How accurate is the count compared to OpenAI's tiktoken?

On English prose you should expect roughly ±10% versus tiktoken's cl100k_base. On code, JSON, or rare Unicode it can drift further — BPE merges are content-specific and any heuristic is going to miss them. The point of this tool is to give you a fast in-browser number for budget sizing, not to replace OpenAI's official tokenizer for billing reconciliation. If you need exact counts, use tiktoken in Python.

Does my text get sent to OpenAI, Anthropic, or Google?

No. The estimator runs entirely in your browser. It does not call the GPT, Claude, or Gemini API. It does not log your input. You can open DevTools, switch to the Network tab, and confirm that typing in the textarea makes zero outbound requests. The calculation is a few lines of arithmetic over character counts.

Why does Claude report fewer tokens than GPT for the same text?

Anthropic's tokenizer averages ~3.5 characters per token on English, versus ~4 for cl100k_base — so Claude actually tends to report slightly *more* tokens per character on prose. The flip side is Claude's lower per-token input price (Sonnet 4 at $3/M vs GPT-4 Turbo at $10/M), so total cost for the same text is usually lower on Claude even with the higher token count.

Where do the prices come from?

From each vendor's public pricing page as of 2026, listed in USD per million tokens — GPT-4 Turbo $10 input / $30 output, Claude Sonnet 4 $3 / $15, Claude Haiku 4.5 $1 / $5, Gemini 2.0 $0.5 / $1.5. Enterprise contracts, prompt caching discounts, and batch API rates are not reflected. If a vendor changes pricing we update this page.

How is CJK (Chinese / Japanese / Korean) tokenized?

For GPT-4 we count each ideograph as 1 token, for Claude 1.1, and for Gemini we apply the same 0.6 token-per-character rate as everything else. Real BPE often splits a single Chinese character into 2 or even 3 subword tokens, so these numbers are a lower bound — a 1000-character Chinese prompt may actually bill closer to 1500–2000 tokens on GPT-4. Treat the displayed number as the best case for non-Latin scripts.

What's the "if reply same length" column doing?

Most cost surprises come from forgetting that the model's output is also billed, usually at a higher rate than input. We assume the reply is the same token count as the input and apply the output-side per-million price. If you expect the model to return half as much, halve that number. If you expect a long generation (drafts, transcripts, code synthesis), multiply by 3–10. The column is a quick "don't forget output costs" reminder, not a prediction.

Size a prompt before pasting it into a paid console

Paste a prompt, document excerpt, or chat history and estimate token volume before sending it to a model that bills by input and output.

Compare vendor cost for the same input

Use side-by-side estimates to see whether a long context task is likely to be cheaper on a different model family before a prototype becomes a recurring workflow.

Explain a cost spike to a product team

Paste representative prompts and show how input length plus expected output length changes the bill, especially when a feature starts adding hidden model calls.

AI Token Counter — GPT, Claude, Gemini Cost Estimator

AI token counter — estimate token count for GPT / Claude / Gemini, with per-model cost calculator (2026 pricing).

Runs locally
Category AI Tools
Best for Estimating cost, shaping prompts, or comparing options before execution.

Text

Text stats

Characters

Words

CJK characters

Token estimates

Model	Input cost	If reply same length
GPT-4 Turbo $10/$30 per 1M	$0	$0
Claude Sonnet 4 $3/$15 per 1M	$0	$0
Claude Haiku 4.5 $1/$5 per 1M	$0	$0
Gemini 2.0 $0.5/$1.5 per 1M	$0	$0

Note：These numbers are heuristic estimates, not byte-exact. GPT/Claude use BPE — actual tokenization varies by content; expect ±10% on English prose, larger on code or non-Latin scripts. Use this for ballpark cost planning, not billing reconciliation.

What this tool does

Paste any prompt, article, code snippet, or full chat history and see the estimated token count for GPT-4 Turbo, Claude Sonnet 4, Claude Haiku 4.5, and Gemini 2.0 side-by-side — plus what that input would cost on each model, and what a same-length reply would add on top. The estimator uses public BPE heuristics: ~4 characters per token for Latin scripts on GPT-4 (cl100k_base), ~3.5 characters per token on Claude, and the documented per-character rate for Gemini, with extra weight on CJK characters where one ideograph typically maps to one token. No request leaves your browser. No external API call. No tracking. Use it to size a prompt before you paste it into a paid console, to compare what the same task costs across the four major vendors, or to sanity-check a bill before you panic. The numbers are ballpark — your actual tokenization depends on whitespace, code fences, and rare characters — but they are tight enough to plan a budget against. Pricing reflects publicly listed per-million-token rates as of 2026.

Tool details

Input: Text; The page exposes text boxes, numeric controls, file pickers, or structured inputs depending on the tool.
Output: Live result; The result area focuses on usable output, with copy, download, or preview actions when supported.
Privacy: Browser-side processing; The main tool logic does not call an external API, so inputs normally stay in the current tab.
Save / share: No account required; Open the page and use it; whether results survive refresh depends on the tool.
Performance budget: Initial JS <= 20 KB; No WASM budget is declared, keeping the tool quick to open on mobile.
Best fit: AI Tools · Developer; Category and role tags drive related tools, internal links, and quick fit checks.

How to use

1. Input

Paste or drop your content into the tool panel.
2. Process

Click the button. All processing is local in your browser.
3. Copy / Download

Copy the result or download to disk in one click.

How AI Token Counter fits into your work

Use it to plan, compare, or structure AI work before spending time or tokens on the real run.

AI workflow jobs

Estimating cost, shaping prompts, or comparing options before execution.
Turning vague AI work into a checklist, template, or measurable plan.
Keeping repeatable AI tasks consistent across a team.

AI checks

Review assumptions before sending data to a model provider.
Avoid pasting confidential data into prompts unless your policy allows it.
Treat generated recommendations as a draft until verified.

Good next steps

These links move the current task into a more complete workflow.

Real-world use cases

Size a prompt before pasting it into a paid console
Paste a prompt, document excerpt, or chat history and estimate token volume before sending it to a model that bills by input and output.
Compare vendor cost for the same input
Use side-by-side estimates to see whether a long context task is likely to be cheaper on a different model family before a prototype becomes a recurring workflow.
Explain a cost spike to a product team
Paste representative prompts and show how input length plus expected output length changes the bill, especially when a feature starts adding hidden model calls.

Common pitfalls

Treating the estimate as exact billing truth; real tokenizers and vendor pricing can differ.
Forgetting that model output is billed separately and often costs more per token.
Counting one user action as one model call when the product actually chains several calls.

Privacy

Token estimation is local arithmetic in the browser. Prompts, pasted chat histories, cost estimates, and model comparisons are not uploaded to Toolora or any model provider.

FAQ

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AI Token Counter — GPT, Claude, Gemini Cost Estimator

What this tool does

Tool details

How to use

1. Input

2. Process

3. Copy / Download

How AI Token Counter fits into your work

AI workflow jobs

AI checks

Good next steps

Real-world use cases

Size a prompt before pasting it into a paid console

Compare vendor cost for the same input

Explain a cost spike to a product team

Common pitfalls

Privacy

FAQ

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