What are the frequently asked questions about AI Token? The 20 most common questions that novices get stuck on when using APIs for the first time

The reason why AI Token frequently asked questions are always searched is usually not because everyone has never heard of Token, but because most people will quickly get stuck on the same batch of nouns after starting to come into contact with AI APIs: Input, Output, Cache, System Prompt, Context Window, and Reasoning. These words all seem to make sense, but when it comes to pricing pages, billing backends, model files, or actual API settings, it's easy to get them all mixed up.

This situation is normal. Although OpenAI, Google Gemini, and Anthropic all regard Token as the basic unit of model processing content, their implementation methods are not exactly the same in terms of token segmentation, billing fields, cache, multi-modal input, and inference mechanisms. In other words, you think you are just understanding a term, but you are actually understanding the usage rules of different platforms at the same time.

This article will not be written as a general article, nor will it compete with the main battle page for teaching how to use AI Token. Instead, it will focus directly on a more clear question:

What are the most common AI Token issues that novices get stuck on when they first connect to the AI API?

After reading these 20 questions, you can usually quickly distinguish which are basic concepts, which are related to price, which will affect costs, and which are the most likely to be confused between platforms.

Let me start with a summary: The more AI Tokens are not, the better, nor is it only about price.

If I want to remember the most important thing first, it is:

AI Token 不是越多越好，也不是只跟價格有關，而是你要看它被花在哪裡、怎麼被計算、怎麼被管理。

As long as you remember this sentence first, many subsequent questions will be much easier.

Group 1: First understand the underlying concept of AI Token

What exactly is AI Token?

Token is the basic unit for model processing content. OpenAI officials are very straightforward. Token is the basic unit when the model processes text; Gemini officials also say that Gemini and other generative models process input and output at the granularity of Token. In other words, the model does not think in terms of "articles, sentences, and paragraphs", but breaks the content into more detailed units to understand and generate.

2Why does AI not charge based on the number of words or articles?

Because different languages, formats, and content types have different complexity for the model. OpenAI officially stated that spaces, punctuation, and some words will affect the number of tokens, and non-English content usually has a higher ratio of tokens to characters. In other words, if you only charge based on the number of words, the actual cost of processing the model will be inaccurately reflected.

Will the English and Chinese Tokens be the same?

Usually not. OpenAI officially reminds that non-English texts usually produce a higher ratio of tokens to characters; Gemini officially gives English experience values, 1 token is approximately equal to 4 characters, and 100 tokens is approximately equal to 60 to 80 English words. This means that you cannot directly copy common evaluation methods for English articles to Chinese content.

Will the Token for the same piece of content be the same on different platforms?

Not necessarily. OpenAI's official cookbook clearly states that different models may use different encodings; Anthropic's Token Counting document also reminds that the token count is an estimate and may be slightly different when actually creating the message. Therefore, just because everyone calls it Token, it does not mean that the cutting method is exactly the same.

Is AI Token the platform’s own rules, or is it used by the entire industry?

The concept is used throughout the industry, but the details are not unified standards. OpenAI, Gemini, and Anthropic all use Token as the basic unit for model processing content, and they all provide token counting or usage fields; however, tokenizer, billing, caching, thinking, and multimodal rules are not exactly the same. A more accurate statement is: everyone uses the Token language, but each company has its own implementation rules.

Group 2: 5 types of Token usage that you really need to understand

What is Input Token?

Input Token refers to the content you send into the model. This not only includes the sentence you typed into the model, but also often includes system prompts, historical dialogues, knowledge background, files, pictures, tools, schemas, etc. OpenAI's token counting document specifically emphasizes that images, files, tools, and schemas will all affect token count.

What is Output Token?

Output Token is what the model returns to you. This column is particularly important because most platforms set the unit price of output higher than that of input. OpenAI's official price page clearly lists input, cached input, and output separately; Gemini Billing also lists output token count as the official basis for billing. Many times, what really drives up the bill is not that you ask too many questions, but that the model answers too many questions.

What is Cached Token?

