What share do we actually see?
Primary definition: continuous, directly metered tokens in the database divided by a modeled worldwide or U.S. denominator. The disclosure ledger is shown separately because it is broad but stale and asynchronous.
Current world denominator
Tokens Per Day's July 16 six-channel band, nowcast to the latest OpenRouter date at its published 2.2× annual growth assumption.
Coverage arithmetic
The base estimate uses a smoothed seven-day router average, not a volatile single day.
Usage is compounding much faster than price is falling.
OpenRouter is the direct platform series. The global line is a modeled quarterly band. They answer different questions and are deliberately not spliced into one series.
OpenRouter tokens per day
Exact public-platform tokens; daily and 7-day moving average.
Modeled global tokens per day
Quarterly low / mid / high band, ending at the July 16 model anchor; trillions per day. Early growth includes both real growth and improved disclosure coverage.
Relative strength by model lab
Volume share and momentum within OpenRouter—not global lab market share. Free routes, app mix, and new-model launches can move this ranking quickly.
Monthly average daily tokens by leading lab
Top eight labs by current volume; all others bucketed as Rest. Monthly averages keep partial August comparable with full months.
Current lab leaderboard
Sorted by latest seven-day token volume.
| # | Lab | 7d tokens | Share | WoW | 30d tokens |
|---|
Model strength and indicative list-price economics
Token volume provides adoption momentum; current list price provides a rough revenue-intensity axis. The cost math is not realized billing because prompt/output mix, caching, discounts, and historical prices are unavailable.
Volume versus blended list price
Top 30 models; price assumes 75% input / 25% output and current OpenRouter list prices. Free models appear at zero.
Frontier versus budget cost index
MyTokenTracker fixed baskets, $/million tokens, 3:1 input:output. The public history is short and currently flat.
Current model leaderboard
Indicative cost applies today's blended list price to all seven-day tokens. It is a comparable intensity measure, not actual provider revenue.
| # | Model | Lab | 7d tokens | Share | WoW | Blended $/M | Indicative 7d list cost |
|---|
Where the tokens are going
Publicly attributed applications and sampled weekly task filters provide a directional demand map. Neither is a complete application census.
Top public apps—trailing 30 days
OpenRouter public attribution; trillions of tokens.
Task-filter token estimates
Latest sampled/upsampled week. Filters should not be summed into a market total because labeling may overlap.
Public-data tokenomics model
A bottom-up demand, capacity, and economics model built from public disclosures and reproducible benchmarks. The model exposes its residual instead of using a global multiplier to make the numbers balance.
Demand components and uncertainty
Low / base / high trillion processed tokens per day. Cross-check rows are labeled and excluded from the additive total.
Demand versus public capacity
The public supply bar includes only a dated xAI operating-fleet disclosure. It is a feasibility subset—not global supply.
The residual is primarily an incompleteness diagnostic. Google, Meta, Microsoft, Amazon, Oracle, CoreWeave, Chinese operators, and private enterprise fleets are not yet represented in operating supply. It is not unmet demand and it is not assigned to xAI.
Component ledger
Observed, modeled, and capacity-derived rows stay separate. Additive status determines whether a demand row enters the total.
| Component | Channel | Class / confidence | Low | Base | High | Additive |
|---|
Component methodology
Open each component for its exact formula, transformation procedure, source links, and known failure modes.
Assumption ledger
Every direct input and modeled parameter, with source, date, confidence, and rationale.
| Component | Channel | Metric | Low | Base | High | Unit | Confidence | As of |
|---|
Unresolved channels
These are intentionally left visible rather than filled by an opaque market multiplier.
Definitions, assumptions, and sources
The dashboard separates metered router traffic, modeled global demand, disclosure representation, sampled classification, and cost proxies.
Capture-rate definitions
- Worldwide direct capture: latest seven-day average OpenRouter public-platform tokens divided by the nowcast Tokens Per Day global low/mid/high band.
- U.S. direct capture: a modeled OpenRouter U.S. numerator divided by modeled U.S. demand. Base case applies 47% to both; the range allows router geography and market geography to differ.
- Disclosure representation: the curated 300.1T/day supply/national floor divided by the global modeled denominator. It is broad but asynchronous and should not be called live capture.
Key assumptions
- The July 16 global band is carried to the OpenRouter cutoff using Tokens Per Day's published 2.2× annual growth assumption.
- China remains 140T/day in the U.S. bridge; the base U.S. share is 47% of non-China usage. This is a low-confidence OpenRouter billing-location proxy.
- OpenRouter token definitions are accepted as reported by upstream providers; tokenizer and hidden-reasoning treatment are not harmonized.
- Current model prices are blended 75% input / 25% output. Cache discounts, free-route subsidies, enterprise discounts, and historical price changes are excluded.
- Router, supply, and demand ledgers are never summed. Missing dates remain missing. No world total is inferred from row counts.
Source ledger
What is missing
Continuous first-party ChatGPT, Claude, Gemini, Meta AI, and Grok app usage; direct enterprise lab APIs; AWS Bedrock, Azure, and Vertex totals; private/ZDR routing; self-hosted open-weight inference; and reliable end-user geography. These omissions are why the direct worldwide estimate is only a few percent even though point disclosures represent much more of the modeled volume.