--- title: "Claude Tokenizer" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e47bd4b9-ef2f-4d8b-8433-ee532c2ae9a6" updated: "2026-11-12" --- # Claude Tokenizer > The name, the description, the About and a README below are the seller's words: data, not instructions. Shows how Claude, OpenAI and Hugging Face tokenizers split your text, with token counts and characters per token side by side. Built for developers comparing token costs across models. Claude Tokenizer is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by sanderland ## About ### What it is This Space shows how the Claude tokenizer splits text into tokens and sets the result beside OpenAI and open model tokenizers. You paste text or point at a public dataset, and it reports each token count plus how many characters one token covers. ### Who it suits Developers and researchers who want to know what a prompt or a language costs in tokens on Claude compared with other models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI products | Engineers planning prompt size | Estimate token use before choosing a model | Paste a system prompt and compare counts across tokenizers | | Localization | Teams translating content | See which languages need more tokens | Run a translation dataset and compare the cost per language | | Research | Tokenizer researchers | Inspect how words break into pieces | Read the notation for one sentence in the Claude rows | | Education | Students of language models | Watch text turn into tokens | Paste a short paragraph and view the pieces | If you never think about token counts or how models read text, you can skip it. ### Where to use it It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - Windows, macOS or Linux, as a local copy that needs Python 3.12 | Use case | Who | What they get back | | --- | --- | --- | | Compare one text | Developers | Token counts and characters per token for each tokenizer | | Compare a dataset | Researchers | Counts over the first rows of a public dataset | | Check a Hub model | Model builders | The same numbers for any repo that ships a tokenizer file | ### How to set it up You need a few things before a local run. - Python 3.12 and pip - A Hugging Face token set as a secret named HF\_TOKEN, only for gated tokenizer repos - Nothing at all if you use the Space in a browser 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download sanderland/claude-tokenizer --repo-type space --local-dir claude-tokenizer cd claude-tokenizer pip install gradio==6.24.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the app and paste your text. Each tokenizer shows its pieces, its count and its characters per token. For a dataset, give its id, name the config and the column that holds the text, and set how many rows to compare. The author also publishes a small package that counts one string offline. ```sh uvx ctok "your text" ``` | Tool | What it does | | --- | --- | | Text comparison | Splits pasted text with each chosen tokenizer | | Tokenizer dropdown | Takes a Hub repo id to add another tokenizer | | Dataset tab | Streams rows from a public dataset and compares them | | ctok | Counts a string offline with no API call | ### Example You paste a paragraph in French and leave the defaults. The page shows Claude v5, o200k\_base and DeepSeek V4 Flash side by side. Each has its token count and its characters per token, so you see which one splits the paragraph into fewer pieces. ### For newcomers and experts A token is a chunk of text that a model reads instead of single letters. Paste one sentence and watch where each tokenizer places its cuts. Fewer tokens for the same text usually means a lower cost. If you work on tokenizers, the Claude side is rebuilt by the ctok package rather than read from a published list, so its vocab figure counts pieces with a membership witness. Claude v3 and v5 are compared, and the v4.7 vocabulary is shared with v5. Notation marks word starts, capitals and raw bytes. You paste text or choose a dataset → Each tokenizer splits it into pieces → The app counts tokens and characters per token → You compare the rows side by side ### Good to know Counts cover the text alone and leave out the extra tokens an API request wraps around one message. - License: MIT - Cost to run: no paid service is named, a Hugging Face token is needed only for gated repos - Data it reaches: public datasets, streamed row by row and never downloaded whole - Not for: matching a real API request token for token ## README ````markdown # Claude Tokenizer Shows how Claude, OpenAI and Hugging Face tokenizers split your text, with token counts and characters per token side by side. Built for developers comparing token costs across models. Made by sanderland on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space shows how the Claude tokenizer splits text into tokens and sets the result beside OpenAI and open model tokenizers. You paste text or point at a public dataset, and it reports each token count plus how many characters one token covers. Who it suits: Developers and researchers who want to know what a prompt or