Seven Classes of AI Tokens
A taxonomy for understanding AI system economics. Every token your system consumes falls into one of these categories—and each has different cost, value, and optimization characteristics.
Why Token Classification Matters
Most teams track total token consumption without understanding what they are paying for. This makes cost optimization nearly impossible. By classifying tokens into distinct categories, you can identify which token classes dominate your costs, optimize the high-impact categories first, and make informed architectural decisions about caching, retrieval, and context management.
The seven classes represent different phases of AI system operation, from foundational setup through reasoning to final validation. Each class has different optimization strategies.
The Seven Token Classes
System Tokens
System prompts and agent personality definitions
Characteristics
Consistent across requests, foundational framing
Example
Agent identity, role definitions, behavior constraints
Context Tokens
Retrieved information that grounds the response
Characteristics
Variable per request, comes from RAG or knowledge bases
Example
Document chunks, knowledge base entries, retrieved facts
Reasoning Tokens
Tokens consumed during model thinking and planning
Characteristics
Internal processing, chain-of-thought, planning steps
Example
Step-by-step reasoning, plan generation, decision evaluation
Output Tokens
The actual response generated for the user or system
Characteristics
Final deliverable, what the user sees or downstream systems consume
Example
Answers, generated content, structured responses
Tool Tokens
Tokens for tool definitions, calls, and results
Characteristics
Function schemas, invocation parameters, return values
Example
API call definitions, function parameters, tool outputs
Memory Tokens
Conversation history and persistent context
Characteristics
Grows over time, requires management strategy
Example
Chat history, user preferences, session state
Evaluation Tokens
Tokens used for quality assessment and validation
Characteristics
Often overlooked in cost models, critical for production
Example
LLM-as-judge calls, output validation, safety checks
Optimization Strategies by Class
System & Context Tokens
Cache aggressively, optimize prompts, implement smart retrieval strategies
Reasoning Tokens
Use appropriate model sizes, implement early stopping, consider when reasoning is necessary
Tool & Memory Tokens
Prune tool definitions, implement memory decay, consolidate history
Evaluation Tokens
Sample-based evaluation, use smaller models for validation, batch evaluations
Key Insight
Evaluation tokens are often the hidden cost in production AI systems. Teams budget for inference but forget that every safety check, quality assessment, and output validation consumes additional tokens. A production system with proper guardrails may spend 20-40% of its token budget on evaluation alone.