Where my AI tokens go

AI runs here for several reasons: an assistant handles conversations and scheduled work; social agents interact; FreeFiFoFum analyzes financial data; and experiments test models and the infrastructure around them.

Token counts help establish the scale of those workloads. They do not measure productivity. Some activity produces useful task results; some is recreation or a way to learn about harness design and model behavior. This page keeps consumption and spending visible without treating a larger number as a better outcome.

Evidence reviewed: October 25, 2026. This is a partial view assembled from records with different windows, not a single monthly bill. The intention is to maintain it as better accounting becomes available, without promising a reporting schedule.

What is covered

These rows should not be summed into a consumption total. They mix daily averages, cumulative periods, and an individual backtest. Some source reports also describe the same requests from different viewpoints.

Existing hardware, hosted usage, and subscriptions

“Zero cost” in a request log can mean several things:

There is no assumption that owning a model server is the cheaper way to accomplish a task. A price comparison needs the workload, quality requirements, usage frequency, and the costs included on each side.

The agent fleet: repeated context dominates

For seven retained GothMUD agents plus Chassy, the October analysis finds daily averages of 4,597 model calls, 144.90 million input tokens, and 340,000 output tokens. About 138.42 million input tokens were cached.

Those agents were part of a larger fleet during the measurement window. The figures select the agents that remained after a later reduction; they are not a measurement of the smaller fleet operating alone.

The hosted day changed the estimate

The initial $7–8/day estimate used that smaller group’s earlier traffic and local cache reuse. It was not a reliable forecast of the larger workload that actually ran hosted.

For October 23 UTC—October 22 at 20:00 through October 23 at 20:00 EDT—the OpenRouter export records 7,589 requests, 364.46M input tokens, and 3.749M completion tokens. About 67.3% of input was reported cached. That is materially different from the earlier full-fleet averages of 277.46M input, 0.687M output, and 96.1% cached input per day, even before considering the smaller retained group.

The large underestimate came from the workload assumptions: population, token volume, and cache behavior. Repricing the observed usage gets within $0.23, about 0.8%, of the provider charge. Most of that residual comes from the Parasail records; their exact pricing difference remains unresolved.

The provider charge is also 17.5% below Otari’s estimate. These are two different estimation errors: the initial scenario understated demand, while the gateway’s pricing overstated the cost of the observed day. The provider and gateway counts differ by 29 records, so their comparison is not yet a matched-request reconciliation. They describe overlapping activity and must not be added together.

Thirty days at the observed daily charge would be about $835. That is a constant-usage projection, not a monthly bill or a forecast for the reduced fleet. The full export totals $29.74 across 8,180 requests over October 19–24 UTC; that wider export must not be mistaken for one day’s usage. Neither scope establishes individual-agent attribution from provider records alone.

The fleet-load report walks through the estimate-to-charge reconciliation. It also explains what is still unknown about the changes in traffic and behavior.

What this means for the next price comparison

An estimate for Haiku, Sonnet, or another model should start with a defined workload and reporting window, then use that provider’s token, cache, and other billing rules. Repricing the observed Qwen workload answers what those quantities would cost; it does not predict how another model will behave. Call counts, output length, cache reuse, and interactions between agents can all change.

Before trusting a monthly forecast, first reconstruct a known charge, show the assumptions and uncertainty, then validate with a separately authorized, spending-limited pilot. No such target-model pilot has been run for this report. More than one representative day would be needed to judge whether an observed rate is typical.

Jane: accounting changed the cost story

Jane is a personal assistant with both an ongoing conversation and scheduled work. The available gateway report covers successful requests on two Jane routing policies, not all of Jane’s activity through every provider.

An earlier price book charged some hosted cached input at the uncached rate. The corrected estimate adjusts affected QwenCloud pay-as-you-go requests before September 27, 11:03:40 UTC; later records pass through unchanged. The $29.53 is not a newly reconciled invoice for all providers, and it does not add subscription charges or local operating costs.

This correction matters: an inflated estimate of hosted spend can make a hardware purchase look easier to justify than it actually is.

The source splits requests between the main conversation and scheduled jobs using time and token-count matching. Some attribution remains uncertain, including thousands of unattributed scheduled requests. Its conversation, daily, and weekly tables overlap; adding them would count the same usage again.

Cache reporting has another limitation: the generator turns missing cache counts into zero. A displayed zero-percent cache rate therefore cannot establish that a model server reused no cached context. Cache comparisons need that distinction repaired first.

FreeFiFoFum: experiments and recurring analysis

A saved analysis of the midcap_rotation_v6 backtest reports:

About 98.5% of those tokens were input: financial filings, earnings material, news, and evidence passed through specialist and judge calls. The simulated market window is not the date when inference ran.

The original analysis includes hosted-price scenarios and a weekly-rebalance projection. Those are historical estimates, not actual hosted expenditure or a measurement of today’s nightly committee. Current recurring committee consumption remains a separate accounting gap.

RSP: paid frontier-model activity

RSP adds agent sessions and external model consultations within the FreeFiFoFum project. Saved records provide a partial cost picture:

The session set includes a rerun; its recorded session IDs are distinct. Consultant summaries duplicate their transcript costs, so only one representation is counted. Some model records explicitly identify list-price cost estimates. These figures are reported model costs, not a complete paid RSP total or an invoice reconciliation.

Market-data subscriptions are another project expense, separate from model inference. They are outside this token accounting.

How this reference will change

Provider exports can improve the coverage of hosted charges. An OpenRouter export covers OpenRouter activity, not Jane’s separate pay-as-you-go or token-plan billing, or her GitHub Copilot sessions. Each source needs its own billing category and coverage window before records can be combined. Gateway logs supply routing context, while direct-provider exports supply another accounting view of overlapping requests.

The next useful improvements are aligned reporting windows, current committee totals, missing consultant charges, and explicit handling of unknown cache usage. As those become available, updates should retain the earlier scope and explain corrections rather than silently replace historical numbers.

The sources are aggregate usage reports, a historical backtest analysis, and saved session/consultation accounting fields. Private conversations, financial decision details, and raw agent captures are not needed to explain these totals.

Revision history

• October 25, 2026 — Initial publication. Added the UTC-aligned hosted day, replaced the unsupported $7–8/day expectation with an estimate-to-charge reconciliation, and distinguished repricing from forecasting another model’s behavior.