LLM Token Expenditure Index — SDLLMTK
Daily benchmark for large language model inference token prices across different providers. Published every trading day as ticker SDLLMTK.
ABOUT THIS INDEX
The LLM Token Expenditure Index (SDLLMTK) is the Silicon Data benchmark for large language model inference token pricing. It is published daily as a normalized blended rate expressed in USD per million tokens, drawn from observations across frontier API providers, open-weight inference platforms, brokered dedicated-instance markets, and self-hosted reference deployments.
The index is published as a single blended reading across the LLM inference market, but daily movements carry a clear signature — different segments of the market leave distinct fingerprints on the print, and our daily commentary calls out which corner is driving the move. Every observation entering the calculation is normalized for input/output token mix, context window, batching behavior, and reliability — the index reflects realized cost-to-serve, not headline list pricing on any single provider's pricing page.
PRICE DRIVERS
1
Provider Pricing Power
The Index captures how model providers translate capability, scarcity, and competitive positioning into market-facing token prices. Rising levels may signal stronger pricing power or sustained willingness to pay for frontier performance.
2
Market Usage Concentration
The Index is weighted by where usage is concentrated and sustained. When demand shifts toward premium models, expenditure rises; when capable lower-cost models gain adoption, the market becomes more cost-efficient.
3
Infrastructure-to-Model Efficiency
The Index reflects how GPU costs, memory constraints, inference efficiency, and serving optimization flow through into real token economics. It helps distinguish whether infrastructure improvements are causally contributing to a more efficient token market.
METHODOLOGY
How the LLM Index is calculated
The LLM Token Expenditure Index is calculated daily from observations across different model providers. Observations are normalized for input/output mix, context window, then filtered for models with sustained usage and market expenditure. Each day’s publication are independently validated.
FORWARD CURVE
What does the LLM Token Expenditure Index actually measure?
Why use the term “token expenditure” instead of “token price”?
How does the index avoid becoming a static catalog of models?
What can cause the index to move up or down?
How should customers use the index in practice?