How to estimate AI Token usage? Newbies should first learn to grasp the approximate range.

How to estimate the usage of AI Token is one of the first problems that many people get stuck on when they first come into contact with AI API. It’s not because they haven’t heard of Token at all, but because most people know that Token is related to API costs, but they still don’t know how to get the approximate usage themselves. Questions such as how many tokens are counted for the same sentence, whether Chinese is more popular than English, whether system prompts are counted, whether pictures and files are counted together, and why the numbers for the same content are different on different platforms are all common.

OpenAI, Google Gemini, and Anthropic all regard Token as the basic unit of model processing content, and they all provide Token Counting or usage-related capabilities, but each company is not exactly the same in terms of tokenizer, pricing fields, and estimation methods.

Let’s talk about the most practical conclusion first: If you want to quickly grasp the approximate dosage, the most useful way is not to memorize the formula by heart, but to understand 3 things first: how big is your input, how long is the output usually, and how many times will it be sent every day. As long as you figure out these three numbers, you can usually estimate a monthly usage range that is very close to the actual usage. This direction is also the core of your original manuscript that is most worth retaining.

Let’s understand the most basic definition first: What is AI Token?

Token is the basic unit for model processing content. OpenAI officials said that Tokens may be as short as one character or as long as a complete word. Spaces, punctuation and some words will affect the number; Gemini officials also said that Tokens can be a single character or a complete word. Long words are usually split into multiple Tokens. This means that the model does not work directly with the "word count", but first cuts the content into Tokens and then processes them.

If you just want to get a feel first, the English experience value given by OpenAI is very practical: 1 Token is approximately equal to 4 English characters, approximately equal to 3/4 English words, and 100 Tokens is approximately 75 English words. However, OpenAI also specifically reminds that non-English texts usually have a higher token-to-character ratio, so content such as Chinese, Japanese, and Korean cannot be calculated directly based on rough estimates in English.

The first thing you should know first: How to roughly estimate the approximate number of Tokens in a piece of content

If you just want to grasp the "approximate", it is enough to use this simple logic first:

For English content, you can first use "4 characters is about 1 Token" to make a rough estimate

Don't rigidly follow the English rules for Chinese content, be more conservative and it will be more accurate

When you really want to estimate the cost, it is best to use the official Token Counting tool to confirm

Anthropic official Token counting The document is very clear: you can know the approximate number of input tokens before sending a message to help you manage rate limits, costs and prompt length, but it also reminds you that this is an estimate and is not guaranteed to be exactly the same as when the message is actually created. Gemini also directly provides official capability descriptions of count tokens.

What problems can a rough estimate solve?

The rough estimate of the maximum value does not allow you to be accurate to single digits, but lets you know first whether the content falls into:

Hundreds of tokens

Thousands of tokens

Or tens of thousands of tokens

For novices, this is enough for the first round of budgeting.

The second thing you must know: Input and Output must be looked at separately

The most common mistake many novices make when calculating usage is to treat all Tokens as the same package. In fact, almost all mainstream platforms now treat input and output separately. OpenAI official instructions directly divide token usage into input tokens, output tokens, cached tokens, and reasoning tokens; Gemini officials also say that after enabling billing, the cost depends in part on the number of input and output tokens; Anthropic's pricing page clearly separates Base Input Tokens and Output Tokens.

In practice, Input is what you send in, and Output is what the model returns to you. When you want to estimate the approximate usage, don’t look at the total number first, but first ask:

How much content do I usually send in at one time?

How long does it usually take for a model to reply to me?

Because many times, what really drives up the usage and cost is not that you ask too many questions, but that the model responds too much. This is obvious from the fact that each company lists the output in a separate column.

The third thing to understand: What content is actually included in Input

Many people think that Input is the sentence they type to the model. Not really. Anthropic's Token counting document clearly states that it accepts the same structured input as when creating messages, including system prompts, tools, images, and PDFs; OpenAI also describes cached tokens as reused content that may come from conversation history. This means that the input actually sent to the model is often not just a prompt.

In other words, these things may usually be counted into the Input:

system prompt

So if you think you have only asked a short question, but find that the Input is very large, it is usually not the platform that calculates it randomly, but that the request actually contains a lot of background that you did not notice. This is why when estimating usage in actual practice, you cannot just look at the most superficial prompt.

The fourth thing to understand: the same piece of content may have different Tokens on different platforms

This is very important. OpenAI officials make it very clear that Tokenization will vary by language; Anthropic directly reminds that token count is an estimate and may be slightly different from when the message is actually created. This means that even if the content is the same, the number of Tokens on different platforms and different models may not be exactly the same.

So if you are a novice, the least likely way to make mistakes is not to invent a set of universal conversion formulas yourself, but to use rough estimates only as directions; when you really want to compare platforms or budget API costs, use official tools to count each one, which is the most stable.

A truly practical lazy algorithm: capture the approximate monthly usage by yourself

If you just want to capture the "approximate monthly usage", you can directly use this sequence:

First capture the average Input Token
Then capture the average Output Token
Finally multiply by the number of requests per day, then multiply by the number of days per month

Total monthly Token ≈ (average Input + average Output) × Number of requests per day × Number of days per month

This algorithm is not an official formula given to you verbatim, but it is directly based on the premise that each official regards input / output as the basic billing and usage structure, so it is very suitable for the first round of estimation.

