Skip to main content

​
Understanding Routing Decisions

Every request Routor handles generates a routing decision. This page explains what each field means and how to read them.

​
Where to See Decisions

Two places:
  1. Response headers on every API call - instant, inline with your request
  2. Playground - visual breakdown with color coding and confidence bar
📷 [Screenshot: Routing decision panel in the Playground showing tier, model, confidence, chain, and cost]

​
The Decision Fields

​
Model

The actual model that handled the request, shown as provider/model-id. 
moonshot/kimi-k2.6
zai/glm-5.2
deepseek/deepseek-v4-pro
This is what you see in the model field of the response body and in X-Routor-Model.

​
Tier

Which of the 5 tiers the prompt was classified into. See The 5 Tiers for the full breakdown. 
NANO | SIMPLE | LIGHT | STANDARD | COMPLEX

​
Confidence

How certain the classifier was, from 0 to 1.
ScoreMeaning
0.75 and aboveClear classification
0.55 to 0.74Reasonable match
Below 0.55Borderline, sat near a tier boundary

​
Method

How the tier was determined.
ValueMeaning
rulesRule-based classifier, standard path
llmLLM-based fallback classifier (reserved for future use)

​
Fallback Chain

The full list of models Routor built for this request, in order of preference. The first one was tried first. If it failed, the next one was used. 
1. zai/glm-5.2                  tried first
2. moonshot/kimi-k2.6           tried if #1 fails
3. google/gemini-3.5-flash      tried if #2 fails

​
Cost Estimate vs Baseline

FieldWhat it means
Cost estimateWhat this request actually costs on the selected model
Baseline costWhat it would have cost on Claude Opus 4.8
SavingsThe difference, shown as a percentage

​
Why a Decision Might Surprise You

If a prompt is routed to a higher tier than expected, these are the most common reasons:
  • Code keywords detected - even mentioning function names or syntax can bump the tier
  • Multi-step patterns - numbered lists or “first… then…” patterns score higher
  • Long context - a large system prompt or long conversation history pushes toward COMPLEX
Use the Debug Endpoint to see exactly which dimensions fired and what score they contributed. That tells you precisely why the tier landed where it did.