Approximately how many AI Tokens will be used to write a 1,000-word article?

If you are looking up "approximately how many AI Tokens will be used in a 1,000-word article", usually what you really want to know is not an abstract definition, but a very practical thing: when I want to write an article, ask AI to produce an article, or estimate the API cost of an article, how many tokens should I grab?

Let’s talk about the most direct answer first:

If you are talking about an article with 1,000 Chinese characters, in practice you can usually grab about 800 to 1,200 Tokens first. If you are talking about 1,000 English words, you can usually grab about 1,300 to 1,400 Tokens first.

But this is not a fixed formula, but a practical estimation interval. Because OpenAI officials clearly stated that Token is not directly equal to the number of words, and non-English texts usually have a higher token-to-character ratio; Gemini officials also said that the Gemini model uses Token as the processing granularity, and the fact that 1 Token is approximately equal to 4 characters is essentially only a rough approximation and cannot be directly applied to every language.

So this article is not to lead you to memorize the formula, but to directly help you answer the question at the top of the search intention: for a 1,000-word article, how many Tokens should be captured before the estimate is too outrageous?

Let’s talk about the most important difference first: the 1000 words you said are 1000 Chinese words or 1000 English words?

This is the first thing to make clear in this article.

Because when many people see "1000 words", they will intuitively mix Chinese and English together, but in fact, the Tokens in these two situations cannot be directly viewed with the same set of ratios. OpenAI officially provides common experience values for English: 1 Token is approximately equal to 4 characters, approximately equal to 3/4 English words, 100 Tokens is approximately equal to 75 English words, and approximately 1,500 English words are approximately 2,048 Tokens.

If you are talking about 1000 Chinese characters

The "1000 characters" here are more suitable to be understood as 1000 Chinese characters. In this case, it is usually more practical to grab 800 to 1,200 Tokens first. This is not the official Chinese formula given verbatim, but a conservative estimate based on OpenAI's clear reminder that "non-English languages usually have a higher ratio of tokens to characters" and combined with the fact that CJK language segmentation is usually denser than English.

If you are talking about 1,000 English words

If you are talking about 1,000 English words, using OpenAI’s official experience value conversion, it will usually fall around 1,300 to 1,400 Tokens. Because 100 Tokens is equal to approximately 75 English words, a rough estimate of 1000 words is around 1333 Tokens.

Why is 1000 words in Chinese usually not as economical as in English?

Because the AI model does not directly use the "word count" to view the content, but first cuts the text into Tokens. OpenAI officials make it very clear that tokens can be as short as a single character or as long as a complete word. Spaces, punctuation, and some words will affect the number of tokens, and non-English texts usually have a higher token-to-character ratio.

Gemini officials also stated that Gemini and other generative AI models process input and output at the granularity of Token, and all input and output will be tokenized, including text and non-text modalities.

Chinese is closer to "one word is close to the magnitude of one Token"

This does not mean that every Chinese character must be equal to a Token, but it means that you cannot use the English idea of "4 characters are worth 1 Token" to evaluate Chinese. Chinese content is usually closer to the sense of magnitude of "the number of tokens will not be too far from the number of words".

So for 1,000 Chinese words, start with 800 to 1,200. It is less likely to underestimate

The advantage of this range is that it is practical enough. It is not used as an actuarial quotation, but to prevent you from overthinking the Chinese content in the first place.

If you are asking "Ask AI to help me write a 1000-word article", then you can't just look at the article body

This is another very important point.

Because what the API really calculates is usually not just the last article, but:

Input Tokens + Output Tokens

OpenAI officially divides usage into categories such as input tokens, output tokens, cached tokens, and reasoning tokens; Gemini officials also say that you can see usage information such as prompt token count and candidates token count in the usage_metadata of the response.

If you are generating a 1000-word Chinese article

The most common situation is usually like this:

Input: dozens to hundreds of Tokens

Output: about 800 to 1,200 Tokens

Total amount: about 900 to 1,500 Tokens

The point here is not to count you to single digits, but to establish the correct concept first: the real big head is usually outputting, not just inputting.

Why many people underestimate the total Token

Because everyone only looks at the length of the final article, but forgets the theme, tone, word count requirements, format requirements, SEO rules, and sample paragraphs you gave the AI, all of which will be included in the input. As long as prompt becomes longer, the total amount will naturally increase.

What situations would make the Token higher for the same 1,000-word article?

It’s the same 1000-word article, but the Token may still be much different. This is why you can’t just memorize fixed formulas.

The first type: there are many titles, columns, numbers, and URLs in the article

Because Token does not just look at the "amount of text", spaces, punctuation, some words, and symbols will all affect the number of Tokens. OpenAI officials are very direct on this point.

The second type: a lot of Chinese and English mixed

If the article contains English terms, numbers, brand names, abbreviations, and product codes, the token segmentation method is usually less intuitive than pure Chinese.