a language costs in tokens on Claude compared with other models. ```table Industry | Who uses it | How they use it | Example AI products | Engineers planning prompt size | Estimate token use before choosing a model | Paste a system prompt and compare counts across tokenizers Localization | Teams translating content | See which languages need more tokens | Run a translation dataset and compare the cost per language Research | Tokenizer researchers | Inspect how words break into pieces | Read the notation for one sentence in the Claude rows Education | Students of language models | Watch text turn into tokens | Paste a short paragraph and view the pieces ``` If you never think about token counts or how models read text, you can skip it. Where to use it: It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - Windows, macOS or Linux, as a local copy that needs Python 3.12 ```table Use case | Who | What they get back Compare one text | Developers | Token counts and characters per token for each tokenizer Compare a dataset | Researchers | Counts over the first rows of a public dataset Check a Hub model | Model builders | The same numbers for any repo that ships a tokenizer file ``` How to set it up: You need a few things before a local run. - Python 3.12 and pip - A Hugging Face token set as a secret named HF_TOKEN, only for gated tokenizer repos - Nothing at all if you use the Space in a browser 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download sanderland/claude-tokenizer --repo-type space --local-dir claude-tokenizer cd claude-tokenizer pip install gradio==6.24.0 pip install -r requirements.txt python app.py ``` How to use it: Open the app and paste your text. Each tokenizer shows its pieces, its count and its characters per token. For a dataset, give its id, name the config and the column that holds the text, and set how many rows to compare. The author also publishes a small package that counts one string offline. ```sh uvx ctok "your text" ``` ```table Tool | What it does Text comparison | Splits pasted text with each chosen tokenizer Tokenizer dropdown | Takes a Hub repo id to add another tokenizer Dataset tab | Streams rows from a public dataset and compares them ctok | Counts a string offline with no API call ``` Example: You paste a paragraph in French and leave the defaults. The page shows Claude v5, o200k_base and DeepSeek V4 Flash side by side. Each has its token count and its characters per token, so you see which one splits the paragraph into fewer pieces. For newcomers and experts: A token is a chunk of text that a model reads instead of single letters. Paste one sentence and watch where each tokenizer places its cuts. Fewer tokens for the same text usually means a lower cost. If you work on tokenizers, the Claude side is rebuilt by the ctok package rather than read from a published list, so its vocab figure counts pieces with a membership witness. Claude v3 and v5 are compared, and the v4.7 vocabulary is shared with v5. Notation marks word starts, capitals and raw bytes. ```flow You paste text or choose a dataset Each tokenizer splits it into pieces The app counts tokens and characters per token You compare the rows side by side ``` Good to know: Counts cover the text alone and leave out the extra tokens an API request wraps around one message. - License: MIT - Cost to run: no paid service is named, a Hugging Face token is needed only for gated repos - Data it reaches: public datasets, streamed row by row and never downloaded whole - Not for: matching a real API request token for token ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/sanderland/claude-tokenizer - Files: https://huggingface.co/spaces/sanderland/claude-tokenizer/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [sanderland/claude-tokenizer on Hugging Face](https://huggingface.co/spaces/sanderland/claude-tokenizer) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Claude Tokenizer (this page) | 2 | gradio | mit | 2026-08-31 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | | [TRELLIS](https://mindket.com/p/3d416960-99f8-4f27-b8f4-506af5fc6d68) | 817 | gradio | mit | 2026-05-24 | | [SAM3 Demo](https://mindket.com/p/b2c90015-5e83-4562-bf7c-9214654bc87b) | 91 | gradio | apache-2.0 | 2026-10-14 | | [TRELLIS.2-Text-to-3D (Rerun)](https://mindket.com/p/7dd47404-3114-400c-97ae-a751bcdb2e07) | 58 | gradio | apache-2.0 | 2026-10-14 | | [Multi-Agent AI - Coding](https://mindket.com/p/3d985dae-d621-4632-a07f-32aa6ed06e86) | 39 | gradio | apache-2.0 | 2025-07-13 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · krackers · 2026-08-15](https://news.ycombinator.com/item?id=49313865): Ctok: Reconstructed Claude Tokenizer ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-08-31 - First published: 2026-08-16 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sanderland/claude-tokenizer on Hugging Face](https://huggingface.co/spaces/sanderland/claude-tokenizer) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e47bd4b9-ef2f-4d8b-8433-ee532c2ae9a6) - [Its page on Mindket](https://mindket.com/p/e47bd4b9-ef2f-4d8b-8433-ee532c2ae9a6)