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The same text can have different token counts in ChatGPT, Claude, Gemini, and standalone tokenizer websites because token boundaries depend on the target model’s vocabulary—and because an API may count more than the text you pasted. To get a useful number, count with the exact model and request format, then compare the API’s usage fields after the call.
What a token count actually measures
A token is a piece chosen from a model’s vocabulary, not a fixed unit such as a word or character. It may be a character, part of a word, a whole word, punctuation, or another piece of text. Token IDs and boundaries belong to an encoding; they are not universal across models. OpenAI notes that model, encoding, and language can all change a text’s count (OpenAI Help Center).
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For example, a familiar word may be represented as one token by one tokenizer and several pieces by another. Even small text changes—such as capitalization, a leading space, or spelling—can alter segmentation. The strings red, Red, and red therefore need not have the same count.
Why the same text gets different counts
Each model may use a different tokenizer
There is no single cross-provider token count. A count from a tokenizer associated with one model is not authoritative for another provider’s model. OpenAI recommends choosing the encoding for the intended model when using its tiktoken library. Anthropic-maintained guidance likewise directs developers to count with the Claude model ID they intend to use (Anthropic-maintained Claude API guide).
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Language and text form affect segmentation
Tokenizers do not necessarily represent every language equally compactly. A 2023 NeurIPS paper, Language Model Tokenizers Introduce Unfairness Between Languages, reported that the GPT-era tokenizer setup it evaluated used about 1.6 times as many tokens for the same Italian text as English, 2.6 times for Bulgarian, 3 times for Arabic, and up to 15 times for Shan. Those are measurements from that paper’s historical setup—not conversion factors for current ChatGPT, Claude, or Gemini models. The paper analyzed 2,000 human-translated Wikipedia sentences across 200 languages and discussed potential effects on cost, latency, and the amount of content that fits in a fixed context (NeurIPS 2023 paper).
A website and an API may count different inputs
A standalone tokenizer typically counts the string pasted into it. An API processes structured messages and may also account for roles, message boundaries, tool definitions, schemas, images, files, or other modalities. OpenAI’s input-token counting endpoint accepts the same kinds of input as its Responses API and includes formatting tokens for request structure (OpenAI token-counting guide). A text-only website cannot provide a like-for-like total if it never received those other inputs.
Gemini also tokenizes non-text modalities, including images, and exposes usage categories beyond a simple text count. Its metadata can distinguish input, output, thought, cached-content, tool-use, and total tokens (Google AI for Developers: Tokens).
Reported output can include non-visible structure
The number of output tokens reported by a platform need not match a count of the displayed answer alone. OpenAI documents that some model responses use tokens for channels, tool calls, and message structure that are not shown as ordinary visible text or in log probabilities. The amount depends on the model and response shape; there is no fixed adjustment from visible words to reported output tokens (OpenAI token-counting guide).
How to count tokens accurately
- For a plain-text estimate, select the target model’s tokenizer. Use the encoding associated with the exact model where available. Do not treat another provider’s tokenizer as a universal counter.
- For a request estimate, count the actual request. Use the provider’s count-tokens endpoint or equivalent with the same messages and supported tools, schemas, images, or files you plan to send. OpenAI says its Responses input-token endpoint accepts the same input format as a real Responses request; Gemini provides
count_tokensfor the intended model and input (OpenAI token-counting guide; Google AI for Developers: Tokens). - After the call, inspect actual usage metadata. Compare input with input and output with output. Keep categories such as cached tokens, reasoning or thought tokens, and tool-use tokens distinct rather than comparing a pasted-text count with an all-in total.
- For capacity or cost planning, check the target model’s current limits and pricing. Tokenization and generated output vary by model and request, and providers may treat usage categories differently. OpenAI’s own guidance cautions that estimates are not exact (OpenAI Help Center).
Why rough word and character conversions are unreliable
Rules such as “about four characters per token” or “about three-quarters of a word per token” are planning heuristics, not counting methods. OpenAI presents these as approximate guidance for English and notes that language and sentence or paragraph variation affect the result. Google’s Gemini guide also gives an approximate four-characters-per-token figure and a range of 60–80 English words per 100 tokens. Neither provider’s approximation guarantees the count for a particular prompt, language, model, or multimodal request (OpenAI Help Center; Google AI for Developers: Tokens).
Quick Recap
Compare like with like when counts disagree
| What to check | Compare |
|---|---|
| Model and encoding | Are both counts tied to the same target model and tokenizer? |
| Input scope | Does one number count pasted text while the other includes roles, boundaries, tools, or schemas? |
| Modality | Does the request include images, audio, video, or files that the text-only counter did not receive? |
| Usage category | Are you comparing input with input, output with output, and keeping cached, reasoning/thought, and tool-use counts separate? |
| Visible text versus generated structure | Does the platform count non-visible formatting or tool-call tokens? |
| Text itself | Are language, spaces, capitalization, punctuation, and code identical? |
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