Cached Token is a reusable prefix or context. OpenAI officially says that cached tokens often have lower rates; Gemini Billing incorporates cached token count and cached token storage duration into the billing basis; Anthropic also prices cache write and cache read separately. This column is important for long system prompts, fixed brand specifications, long backgrounds, and multiple dialogue rounds.

What is Thinking/Reasoning Token?

This type of Token is related to the internal reasoning of the model. Gemini's usage metadata will list thoughtsTokenCount; OpenAI also mentioned that some reasoning models may use more internal tokens first; Anthropic provides extended thinking and adaptive thinking. Simply put, this is not the output you see directly, but it affects the quality, latency and cost of complex tasks.

Are pictures, PDFs, and tools also considered Tokens?

Yes. OpenAI says that images, files, tools, and schemas will all affect token count; Anthropic also says that images and PDFs can be used for token counting, and tool use will also bring in additional tool use system prompt tokens; the Claude Vision document even gives an approximate algorithm for image tokens. This means you can’t just look at pure text length, tools and multimodal content are often a source of cost as well.

Group 3: How to read the price list so as not to give up at first glance

Which columns should a novice look at first when looking at the price list?

It is enough to understand the 4 fields first: model name, Input, Output, Cache. The OpenAI price page has such a basic structure; Gemini Billing also has input, output, and cache as the main axes; the Claude price page has input, cache writes, cache hits, and output. If you understand these 4 fields first, most of the subsequent price pages will be easy.

Why can’t you just look at the lowest unit price when looking at the price list?

Because the lowest unit price is usually only a small part of the answer. Your true cost will also be affected by output length, cache, tools, batch mode, long context, and workflow structure. OpenAI officials recommend using the most capable model to establish a benchmark first, and then see if other models can achieve the same results at a lower cost; this means that the "total cost of completing the same thing" is more important.

Why is Output often worth looking at first than Input?

Because the unit price of output of many models is higher than input. When your tasks are long articles, reports, long JSON, code, or complete analysis, it is often the output that really drives up the bill. This is why many people think that "the unit price is cheap" but the total cost at the end of the month is still quite high.

Cache seems very advanced, is it really worth understanding for novices?

Worth it. OpenAI said that Prompt Caching can reduce input costs by up to 90%; Anthropic said that cache read is only 0.1 times the base input token rate; Gemini also officially included cache in Billing. As long as your process has a lot of repetitive context, caching is almost certainly a cost point worth looking at first.

Why do some platforms add Storage Duration or Cache Duration?

Because some platforms not only look at how many tokens you withdraw, but also how long you keep them. Gemini's official billing lists cached token storage duration as the official billing basis; Anthropic also distinguishes between 5 minutes and 1 hour of cache writing. This means that cache is not just about hits or misses, but also about retention time.

Group 4: How to calculate AI Token? How to estimate?

What is the most stable estimation method for novices?

Not guessing, but counting first. OpenAI's token counting document clearly states that you can get a more accurate input token count before sending a request to estimate costs, avoid context limits, and avoid estimation errors in images and files; Anthropic also has a Token Count API. For formal API usage, this is generally more reliable than empirically estimating word count.

Then how do I estimate how much it will cost per month?

The most practical approach is usually to grab 3 numbers first: average input, average output, and number of requests per day or month. Then multiply it by the input/output unit price corresponding to the model. It’s not the most sophisticated algorithm, but it’s enough to get you started with a first-level budget estimate. Both OpenAI and Gemini explicitly tie pricing to input/output. This valuation method is a practical practice directly extended from the official price structure.

Why is it easy to spend completely different amounts of money when everyone talks about one million Tokens?

Because one million Tokens are not equal to the same cost. If one million Tokens are mainly input, the price will be different from that of mainly output; if there are a large number of cached tokens in one million Tokens, the price will also be different; if it also contains thinking, tools, images or long context conditions, the difference will be even greater. That's why looking at totals is often not enough.

Group 5: How to start saving costs, and not randomly

What should be the first step in AI Token cost control?

