LLM Token Expenditure Index: average cost per million tokens has fallen to 97 cents, part of a sharp months-long decline since hitting a high of $2.07 on May 28
Context & Ripple Effects
Silicon Data’s index was created late in 2025, according to contemporaneous discussion, making the 97-cent reading its lowest recorded level. The decline is being treated as a frontier-model economics story by syndicated coverage, which frames lower token pricing as pressure on OpenAI and Anthropic revenue.
The index measures token expenditure, not the cost of a completed task. One commentator noted that newer models may consume more tokens per request, a distinction that makes usage efficiency central to interpreting the price decline.
First-order effects
- Application developers buying model output face a lower per-token input cost, improving the economics of token-intensive workloads at unchanged usage levels.
- OpenAI and Anthropic face greater pressure to defend revenue per unit of model consumption as market token prices fall, as syndicated coverage frames it.
Second-order effects
- AI application vendors can compete more aggressively on included usage or lower-priced plans, while model providers have stronger incentives to differentiate on capability, reliability, and efficiency rather than token price alone.
- Rising tokens consumed per request can offset cheaper token rates for developers, shifting procurement attention from listed token prices to end-to-end workload cost.
Third-order effects
- If lower token prices persist while model requests grow more token-intensive, the relevant unit of competition shifts from cost per million tokens to cost per useful task.
- Frontier-model margins increasingly depend on whether providers can pair price reductions with efficiency gains, rather than treating token-volume growth as a sufficient revenue defense.
The trend: LLM economics are moving from headline token pricing toward effective inference cost per completed task as lower unit prices meet heavier model usage.