The simplest example

If you send an average of 1,000 input tokens each time, the model returns an average of 500 output tokens, and it is used about 100 times a day, that is 150,000 tokens a day; a 30-day month is about 4,500,000 tokens.

Then you can compare the input/output unit prices of the model and get an approximate monthly cost. This estimation method is very suitable for making a budget before officially connecting to the API.

If you don’t know how to capture the average Input / Output, you can do this

The most stable method is actually very simple:

First take your 5 to 10 most common requests in the future

Run official token counting or actual requests

Then grab a rough average of the results

Anthropic’s Token counting is designed for this kind of thing. The point is to let you know how big the input token will be before actually sending the request; OpenAI official instructions also encourage the use of tokenizer and The tiktoken tool explores tokenization.

If you are an individual user, it is even enough to capture three scenarios:

As long as you capture the approximate Input/Output of these three types first, you will understand how much you will probably spend faster than most people who only look at the price page. This is practical advice based on official counting capabilities.

Why do many people underestimate the dosage?

There are 4 most common reasons.

First, only user issues are counted, system prompts and historical conversations are not counted

This is the most common source of underestimation. The longer the dialogue and the more background it has, the fatter the input will be.

Second, only look at Input, not Output

But output on most platforms is priced independently, and is usually more expensive. OpenAI, Anthropic, and Gemini all clearly list output separately.

Third, ignoring the repeated background can actually be cached, or conversely, it is obviously sending repeatedly but not counting that it will keep eating input

OpenAI's prompt caching document points out that prompt caching can reduce latency and reduce the input token cost to up to 90%; Anthropic's pricing page also clearly lists cache write and cache hits separately.

Fourth, use the experience value of one platform to cover everything

However, the tokenization and estimation methods of different platforms and different models are not exactly the same.

If you want to get closer to the true cost, what two more things should you look at?

The first thing is Cache

If your process has fixed system prompts, brand specifications, long background knowledge, and multiple rounds of dialogue prefixes, then caching will directly affect your real costs. OpenAI's Prompt Caching document states that caching can reduce input costs to up to 10% of the original cost; Anthropic's pricing also clearly states that the rates for cache hits & refreshes are significantly lower than base input.

The second thing is Batch

If your task is not immediate customer service, but a large number of summarization, classification, and sorting that can be deferred, then Batch will usually significantly change the cost structure. Anthropic’s official pricing clearly states that the Batch API offers a 50% discount on both input and output.

The most valuable thing for newbies to learn first is not to be accurate to the decimal point, but to quickly estimate the level gap

If your current goal is just "I want to know first whether I am hundreds of thousands of Tokens, millions of Tokens, or tens of millions of Tokens per month", then you don't need to be actuarial at the financial level from the beginning.

As long as you can clearly distinguish:

I am a long background knowledge type

I am a high-frequency automation type

Then use the average input, average output, and number of requests to estimate, it is enough. This estimation method is not perfect, but it is very suitable for getting started, and it is directly based on the official token and billing logic. This is also the most practical value of your original draft that is worth retaining.

How to estimate AI Token usage? The most practical way for lazy people is not to memorize a bunch of conversion formulas, but to first capture the average input, average output, and number of requests, and then use the official Token Counting to correct it. As long as you can understand the three layers of input / output / cache first, you can probably figure out the usage. Later, you will have to read the price list, estimate the cost, and compare the platforms, and it will be much smoother.

FAQ

Can AI Token be directly converted by word count?

It can be roughly estimated, but it cannot be directly equalized. Both OpenAI and Gemini give approximate empirical values for English, but non-English content is often more prone to bias.

Why does Chinese often use more tokens than English?

Because OpenAI officials pointed out that non-English texts usually have a higher ratio of tokens to characters, Chinese cannot directly apply the English estimation method.

Is it enough to just look at the Input?

Not enough. Most platforms price output separately, and output is often more expensive, so be sure to look at input and output separately.

Does cache have to be counted?

It is recommended if your process has a lot of repeated background. Because cache will directly affect the real input cost.

The same piece of content, why are the Tokens different on different platforms?

Because tokenizer and model encoding may be different. The official documentation from OpenAI and Anthropic clearly mention this.

What is the most stable way for a novice to estimate monthly usage?

First capture the average Input, average Output, and number of requests per day, and then multiply them by the number of days per month; then use official token counting to correct them. This is the most suitable estimation method for beginners.

Data source and credibility statement

This article is written based on the official Token, Billing, Pricing and Token Counting documents, focusing on official sources such as OpenAI: What are Tokens and how to calculate, OpenAI: Prompt Caching, Anthropic: Token counting. The content is organized in a three-layered manner of "Official Rules × Usage Estimation × Beginner's Practice". The purpose is to help readers figure out the approximate dosage that is accurate enough, rather than being scared off by complicated pricing at the beginning.

If you want to return to the main page of AI Token calculation first, you can read this article first: How to calculate AI Token? Newbies understand the most basic calculation methods

If you want to start from the homepage of the entire AI Token × API × Model Cost Teaching Site, you can also go back here: AI Token

This article belongs to the "AI Token Calculation" category

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

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How to estimate AI Token usage? Newbies should first learn to grasp the approximate range.