Prompt Engineering

Why Token Counts Vary Between Models

The same text is a different number of tokens on GPT, Claude, and Gemini. This shows the spread on multilingual text — which is why an honest count is a range.

Open in Token Counter

Overview

Every model has its own tokenizer, so "how many tokens" has no single answer — only a per-model answer. This loads multilingual text, where the spread is largest: non-Latin scripts use more tokens per character, and each tokenizer handles them differently. The report puts all four model estimates side by side and explains why the headline number is a range, not a point. An honest counter shows the disagreement instead of hiding it behind one confident figure.

How to use this resource

Use varied text

Multilingual content shows the widest spread between tokenizers.
Compare the models

Four estimates for the same text, side by side.
Accept the range

No single true number — the range is the honest answer.

Why This Works

Each model's tokenizer is different, so the count genuinely varies
Multilingual text exposes the largest, most instructive spread
The range is presented as honesty, not hedging

Best for

Understanding tokenizer differences
Estimating non-English or mixed-language text
Anyone expecting one true token number

Not for

Exact per-model counts — use each provider's official tokenizer
Context-window fit — use the Context Window Estimator

FAQ

Why does the same text show different token counts for GPT-5, Claude, and Gemini?

Each model ships its own tokenizer, so the same multilingual text estimates at GPT-5 ~412, Claude Opus ~432, Claude Sonnet ~432, and Gemini Pro ~412 — the MODEL NOTES put them side by side. CJK and non-Latin scripts use more tokens per character, widening the spread. The token-counter shows the disagreement as a range (320-503) rather than one confident number.

Can I use these numbers for exact billing?

These are estimates, not invoices — the ESTIMATION NOTES state they are "character-based ESTIMATES, not tokenizer output," and pricing is approximate as of June 2026: "providers change rates — verify before relying on a number." For exact per-model counts, notFor points you to each provider's official tokenizer. The per-1,000-calls line ($8.40-$8.63 here) is for budgeting on volume, not an invoice.

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

Copy it as-is, or use Open in Token Counter to load it pre-filled and customize it with your own context.

TOKEN COUNT REPORT

TOKEN ESTIMATE
- Estimated tokens: ~412 (range 320–503)
- Characters: 703
- Words: 75
- Detected content type: CJK-heavy text
- Tokens per word (approx): 5.5
- Tokens per character (approx): 0.6

COST ESTIMATE — GPT-5
- Pricing (approximate, June 2026): input $1.25/1M tokens · output $10.00/1M tokens
- Input cost (this prompt): $0.000400–$0.000629
- Assumed response: Medium response (~800 tokens) -> output cost $0.008000
- Combined per call: $0.008400–$0.008629
- Per 1,000 calls: $8.40–$8.63

MODEL NOTES
- OpenAI tokenizer (o200k-class) — English averages roughly 4 characters per token.
- Same text, estimated per model: GPT-5 ~412 · Claude Opus ~432 · Claude Sonnet ~432 · Gemini Pro ~412 (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 medium 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.

More resources from Token Counter

Resource

Estimate Token Usage Before You Run It

Know how many tokens a job will consume before you send it — input plus an assumed response, costed per call and at scale.

Prompt Engineering

Resource

Token Counter for AI Prompts

Paste a prompt, get an honest token estimate — a range, not a fake-precise number — plus the cost across GPT, Claude, and Gemini.

Prompt Engineering

Resource

Calculate AI API Cost for a Prompt

Turn a prompt into a dollar figure: input cost, output cost, combined per call, and the number that actually matters — cost per 1,000 calls.

Prompt Engineering

Resources that pair well

Resource

Estimate Token Budget — Plan Before You Paste

Token budget planning for real workloads: how much of the window a transcript actually consumes, what is left for the answer, and how much headroom remains.

Prompt Engineering

Resource

Message Too Long — the Fix That Doesn't Butcher Content

The "message too long" error has a structural fix: split at paragraph boundaries into sequenced chunks with wait rules, instead of pasting fragments and hoping.

Prompt Engineering

Resource

Prompt Cleanup Examples (Before & After)

A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.

Prompt Engineering

Related tools

Tool

Token Counter

Estimate how many tokens a prompt is and what it costs — honest ranges across GPT, Claude, and Gemini, with per-call and per-1,000-call pricing.

Prompt Utilities

Tip: Save time by exploring related resources and tools that integrate with this resource.