Reasoning Efficiency
The model producing correct output with minimal interpretive overhead — the reward for clean context.
Reasoning Efficiency is the positive outcome that all the negative terms in this category point toward. When context is clean, structured, and focused, the model spends its reasoning budget on the actual task rather than on interpretation. The result is faster responses, lower cost, higher accuracy, and less need for follow-up corrections. Reasoning Efficiency isn't a feature you turn on — it's what happens naturally when Context Debt, Prompt Bloat, and Reasoning Load are minimized.
More in Reasoning Cost
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.
Token Leverage
Getting more useful output per token spent. The inverse of Token Bleed.