Reasoning Cost
Extra model work caused by poor context.
When context is messy, the model doesn't just produce worse output — it works harder and charges more to produce it. Every ambiguity that needs resolving, every contradiction that needs reconciling, every irrelevant paragraph that needs filtering costs reasoning tokens. These terms name the different ways that poor context translates into real cost — measured in tokens, dollars, time, and quality.
Reasoning Tax
Extra model cost paid to compensate for context debt.
Token Bleed
Silent budget drain from re-onboarding — tokens spent re-explaining context the system should already hold.
Token Burn
Token waste caused by a client sending the entire session history to the LLM on every call.
Context Compensation
The model using additional reasoning to make sense of poor, bloated, or unstructured context.
Reasoning Inflation
More reasoning is required to extract the same signal from worse context.
Reasoning Load
The amount of interpretive work a model must perform before useful task execution begins.
Cognitive Drag
Friction introduced by poorly structured context.
Reasoning Efficiency
The model producing correct output with minimal interpretive overhead — the reward for clean context.
Token Leverage
Getting more useful output per token spent. The inverse of Token Bleed.
First-Pass Execution
The model completing a task correctly on the first attempt because context was clear enough to skip interpretation loops.