TokenPad
Reference

Context Rot Visualizer

A fuller window is not a better one.

Settings
Context RotExact
13%Signal ratio
Signal ratio13%of what you send is relevant
Window filled75%50.000 tokens spare
Padding per year$234.000spent sending tokens the model does not need
Token cost of this result
Output tokens0
As input$0.00
× 100K requests$0.00

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Result
 

The effect is real and documented

Recall and instruction-following degrade as a context window fills. Anthropic’s own documentation makes the point directly: curating what goes into context matters as much as how much space is available.

The consequence is that a tightly retrieved 20,000 token prompt usually beats a padded 500,000 token one on accuracy, and always beats it on cost.

Position matters as much as volume

Models attend more strongly to the start and end of a context than the middle. A decisive fact buried in the middle of a long document is the one most likely to be missed.

Put framing at the start, bulk reference material in the middle where low attention costs you least, and the question plus output constraints at the end.

Frequently asked questions

Are these numbers measured?
No — the curve here is illustrative. The effect is well documented but its exact shape varies by model and task, so treat this as a way to reason about the trade-off rather than as data.
Then why use a million-token model at all?
For genuinely large single documents where chunking would lose cross-references. Not as a substitute for retrieval, which is what it usually gets used as.

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