<\/span><\/p>\n