Install it and Trazum counts OpenAI prompts exactly instead of estimating them. Do not install it and nothing changes: @trazum/core declares no dependencies, and every figure goes on saying which counter produced it.

npm install @trazum/tokenizer-openai

Why it is a separate package

The rank tables are twenty-two megabytes. The zero-dependency promise in @trazum/core is load-bearing for the CI case this product is built around -- a gate that pulls a twenty-megabyte table into every build is a gate teams turn off -- so the only shape that improves the absolute figures without spending that promise is a package somebody installs on purpose.

Nothing in @trazum/core imports this. The counter is passed in through the TokenCounter seam that has been there since the beginning.

What it is worth

The heuristic in @trazum/core is published under a ±33% band measured against Anthropic's counter, and against OpenAI's it has been measured at 112.4% out at worst -- more than twice, on German prose. The estimator is not broken; it is calibrated for a tokenizer that packs Latin text far less densely than o200k_base does. This package removes the question.

Against the 47-sample corpus in this repository, measured through OpenAI's own API with a real key, this package reproduces every count exactly: 47 of 47, to the token.

What it refuses

A model it does not have the encoding for. Not a nearby encoding, not the newest one, not a default. gpt-5-codex and any model shipped since the rank tables were written are refused, because a count produced by guessing which table to use would go out labelled exact, and exact is the strongest word this tool uses about a number.

To claim it knows whose a model is. A Claude model refuses here as unknown-encoding rather than as wrong family. This package holds encodings, not a catalogue.

What the number is

The token count of the text. It is not the token count of a chat request: an API call wraps each message in role markers and primes the reply, and those tokens belong to the envelope rather than the prompt.

Usage

import { openaiCounter } from '@trazum/tokenizer-openai';

const counter = openaiCounter('gpt-5');
if (counter.ok) {
  console.log(counter.encoding);        // 'o200k_base'
  console.log(counter.count('hello'));  // 1
} else {
  console.log(counter.refusal.reason);  // 'unknown-encoding'
}

canCount(model) answers the same question without parsing a two-hundred-thousand-rank table, for a caller deciding what to say before it decides what to do.