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AI Meeting Assistant Token Budget Planner

The AI Meeting Assistant Token Budget Planner estimates the language-model tokens needed to process meetings into transcripts, summaries, action items, and structured notes. It models transcript length from meeting duration and speech rate, then adds prompt context and expected generated output for every meeting in a batch.

The estimate helps teams choose context-window strategies, forecast API use, and decide when long meetings should be chunked. Conversation overlap, silence, language, and note detail can change token use, so the safety margin is intended to cover normal variation rather than replace measurement.

Meeting token assumptions

min
%
words/min
tokens
tokens
tokens
meetings
%
Result
—
planned tokens for all meetings
Transcript words per meeting—
Base tokens per meeting—
Planned tokens per meeting—
Approx. 1M-token blocks—

1. Step 1
Enter the scheduled or average length of one meeting.

2. Step 2
Estimate what share of that time contains active speech rather than silence or breaks.

3. Step 3
Set the speaking rate and tokens-per-word conversion for the meeting language.

4. Step 4
Add prompt context and expected summary, action-item, or note output tokens.

5. Step 5
Enter the number of meetings and a margin for length and tokenization variation.

6. Step 6
Use the per-meeting figure to check context limits and the batch total for budget planning.

Transcript words per meeting = meeting minutes × active speech share × words per active minute. Transcript tokens = transcript words × tokens per word. Base tokens per meeting = transcript tokens + prompt tokens + output tokens. Planned total = base tokens × (1 + safety margin) × meeting count.

What the result means

The main result is the token allowance for the full meeting batch, including transcript processing and generated notes.

Audio ingestion charges and speech-to-text billing are not included unless they are token-based and entered separately in the prompt or output allowance.

Given: A 45-minute meeting with 65% active speech, 150 words per active minute, 1.33 tokens per word, 1,500 prompt tokens, 1,800 output tokens, 20 meetings, and 20% margin.

Calculation: Words = 45 × 0.65 × 150 = 4,387.5. Transcript tokens = 4,387.5 × 1.33 = 5,835.4. Base = 5,835.4 + 1,500 + 1,800 = 9,135.4. Planned batch = 9,135.4 × 1.20 × 20 = 219,249.

Result: Budget approximately 219,249 tokens.

Why use active speech share?

Meeting duration often includes silence, breaks, and screen-sharing time that do not produce transcript words.

How should overlapping speakers be treated?

If the transcription system captures overlapping speech, increase active speech share or use measured transcript word counts from similar meetings.

Do calendar descriptions count as prompt tokens?

Yes, when they are sent to the model as context. Include agendas, participant metadata, and system instructions in the prompt allowance.

When should a meeting be chunked?

Chunk when the combined transcript, prompt, and reserved output may approach the model context limit or when incremental processing improves reliability.

Does the result include storage tokens?

No. Tokens are a model-processing unit, not a measure of audio or transcript storage capacity.