AI Model Spend Leaderboard

Methodology

How we estimate spend. OpenRouter publishes daily token totals per model (prompt + completion tokens combined). We multiply those totals by each model's published per-token pricing to estimate dollars spent.

The input/output caveat. OpenRouter's dataset does not split tokens into input vs. output, and output tokens typically cost 3–5× more than input tokens. We therefore apply an assumed mix — by default 85% input / 15% output for general traffic, and more input-heavy assumptions (90–92%) for coding and agentic models, reflecting how production agent workloads consume context. Because this is an assumption, every figure is shown with a range: the low bound assumes a 95/5 input-heavy mix, the high bound assumes 70/30.

What this does and doesn't capture. Figures reflect usage routed through OpenRouter only — not direct API traffic to OpenAI, Anthropic, Google, etc. — so treat them as directional, not total-market. Prompt-caching discounts are not reflected, which may overstate spend on heavily cached models. Token counts come from each provider's own tokenizer, so cross-model token comparisons are approximate; dollar math is unaffected.