Guide

What is LLM routing?

LLM routing means sending each AI request to the model that best fits it, behind one stable interface. Instead of wiring every feature to a single model, an AI model router lets your product ask for what it needs while the model choice can change underneath.

Routing in one sentence

A router gives your application one endpoint and one key, then decides where each request actually runs. The decision can be explicit — you name a model in the call — or automatic, based on the task, the prompt length, a latency target, cost, or which providers are healthy right now. The point is separation: your code says what it wants, the routing layer handles where it runs.

Why use more than one model

No single model wins at everything. A large reasoning model earns its cost on a hard coding or analysis task; a smaller, faster one is the better pick for classification or a short reply. Prices, limits, and availability also shift often. Without a routing layer, those differences leak into your code — provider-specific SDKs, response shapes to normalise, credentials to rotate, integrations to rewrite every time your preferred model changes. A router collapses that into one consistent surface.

• Quality — stronger reasoning where a mistake is expensive.
• Latency — fast models for interactive chat and autocomplete.
• Cost — reserve the pricey models for requests that benefit.
• Reliability — move traffic when a model or provider is down.
• Flexibility — try new models without rebuilding your integration.

How an AI model router works

Your app sends a request to a shared endpoint. The router authenticates it, validates it, applies any account or policy limits, and picks a route. It forwards the request to the chosen provider and streams back a normalised response. A basic router uses the model id you pass. A more capable one adds ordered fallbacks, workload rules, or a lightweight classifier that estimates which model is enough for a given prompt. The best routers are observable — you can see which model handled a request, and why.

Routing vs. load balancing

Load balancing spreads equivalent traffic across interchangeable servers. Routing makes a semantic choice between models that differ in capability, price, speed, and context window. They compose: a router can pick a model first, then balance across the providers that serve it. The distinction matters because a response that arrives isn't automatically a good response — production routing should weigh task fit, not just whether an endpoint answered.

When routing is worth adding

Routing earns its place when you use more than one model family, want a fallback strategy, serve workloads with different quality or latency needs, or want to switch providers without changing your product's public interface. For a one-model prototype, a direct integration is perfectly fine. Add the layer when it removes repeated integration work or improves a constraint you can measure. Start with explicit model selection and clear logs; automate the decision only once you have enough traffic and evaluation data to trust it.

What to evaluate in a router

Check API compatibility, streaming behaviour, model coverage, pricing clarity, error formats, data handling, observability, and fallback controls. Test the exact features you depend on — tool calls, structured output, long context, images, and cancellation mid-stream. The practical next step is to run one small evaluation set through several models, record quality and latency, and choose routes from evidence in your own workload.

How vexer approaches it

vexer is a single API in front of Claude, Grok and GPT — compatible with both the OpenAI and Anthropic request formats, so it drops into tools you already use. You can name a model, or send "model": "auto" and let vexer pick: hard, long, or code-heavy prompts route to the deepest model, and everything else stays on a fast, balanced default. Every model also carries a fallback — if a provider is unavailable, the request reroutes before any token streams, so a blip upstream doesn't surface as your error. Responses report the model that actually ran, and billing is per token with no subscription. Browse the model catalog to compare context, strengths and price.