Token Economics
Token Economics is the framework for measuring and optimizing the trade-offs between model performance, cost, latency, and token usage in AI deployments. In the era of LLMs, tokens have become the “currency” of intelligence.
Core Definition
Section titled “Core Definition”Tokens are the fundamental units of data (words, pixels, audio) that models process. AI Tokens Explained highlights that tokenization converts raw data into numerical representations.
The Tokenizer Factor (The Compression Ratio)
Section titled “The Tokenizer Factor (The Compression Ratio)”A lower “price per token” can be deceptive if the tokenizer is inefficient.
- Gemini’s SentencePiece: Uses a larger vocabulary (~256k) and treats input as a raw byte stream, often resulting in higher density and lower token counts than OpenAI’s BPE Gemini Analysis.
- Characters-per-Token (CpT): A crucial metric. Gemini is exceptionally efficient for multilingual scripts and code.
The Economic Shift
Section titled “The Economic Shift”As AI scales, focus is shifting to inference costs.
- Output-Based Metrics: NVIDIA argues for Cost per Token as the primary metric.
- Invisible Token Inflation: Actual cost must account for the “Tokenization Multiplier” Gemini Analysis.
Key Concepts
Section titled “Key Concepts”- Token-Based Billing: Costs are typically split between input/output tokens Healthark.
- Reasoning Tokens: Advanced models generate internal steps before output NVIDIA.
- Prompt Caching: A massive “burn rate” killer, offering 80-90% discounts on repeated queries Gemini Analysis.