AI token prices fall 41% from March peak, Ramp data shows

September 10, 2026 at 01:22 AM2 min

This digest was compiled by AI from multiple sources — links to the originals are below.

The effective price U.S. businesses pay per million AI tokens fell about 41% from its March peak, from $1.15 to 68 cents, according to Ramp data published Wednesday. The share of usage going to frontier models dropped from about 53% in early August to 45% by September. The top 1% of spenders, driving about 80% of OpenAI and Anthropic's enterprise revenue, cut per-employee spend by nearly 10% in August.

Key Facts

Ramp data shows the effective price per million AI tokens fell about 41% from its March peak, from $1.15 to 68 cents.
The share of usage going to frontier models dropped from about 53% in early August to 45% by September.
The top 1% of spenders, who drive about 80% of OpenAI and Anthropic's enterprise revenue, cut per-employee spend by nearly 10% in August.
Morgan Stanley has flagged vulnerability for up to $300 billion in bonds financing neocloud buildouts if token prices don't keep up.
OpenAI slashed the cost of its GPT-5.6 Luna model by 80%, and Anthropic announced its own cuts last month.

Token Price Decline

Ramp, the corporate spending platform, published data Wednesday showing the effective price American businesses pay per million tokens fell about 41% from its March peak, from $1.15 to 68 cents. The share of usage going to frontier models dropped from about 53% in early August to 45% by September. The top 1% of spenders, the cohort that drives about 80% of OpenAI and Anthropic's enterprise revenue, cut per-employee spend by nearly 10% in August. Ara Khazarian, the Ramp chief economist who runs the Index, told Fortune the trend is a "crack in the AI thesis" rather than a disaster or bubble bursting.

Market Implications

Morgan Stanley has flagged vulnerability for up to $300 billion in bonds financing neocloud buildouts—CoreWeave-style companies that borrowed to build data centers before signing tenants—if token prices don't keep up. Citadel Securities noted in June that Silicon Data's LLM Expenditure Index started to fall because of a "bifurcation" between frontier AI, concentrated among the few tech-heavy firms that can afford it, and the "everyday" AI the rest of the economy runs on. Khazarian said the price decline reflects a mix of labs being forced to cut prices—OpenAI slashed the cost of its GPT-5.6 Luna model by 80%, and Anthropic announced its own cuts last month—and customers trading down to cheaper and simpler models.

Corporate Cost Discipline

In the spring, "tokenmaxxing" entered the tech lexicon, with Nvidia CEO Jensen Huang insisting a $500,000 engineer should burn $250,000 a year in tokens. By the summer, cost discipline set in; Amazon and Meta killed their own leaderboards in May, while Microsoft cancelled Claude Code subscriptions. Khazarian said he is now hearing the opposite of tokenmaxxing from businesses: companies are imposing defaults that steer employees away from frontier models entirely.