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Why AI usage numbers do not match

Two tools report different token totals for the same work. Here are the six reasons, and which number to trust for which decision.

Quick answer

Disagreements usually come from scope, fields, or timing. One tool counts a session, the other a billing period; one includes cached and reasoning tokens, the other does not; one updates instantly, the other hourly. Read both definitions before treating the gap as an error.

The six causes

CauseExampleFix
ScopeOne project vs whole accountMatch the period and filter
FieldsCached tokens included or notSeparate the fields
TimingInstant vs hourly refreshWait, then re-check
RetriesFailed calls counted onceLog attempts
RoundingThousands abbreviatedUse raw counts
PlansAllowance vs raw tokensDo not compare directly

Which to trust

  • For whether you can continue: the provider's reported figure.
  • For what a task consumed: the local session record.
  • For the invoice: the billing statement.
  • For trends: whichever source you used last month, consistently.

Consistency beats precision. A series with a stable method is more useful than a perfectly accurate point that cannot be compared with anything.

Reconcile in four steps

  1. Align the period and the scope.
  2. List which token fields each side includes.
  3. Account for other machines and web usage.
  4. Write down the remaining difference as a known gap.

UsageAtlas shows the fields separately for that reason. See local vs provider data and how usage is measured.

Questions, answered

Why does my tracker show fewer tokens than the provider?

The provider includes other machines, web and desktop sessions, and fields your local logs may not record.

Can two trackers disagree?

Yes, if they treat cached or reasoning tokens differently or use different periods. Check definitions before assuming a bug.

Which number should I report?

Use the provider figure for limits and billing, and local figures for task and project attribution. State which you used.

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