Token efficiency, not just model cost, will decide AI budget winners

The assumption that selecting a capable low-cost model solves AI spending has become outdated. A far more pressing issue now confronts development teams: agentic systems churn through tokens far more rapidly than traditional inference. A moderately intricate agent request can consume 20,000 to 60,000 tokens within its reasoning chain alone, while genuinely complex engineering work can demand 150,000 to 200,000 tokens per task.

The result is that token economy—not model pricing—is becoming the dominant factor in controlling AI infrastructure costs.

The compounding cost of multi-agent handoffs#

When multiple specialized agents collaborate on a single workflow, expenses balloon unexpectedly. Each agent needs sufficient context to execute its assigned work, which can mean processing 30,000 tokens of input just to generate a 500-token output. Every time one agent hands off work to another, the receiving agent must absorb the sender's state, reasoning, and instructions into its own context window. When results flow back to the orchestrator, that information gets reingested alongside ongoing work.

These exchanges create a multiplier effect: a task solvable in roughly 50,000 tokens by a single agent can easily spiral into several hundred thousand tokens when distributed across a multi-agent chain. Each handoff layers additional overhead.

Practical paths to token efficiency#

Three strategies are emerging as particularly effective:

Compress context without losing reasoning

Rather than preserving an ever-expanding conversation history that must be replayed with each step, systems can summarize earlier portions before passing them downstream. One approach narrows what the agent sees—showing only the specific codebase sections or document excerpts relevant to the current task rather than the full collection. A careful balance is required: too much filtering and the agent misses crucial information it will later need.

The solution combines compressed context with a compact memory layer storing key decisions and facts, allowing the agent to recall its reasoning path without constantly re-reading full histories.

Assign tasks by complexity tier

Not every subtask requires the most capable—and expensive—model. Hierarchical routing evaluates which model suits each component: lightweight models handle parsing JSON, formatting logs, or checking file existence, while more sophisticated models tackle decisions that genuinely demand deeper reasoning. When 60 to 70 percent of an agent's operations are routine work, routing them through smaller models can slash token spend across the entire workflow.