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The rate-limit counter on my gpt-5-mini deployment decrements by

A developer reports that Azure OpenAI's rate-limit counter for gpt-5-mini deployments decrements by exactly the input token count, not by the documented formulas that include max_tokens. In 29 paired samples, 20 charges matched usage.prompt_tokens exactly, while 7 exceeded input after connection-error retries, and none matched input + completion. The behavior was identical across reasoning_effort settings, ruling out reasoning-budget effects, and contradicts Azure's documented worst-case ceiling and prompt + max_tokens formulas.

read2 min views1 publishedAug 29, 2026
The rate-limit counter on my gpt-5-mini deployment decrements by
Image: Promptcube3 (auto-discovered)
`x-ratelimit-remaining-tokens`

on a Global Standard gpt-5-mini (1M TPM, Sweden Central, API version `2024-12-01-preview`

), computed each charge as limit - remaining

, and kept only responses where a single request finished within ±2 seconds so the delta was attributable to one call.Breakdown of the 29 paired samples:

20 charged exactlyusage.prompt_tokens

(examples: 8,329 / 13,605 / 40,073 / 93,837)2 within 5% ofinput + max_tokens

ormax(input, max_tokens) — probably coincidence7 charged well above input — all retried after connection errors (one request with 252,781 input tokens was charged 630,582, suggesting two attempts each hit by a character-based input estimate)

This rules out the documented formulas:

  • One request was charged 8,329 tokens with max_completion_tokens = 10000

. No formula involving max_tokens — sum or maximum — can produce a charge below max_tokens itself. input + completion

matched 0 of 29. Completions aren't counted.- Behavior was identical with reasoning_effort

atminimal

,low

, and unset. Not a reasoning-budget effect.

The Azure docs are contradictory. One section says the estimate "includes prompt text and count, the max_tokens parameter setting, the best_of parameter setting" — a worst-case ceiling reading. The 429 section commits to an actual sum: "prompt + max_tokens." Neither matches reality. OpenAI's platform docs use

max(max_tokens, character-based estimate) — also doesn't match.So for a completed request, the counter visibly ends at exact input tokens with no max_tokens component. What I can't observe externally:

  1. Is the counter charged when the request arrives (the documented prompt + max_tokens estimate, later reconciled to actual input), or charged once at completion with the exact input?

  2. If requests are charged on receipt, does the over-estimate get refunded, or is the refund delayed/aggregated such that it's invisible in my per-request delta?

  3. For the retried requests, is each attempt charged independently against the remaining budget, or is there a deduplication window?

Has anyone else seen this exact-input-only behavior? I'm especially curious if the charging-on-receipt theory holds — that would explain why max_tokens never shows up in completed-request deltas.

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