acento.io
Developer tool

Token Counter

Estimate LLM tokens across GPT-4o, Claude, Gemini, and Llama instantly — no API key, no server, no data upload.

By Carlos Suárez , Systems engineer
Last updated:

What this Token Counter does

This English-language token counter estimates how many tokens your text consumes across five major LLM families — GPT-4o, GPT-3.5, Claude, Gemini, and Llama — in a single pass. Token counts drive API costs, context-window limits, and prompt design, yet most free tools force you to pick one model before you get any numbers. This tool shows all models side by side so you can compare tokenization differences without switching tabs. Beyond raw token counts, it surfaces character count, word count, and sentence count, and projects cost at typical API rates for each model. Because tokenizers vary — GPT models use BPE via tiktoken, while Claude and Gemini use their own schemes — the same 500-word paragraph can produce meaningfully different token totals depending on which model you target. 100% client-side — your data never leaves your browser. No uploads, no tracking, no server logs. The Oxford English Dictionary tracks roughly 600,000 word forms, but the average native speaker uses around 20,000; knowing the token weight of your actual vocabulary helps you budget prompts with confidence.

Features

  • Multi-model token estimates. Displays token counts for GPT-4o, GPT-3.5, Claude, Gemini, and Llama simultaneously — no need to switch tools or re-paste your text.
  • Cost projections. Shows estimated API cost at current published rates for each model family, so you can pick the most cost-effective option before you commit.
  • Text statistics. Reports character count, word count, and sentence count alongside token totals — useful for prompt audits and content planning.
  • Privacy by design. All computation runs in your browser via JavaScript. Nothing is sent to a server, logged, or stored. You do not need an account or API key. This matters especially after incidents like Cloudbleed demonstrated how server-side processing can expose user data unexpectedly.
  • Tokenization difference view. Highlights where GPT and Claude tokenizers diverge on the same input — punctuation, whitespace, and non-ASCII characters are common culprits.
  • Clipboard integration. Copy the full results summary to your clipboard in one click for quick pasting into a prompt log, cost spreadsheet, or Slack message.

How to use the Token Counter

Paste or type your text into the input box and click Calculate. Results appear instantly — no page reload needed.

  1. Paste your text. Drop any plain text into the input field — a system prompt, a document excerpt, or a raw string like Hello, world!. Markdown, code, and Unicode are all supported.
  2. Click Calculate. Hit the Calculate button. The tool runs all tokenization estimates client-side and renders counts for each model in under a second.
  3. Review per-model counts. Compare token totals across GPT-4o, Claude, Gemini, and Llama. Pay attention to outliers — a prompt heavy in punctuation or code often tokenizes differently between model families.
  4. Check cost projections. Review the projected API cost for each model at typical rates. For high-volume use cases this difference can add up fast.
  5. Copy the results. Use the Copy button to grab a formatted summary. You can paste it into a prompt design doc or a cost-tracking sheet.

Common use cases

  • Prompt engineering budgets. Developers in New York and Seattle building GPT-4o or Claude integrations use token counts to stay inside context windows and control per-request spend before deploying to production.
  • RAG chunk sizing. When building retrieval-augmented generation pipelines, engineers need to know how many tokens each document chunk consumes so embeddings and context windows stay within model limits.
  • Comparing OpenAI vs. Claude costs. The same prompt can cost 10–20% more on one model family than another due to tokenization differences. Running both through this tool in one pass surfaces that gap immediately.
  • Content and copywriting audits. Technical writers and content teams use word and token counts together to calibrate AI-assisted drafts — Stack Overflow questions average about 130 words, which lands at roughly 170–190 tokens depending on the model.
  • API cost forecasting. Product teams building features that call LLM APIs use token projections to model monthly spend at different usage tiers before committing to a pricing plan. If you also work with encoded payloads, the [Base64 encoder & decoder](/en/base64/) and [URL encoder & decoder](/en/url-encoder/) handle encoding transformations without leaving the browser.

Frequently asked questions

Is my text sent to any server?

No. Every calculation runs entirely in your browser. No text, no metadata, and no results are transmitted to any server. There is no account, no API key, and no session logging. You can disconnect from the internet after the page loads and the tool still works.

Why do token counts differ between GPT-4o and Claude for the same text?

Each model family uses its own tokenizer. GPT models use a byte-pair encoding (BPE) scheme via tiktoken; Claude and Gemini use different vocabularies trained on different corpora. Whitespace handling, punctuation merging, and non-ASCII characters cause counts to diverge — sometimes by 10–20% on technical prose or code.

What is an OpenAI token, exactly?

A token is a chunk of text — roughly 4 characters or ¾ of a word in average English prose, though this varies widely. Short common words like 'the' are usually one token; longer or rarer words may split into two or more. The WHATWG Encoding Standard governs how raw bytes map to characters, which is the layer below tokenization.

How accurate are the cost projections?

Projections use publicly documented rates for each model family at the time the tool was built. Actual costs may differ if providers update pricing or if you have negotiated enterprise rates. Treat the numbers as directional estimates, not invoices.

Can I use this to check if my prompt fits in a context window?

Yes. Each model family has a published context-window limit measured in tokens — 128k for GPT-4o, for example. Paste your full prompt (system message + user message + expected output buffer) and compare the total against the model's limit. If you need to manipulate or encode the text first, the [URL encoder & decoder](/en/url-encoder/) is available in the same browser tab.

Does it handle code and non-English text?

Yes. Code snippets, JSON, Markdown, and Unicode text (including CJK characters and emoji) are all supported. Non-ASCII input is especially worth checking because tokenizers often assign more tokens per character to scripts outside Latin alphabets, which can significantly affect cost estimates for multilingual applications.