Prompt Engineering

Estimate Cost per 1,000 Calls

A classification call costs a fraction of a cent — until you run a million of them. This prices a small repeated prompt at the scale that actually bills.

Overview

Per-call pricing hides the truth for high-volume work: the number that matters is cost at scale. This loads a short sentiment-classification prompt — the kind you might run on every review or ticket — and prices it per 1,000 calls, where fractions of a cent become real money. For batch and pipeline workloads, the single-call figure is a rounding error and the per-1,000 figure is the budget. The tool leads with the number you will actually be billed on.

Workflow

Load the unit prompt
The short prompt you run on every record.
Read the per-1,000 line
Fractions of a cent multiply into a real figure.
Project your volume
Scale the per-1,000 number to your actual call count.

Why This Works

High-volume cost lives in the per-1,000-calls line, not the per-call one
Short repeated prompts are exactly where scale cost hides
Leading with the scaled number prevents budget surprises

Best for

Batch classification and extraction pipelines
High-volume API workloads
Projecting cost from a single unit prompt

Not for

One-off prompts where scale is irrelevant
Exact billing — use your provider dashboard

Use cases

Batch classification and extraction pipelines
High-volume API workloads
Projecting cost from a single unit prompt

Customize This Resource

Opens this text in Token Counter. Count to get the full token and cost report — then adjust the model and assumed response length.

Open in Token Counter

Prompt Template

TOKEN COUNT REPORT

TOKEN ESTIMATE
- Estimated tokens: ~53 (range 48–58)
- Characters: 208
- Words: 35
- Detected content type: Prose
- Tokens per word (approx): 1.5
- Tokens per character (approx): 0.3

COST ESTIMATE — GPT-5
- Pricing (approximate, June 2026): input $1.25/1M tokens · output $10.00/1M tokens
- Input cost (this prompt): $0.000060–$0.000073
- Assumed response: Short response (~200 tokens) -> output cost $0.002000
- Combined per call: $0.002060–$0.002073
- Per 1,000 calls: $2.06–$2.07

MODEL NOTES
- OpenAI tokenizer (o200k-class) — English averages roughly 4 characters per token.
- Same text, estimated per model: GPT-5 ~53 · Claude Opus ~56 · Claude Sonnet ~56 · Gemini Pro ~53 (a count, not a fit check — for "will it fit?" use the Context Window Estimator).

USAGE GUIDANCE
- For scale on GPT-5: a short prompt ≈ 71, a medium prompt ≈ 506, a large prompt ≈ 3,031 tokens.
- This text is closest to a short prompt.
- A single call looks cheap; the per-1,000-calls line is where token cost becomes real — budget on volume, not on one request.

ESTIMATION NOTES
- A token is not a character and not a word — it is a sub-word chunk. English averages ~4 characters / ~0.75 words per token.
- Estimates vary by tokenizer: the same text tokenizes differently on GPT, Claude, and Gemini — that is why this is a range, not a single number.
- Language matters: CJK and many non-Latin scripts use more tokens per character than English.
- Code differs from prose: symbols, indentation, and punctuation push code to more tokens per character.
- These are character-based ESTIMATES, not tokenizer output. Pricing is approximate as of June 2026; providers change rates — verify before relying on a number.

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