The third type: the content is JSON, tables, codes or special formats

Gemini officials also make it clear that all input and output will be tokenized, including non-plain text content. This means that the format itself may also cause Token to rise.

Fourth way: You put a long background information in the prompt

For many people, it's not that the article itself is fat, but that the prompt itself is fat. Like brand specifications, SEO structure, sample paragraphs, reference articles, format requirements, as these become longer, the input will go up first.

What is the most accurate algorithm? Don’t guess, count first

If you really want to estimate costs, quote customers, and control the length of articles, the best way is not to just memorize “How many tokens are there in 1,000 words”, but to count them directly with your actual content.

OpenAI officially provides the Tokenizer tool, which allows you to directly see how text is cut into Tokens; Gemini also provides the count_tokens method, which allows you to count the number of input tokens before sending them; Anthropic also provides the official token counting file, which explains how to estimate the number of tokens first.

The most stable way to estimate costs

First use the range of this article to grasp the general direction, and then use the actual content to run tokenizer or count_tokens.

This is more suitable for going online than memorizing formulas

Because what you really want to control is the actual request, not the word count of the abstract article.

Remember one sentence first: The token of a 1000-word article is usually not the word count of the article itself, but the total amount of the entire request

This sentence is worth writing down first.

If you are simply asking "How many tokens are there for a 1,000-word article?", then grabbing 800 to 1,200 for Chinese and 1,300 to 1,400 for 1,000 words in English are usually enough for you to make the first level of judgment.

But if you are asking "How much will be deducted if the API generates a 1000-word article at a time?", then what you need to look at is not the article itself, but:

How long is the Prompt

Are there any additional format requirements

Looking at it this way is closer to the real bill.

The 5 most common mistakes for newbies

First, thinking that 1000 words must be equal to 1000 Tokens

Not necessarily. Chinese sometimes approaches this level, but this is not a fixed formula. English, mixed languages, and formatted content will all change.

Second, I think English and Chinese can use the same algorithm

No. OpenAI officials have made it clear that non-English languages usually have a higher ratio of tokens to characters.

Third, thinking that just reading the article itself is enough

If you are estimating the cost of API requests, you must also include prompts.

Fourth, I think input is more important

The real bulk of many article generation tasks is the output, because the article spit out by the model is usually longer than the prompt. OpenAI's official usage category itself tracks input and output separately.

Fifth, I think that as long as I know the approximate proportion, I don’t need tools.

If you really want to go online, quote, and control costs, it is recommended to run it directly with tokenizer or count_tokens first.

Conclusion: How many AI Tokens are there for a 1,000-word article? Capture the interval first, and then use the tool to confirm

If you want the simplest version, I will collect it again for you:

If you are talking about 1,000 Chinese characters, you can first capture about 800 to 1,200 Tokens. If you are talking about 1,000 English words, you can first grab about 1,300 to 1,400 Tokens.

But when you really want to look at the API cost, it is best to look at:

separately, and finally use the tokenizer or count_tokens tool to measure it. This is the least error-prone and most consistent with how official documents are used.

FAQ: The 3 most frequently searched questions

Is a 1,000-word Chinese article necessarily worth 1,000 Tokens?

Not necessarily, but often close to this magnitude. Because OpenAI has reminded that non-English languages usually have a higher ratio of tokens to characters, so Chinese cannot be forced to use the English 4 characters = 1 Token.

Why did someone calculate that there are only a few hundred Tokens?

Usually it's because he uses a rough formula in English. But that formula is easier to use in English, but it is easy to underestimate it in Chinese.

I want to estimate the cost of article generation, which number should I look at?

Look at the output Tokens first, then the input Tokens. Because the real bulk of article generation tasks is usually output, and OpenAI and Gemini will count input / output separately.

Data source and credibility statement

This article is compiled and written based on the official token files of OpenAI, Google Gemini and Anthropic, focusing on the OpenAI Token description, OpenAI Tokenizer, Gemini Token file and Claude Token Counting. The content is organized using a three-level approach of "Official Token Definition × Chinese-English Conversion Difference × Practical Estimation Range". The purpose is to help readers first obtain an operable estimate range when querying the Token usage of a 1,000-word article, and then further use tools to confirm the actual value.

If you want to understand the front and back concepts together, it is recommended to continue reading from AI Token.

This article belongs to the category "AI Token Calculation"

This category mainly organizes the basic conversion of AI Token, the difference between word count and token, cost estimation, background digital interpretation and the most common calculation problems encountered by novices. It helps readers understand "how to look at numbers" first, and then make further cost and model judgments.

How many words is one AI Token? There is actually a lot of difference between Chinese and English

How to calculate the AI Token conversion? Don’t rush to just look at the number of words

How to check the usage of AI Token? Which backend number is most important

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Token conversion
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AI Token organizes the basic concepts, calculation methods, API fees and model comparisons of AI Token (word elements), and covers common models such as ChatGPT, Gemini, Claude, etc. to help you establish clear understanding and judgment faster.

Approximately how many AI Tokens will be used to write a 1,000-word article?