ttok version 0.4

Meta description: Ttok 0.4 updates the OpenAI tiktoken CLI, fixes a Click warning, refreshes CI, and adds --list-models for counting tokens from files or uvx pipelines.

Tags: ttok, tokenization, OpenAI, generative-ai, llms

Featured image: abstract, friendly illustration style, no real people, no logos, no text. Shows a command-line terminal connected to a stream of word fragments and a small model-list icon.

Ttok 0.4 is a refreshed command-line token counter for developers, technical writers, and AI application builders who need a quick way to estimate how text maps into model inputs. The release updates ttok, a tool built on OpenAI's open source tiktoken library, after a gap of a couple of years, fixes a Click warning, updates the project's CI pipeline, and adds a --list-models command for showing available models.

What Ttok Does and Why Tokens Matter

Ttok takes text and counts the tokens that an AI model's tokenizer would use. A token is a piece of text: a word, part of a word, punctuation mark, or space, depending on the tokenizer. This matters because large language models accept and produce text in token-sized units. Developers often need to know how long a prompt, article, log, or dataset is before sending it to a model, especially when working with context limits or comparing input sizes across tasks.

The tool is useful in a terminal because it lets users count tokens without opening a notebook or building a separate script. A file can be piped into ttok, and the command returns a count. That makes it suitable for quick checks during writing, prompt development, or data preparation.

What Changed in Ttok 0.4

The release describes a maintenance update with one new user-facing feature. The first change is a fix for a warning from Click, a Python library for building command-line interfaces. Such warnings do not always break functionality, but they can create noisy output and make users unsure whether a command is working correctly.

The second change is CI. In software projects, CI means automated checks that run when code changes are pushed, such as tests and builds. Updating CI helps keep the project testable and maintainable as Python versions and dependencies move forward.

The third and most visible change is --list-models. This command lets users ask ttok to show available models instead of relying on memory or external documentation. That can be helpful when trying to match a token counter to a specific tokenizer or model family, though the update does not list every model or explain how the list is generated.

How to Use It With uvx

The release highlights that ttok works with uvx, a command runner for Python tools. In practice, uvx can run Python applications without requiring the user to create a separate virtual environment first.

For a file, the example given is:

cat file.txt | uvx ttok

This pipes the contents of file.txt into ttok through uvx. The uvx form can reduce setup for users who do not want to install the tool first. For people who only need token counts occasionally, that is one of the most practical parts of the release: the tool remains a small command, but it can be used with less ceremony.

What It Means for AI Workflows

A token counter may look like a minor utility, but it sits close to several everyday AI development tasks. Prompt engineers may use one to check whether a long instruction still leaves room for examples. Technical writers may use it to understand how much of a page will fit inside a model's context window. Data builders may use it to compare sample sizes before feeding text into an evaluation set.

The addition of --list-models is modest, but it reduces friction. If a user can list models from the tool itself, they have a faster way to discover which models are available and whether ttok is likely to produce counts relevant to their target model. The update also matters for reliability: a quiet warning and a refreshed CI pipeline do not change the main feature, but they signal that the project is being maintained.

For readers who already use OpenAI's tiktoken, ttok 0.4 is not a new tokenizer or a new model. It is a command-line tool that became easier to run and slightly more self-explanatory. The practical takeaway is simple: if you need quick token counts from the terminal, this release is a useful refresh; if you need a full application integration, you will still choose between ttok for shell checks and tiktoken for code-level control.

Who Should Use It and Who Should Skip It

People who work at the command line will get the most value: writers checking prompt length, developers debugging context limits, and researchers estimating dataset size. Users who need deep integration inside a Python application may still work directly with OpenAI's tiktoken, because ttok is designed for terminal checks, not for embedding tokenization logic inside a larger program.