AI Model Spend Leaderboard
Estimated dollars spent per model, based on real token usage routed through OpenRouter and live per-token pricing. Every figure is an estimate — see methodology for the input/output ratio assumption.
| # | Model | Provider | Tokens (7d) | Blended $/M | Est. Spend (7d) | Range |
|---|---|---|---|---|---|---|
| 1 | GPT-6 Astra general |
openai | 246.06B | $16.00 | $3.94M | $2.95M – $5.41M |
| 2 | MiMo V2.5 general |
Xiaomi | 2.64T | $0.43 | $1.15M | $911.6K – $1.51M |
| 3 | Claude Sonnet 4.6 agentic |
Anthropic | 260.95B | $3.96 | $1.03M | $939.4K – $1.72M |
| 4 | MiniMax M3 agentic |
MiniMax | 445.37B | $1.49 | $662.7K | $614.6K – $1.02M |
| 5 | DeepSeek V4 Flash chat |
DeepSeek | 4.66T | $0.12 | $559.4K | $372.9K – $606.0K |
| 6 | Gemini 3 Flash Preview chat |
237.86B | $1.13 | $267.6K | $148.7K – $297.3K | |
| 7 | DeepSeek V4 Pro agentic |
DeepSeek | 695.77B | $0.32 | $219.9K | $215.7K – $250.5K |
| 8 | Gemini 2.5 Flash chat |
142.26B | $0.85 | $120.9K | $58.3K – $136.6K | |
| 9 | Gemini 3.1 Flash Lite chat |
161.08B | $0.56 | $90.6K | $50.3K – $100.7K | |
| 10 | DeepSeek V3.2 general |
DeepSeek | 133.93B | $0.30 | $40.3K | $38.4K – $43.1K |
| 11 | GPT-4o Mini chat |
OpenAI | 41.39B | $0.26 | $10.9K | $7.1K – $11.8K |
Source: OpenRouter (openrouter.ai/rankings), as of —.
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.