Cascade Analysis — Understanding Token Flow
Every AI coding session moves tokens through a cascade. Learn to read the flow — and tell compounding signal from burning tokens.
What a token cascade is
A token cascade is the flow of tokens through an AI coding session. Every turn moves tokens through four stages: you send fresh input to the model, the model generates output back, cache-read tokens are reused from prior context via prompt caching, and cache-write tokens are written to cache for future reuse. The cascade is the full picture of how tokens enter, circulate, and leave the session.
The metaphor is deliberate: tokens cascade like water through a series of pools. Some pools compound — cached context flows back into the next turn, amplifying a small fresh input into large output. Other pools drain — fresh input pours in, nothing is reused, and thin output trickles out. Cascade analysis is the art of reading which kind of cascade you have.
How to read your cascade
Read the four pillars together, never in isolation. A high input number is bad if cache-read is low (you are spending without compounding) but fine if cache-read is also high (you are sending fresh input on top of a large cached foundation). A high output number is good only if it is dense — SNR tells you that. The yield metric Υ = cache_read × output / input² is the synthesis: it rewards high cache reuse and high output while punishing high fresh input. If your yield is low, the four pillars tell you why.
What each pillar reveals
Input → Spend
How many fresh tokens you are sending. The cost side. High input without cache reuse means you are paying full price every turn.
Output ← Return
How many tokens the model generates back. The return side. High output is good — but only if it is dense (check SNR).
Cache-read ↻ Compounding
How much prior context you are reusing for free. The compounding layer. High cache-read is the hallmark of a healthy cascade.
Cache-write ✎ Investment
How much context you are storing for future turns. An investment. It only pays off if you read it back — check cache-read.
Diagnostic patterns
The four pillars combine into recognizable shapes. Here are the four most common — and what each one tells you.
The Compounder
High cache-read, low input, high outputSignal is compounding. You reuse cached context well and send minimal fresh input. This is the TRANSMITTER pattern — the cascade architecture yield rewards.
The Burner
High input, low cache-read, low outputTokens are burning. You send large fresh prompts without reusing context and get thin output back. Yield is low. Cut input, build cache, reuse context.
The Echo
High input, high output, low cache-readThe model is parroting your input back. Compression ratio may look fine, but without cache reuse the cascade is not compounding. Yield stays flat because input² punishes the spend.
The Hoarder
High cache-write, low cache-read, low outputYou are writing context to cache but never reading it back. The investment is not paying off. Either the cached context is not reusable or you are not structuring turns to recall it.
Explore the category
How to Read Your Cascade
A step-by-step guide to interpreting your four token pillars, spotting diagnostic patterns, and turning the numbers into action.
Yield (Υ) Cascade
The headline metric that summarizes cascade architecture: cache_read × output / input².
Cache Hit Rate
How well you reuse cached context — the difference between compounding and hoarding.
Leverage
How much cached context amplifies your fresh input — the compounding multiplier.
Compression Ratio
Output over input — whether the model is doing more with your input than echoing it.
Signal-to-Noise Ratio (SNR)
The density of useful output in your cascade — signal tokens over total tokens.
Velocity
Tokens produced per unit time — the throughput lens on the cascade.
Cascade Comparator
Compare two operators' token cascades side by side — see where the yield gap comes from.
FAQ
- What is a token cascade?
- The flow of tokens through an AI coding session across four stages: input, output, cache-read, and cache-write. The cascade is the full picture of how tokens enter, circulate, and leave a session.
- What is cascade analysis?
- The study of token flow through sessions. By reading the four pillars you diagnose whether signal is compounding or tokens are burning. Yield (Υ) summarizes the architecture in one number.
- How do I read my cascade?
- Read the four pillars together. High cache-read + low input + high output = compounding. High input + low cache-read + low output = burning. The diagnostic patterns on this page map the common shapes.
- What does each pillar reveal?
- Input reveals spend, output reveals return, cache-read reveals compounding, and cache-write reveals investment. Together they describe the full cascade architecture.
- Compounding vs. burning cascade?
- A compounding cascade has high cache-read, low input, high output. A burning cascade has high input, low cache-read, low output. Yield is high for the first, low for the second. The difference is architecture, not model.