How to check the usage of AI Token? Novices can understand the numbers in the background without confusion

When you start using ChatGPT, Claude, Gemini or other AI APIs, you will soon see a bunch of numbers in the background: input tokens, output tokens, total tokens, usage, limit.

When many novices see these fields for the first time, the biggest problem is not that they don’t know how to use them, but that they don’t know what they are looking at. OpenAI officials also clearly stated that the API will return usage information such as input tokens, output tokens, cached tokens, etc. These numbers will be used for billing and usage tracking.

This article does not focus on what AI Token is, nor how to calculate AI Token, but directly deals with a more practical problem: How to measure AI Token usage? Only after you understand the backend numbers will you know where the costs are spent, where they are most likely to be wasted, and how to control them.

First understand the three most common fields in the background

Input Tokens

Input tokens refers to the content you send into the model. It’s not just the sentence you type right now, it usually also includes system prompts, background descriptions, historical conversations, and the context that is brought in together. OpenAI officially lists this category directly as input tokens; Anthropic's documentation also regards the context window as the content range that the model will process together.

So if you see that the input tokens are very high, it does not necessarily mean that your prompt is very long. It may also be because the previous dialogue, system rules, and background information are all included.

Output Tokens

Output tokens refer to the content returned to you by the model. That is, answers, summaries, articles, and analysis results written by AI. OpenAI officially lists the content generated by the model as output tokens and clearly states that they are also part of billing and usage tracking.

This column is very important, because what many people are most likely to lose control of is not input, but output. You only ask one question, but if the model returns many sections, the cost will pile up.

Total Tokens

Total tokens is usually the total number of inputs plus outputs. Google Gemini also officially provides token counting and usage metadata methods to help you see the overall request size.

If you just want to quickly know whether the request is big or not, you can look at total first; but if you really want to find the cost problem, you still need to separate it and look at input and output.

Different platforms have different names, but the views are actually similar

The common ones in OpenAI are prompt tokens, completion tokens, or directly displaying input / output tokens. OpenAI officials also mentioned that different token types such as input, output, cached, and reasoning may appear in the response.

The common ones in Anthropic are input tokens and output tokens, and the naming is more intuitive. Its context window file also clearly states that the model will process the conversation context together.

Google Gemini sometimes displays it in different ways in different interfaces, but the official has provided a count tokens file, allowing you to directly see how many tokens a piece of content will consume.

So if you see the column names are different later, don’t worry and think the platform is confusing. Let’s go back to the core judgment first: whether this number counts input, output, or the overall total.

Many people think that the cost is spent on input, but in fact it is often not

The most common misunderstanding made by novices is that they think that their prompt typing is very long, so that must be the most expensive part. But in many generation tasks, what is really easy to explode is the output. OpenAI officially lists output tokens independently, which itself means that they are one of the important sources of billing.

For example, you only type one line: "Write a 2000-word article for me." This input may not be very high, but if the model really responds to you with a whole long article, the output tokens are likely to be much higher than the input tokens.

So when you look at the background, don't just stare at the input. Many times, what you should really look at first is whether the output is too long.

Why are there so many Tokens even though I only asked one question?

This is the most common reason. If you keep asking questions in the same conversation, the platform usually not only processes your last sentence, but also brings in the previous historical conversations. Anthropic's official description of context windows is based on this logic.

In other words, you think you are just asking a question, but what the model actually processes may be adding a sentence to the entire conversation.

Some applications will have a long system prompt behind them, such as role settings, format rules, brand tone, and process requirements. Although these words are not what you are typing right now, as long as they are sent to the model, they will be entered into the input tokens.

If you don't specify the answer length, it's easy for the model to answer longer than you think. OpenAI officials also recommend that settings such as max_output_tokens, max_completion_tokens or max_tokens can be used to control the output length, because shorter replies help control costs and delays.

How do you read the background numbers to really understand them?

The really useful thing is not just translating the field names, but knowing how to find problems from the numbers.

Looking at the input is to see how much background you have brought

If the input tokens are very high, you have to check first:

Is the prompt too long this time

Is there too much historical dialogue

Is the system prompt too lengthy

Whether unnecessary file content is also sent in

Looking at the output is to see if the model talks too much

If the output tokens If it is particularly high, you have to check:

Whether the answer length is not specified

Whether the model is required to do too many things at once

Whether it originally only requires a summary, but allows it to expand freely

Whether the same task can be broken down into smaller parts

Looking at total is to see if the entire request is too heavy

If total is very high, but you can't see the problem for a while, go back and split input and output. What we are really looking for is not "how much will it cost this time", but "which side is bigger?"

The most practical way to look at AI Token usage is to make comparisons

Instead of focusing on a single number, it is better to start comparing similar tasks.

For example, you have three summary requests:

Type A has high input and normal output

Type B has normal input and high output

Type C is high on both sides

This way you will quickly see the problem:

Type A usually has too many backgrounds

Type B usually has an out-of-control answer length

Type C usually means the task itself is too big and should be split

This view can really help you control the usage, rather than just looking at the total number.

How to control Token usage?

This is usually the most effective first step. OpenAI officials clearly recommend using output caps, clear instructions, stop sequences, etc. to control the generation length, because shorter replies are usually more cost-effective and faster.

Don’t let the same conversation accumulate indefinitely

If the topic of the task changes, it is usually cleaner to open a new conversation directly. Because the longer the context, the more likely it is that each subsequent round will bring more history into the cost.

The Prompt should be clear, not lengthy

Many people think that the longer the prompt, the more professional it is, but in fact, redundant descriptions, repeated requirements, and long backgrounds often just make the input tokens larger, which does not necessarily make the answer better.

If you are going to do a large generation task, it is usually easier to control the tokens by first making an outline, then segmenting, and finally integrating it than cramming it all at once. OpenAI officials also recommend that large text can be cut into small pieces for processing when the limit is exceeded.

If you just want to remember the most important thing first, that is:

AI Token usage is not just about total, but about input, output and context accumulation separately.

When you start to look at the background numbers in this way, you will really know where the costs are spent, and you will have a way to catch the waste.

How to check AI Token usage fastest?

Look at total first, then split input and output. When really looking for a problem, be sure to separate and see which side is getting bigger.

Why is there still a lot of tokens when I only ask one sentence?

Because the model usually not only processes the last sentence, it may also include system prompts and historical conversations.

Which one is more worthy of attention first, Input or Output?

In many generation tasks, what is really easy to get out of control is the output, because the answer length is often larger than you originally expected.

Can I control the output token?

Yes. OpenAI officially provides max_output_tokens, max_completion_tokens, max_tokens and other methods to control the generation length.

How to prevent token usage from rising continuously?

It is usually most effective to control the output length, reduce unnecessary background, and do not let the same conversation accumulate indefinitely.

Data source and credibility statement

This article is compiled and written based on official AI documents and token usage instructions, focusing on the following sources:

OpenAI | What are tokens and how to count them?

OpenAI | Controlling the length of model responses

Anthropic | Context windows

Google AI for Developers | Understand and count tokens || Readers can not only understand token numbers, but also really know which numbers are worth pursuing and where they are most likely to be wasted.

If you want to read more about related topics, you can go directly to AI Token.

This article belongs to the category of "AI Token Computing".

This category mainly organizes AI Token calculation methods, usage interpretation, input and output differences, word count conversion, background data understanding and cost estimation. It helps novices not only know how to calculate tokens, but also truly understand the relationship between background numbers and actual costs when they come into contact with ChatGPT, Claude, Gemini or other AI APIs.

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How to check the usage of AI Token? Novices can understand the numbers in the background without confusion