The first step is usually not to switch to the cheapest model, but to take a clear look at the cost structure. A more practical sequence is: first divide input-heavy and output-heavy, then look at which content can be cached, then ask which tasks can be batched, and finally go back to compare the unit price of the model. The official price structures of OpenAI, Gemini, and Claude actually support this order of judgment.

What is the most easy-to-ignore method for newbies, but actually the most worth doing immediately?

There are usually three. First, think about cache first when repeating backgrounds; second, think about batch first for non-real-time tasks; third, don’t use high-order models on tasks that are too simple. Both OpenAI and Gemini have Batch API routes, and both OpenAI and Anthropic clearly regard caching as a formal cost optimization method. These methods are not necessarily the flashiest, but they are usually the first to see results.

What you really need to know now is not to memorize numbers, but to have a set of judgment sequences

If these 20 questions are compressed into a set of the most practical practical sequences, it will probably look like this:

First know what a token is, and then clearly distinguish input, output, cache, and thinking; then learn to estimate using official token counting or usage metadata; then read the price list; and finally do cost optimization and platform comparison.

This order is less likely to make mistakes than chasing the lowest unit price from the beginning.

If you just want to grab a primary keyword and start slowly, it is recommended to look down from the topic of AI Token. First establish the skeleton of definition, calculation, price and cost control, and then extend to model comparison, API procurement and team governance. It will be much smoother.

FAQ

Can AI Token and word count be directly converted?

It can only be roughly estimated, and the equal sign cannot be drawn directly. Both OpenAI and Gemini have approximate empirical values, but non-English content often consumes more tokens, so it is best to use token counting for formal estimation.

Only looking at the lowest unit price, why do you often still spend a lot?

Because the real cost also depends on output, cache, tools, multi-modality and workflow structure. A low input unit price does not mean a low total cost.

Why is Output often more worthy of attention than Input?

Because the unit price of output on most platforms is higher than that of input, and tasks such as long articles, reports, and program codes are particularly easy to drive up output.

Is Prompt Caching only needed by large companies?

no. Whenever you have a fixed system prompt, long background, multiple rounds of dialogue, or repeated prefixes, the cache is usually worth looking into first.

When an enterprise introduces AI, when should it start looking at budget and permissions?

When you enter the multi-person, multi-department, and multi-project usage stage, it is worth starting to look at the governance capabilities of project budgets, usage dashboards, and workspace limits. Official documents such as OpenAI and Google Cloud have corresponding capabilities.

The number of tokens in the same piece of content is different on different platforms. Is this normal?

Normal. Different models and platforms may use different encoding or different estimation methods, so don’t just apply the numbers from one platform to all.

Data source and credibility statement

This article is written based on mainstream AI official Token, Pricing, Billing, Token Counting, Prompt Caching and usage management documents, focusing on official information such as OpenAI Token description, OpenAI API Pricing, OpenAI Prompt Caching, Gemini Tokens, Gemini Billing, Claude Token Counting, Claude Pricing. The content is organized in a three-layered manner of "Official Rules × Usage Structure × Cost Control Practices", with the purpose of making the information verifiable, operable, and extendable, rather than just explaining the terms.

If you want to go back to the main page of AI Token usage teaching, you can start from this article: AI Token Teaching Lazy Pack: From getting started, calculating to cost saving, understand it all at once

If you want to start from the whole site theme entrance, you can also go back to the home page: AI Token

This article belongs to the category of "AI Token Usage Teaching"

This category mainly organizes the actual use methods and APIs of AI Token Getting started, usage interpretation, cost estimation and platform operation logic help novice users, content creators, case recipients and enterprises to understand more quickly how to start using AI APIs and model platforms, how to check usage, and how to avoid pitfalls at the beginning.

AI Token teaching package for lazy people: from getting started, calculating to cost saving, understand it all at once

Can’t understand the AI Token price list? Newbies should first understand where the costs come from

What should we look at first for AI Token cost control? Don't just look at the lowest unit price

AI Token
Prompt Caching
AI Token FAQ

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.

What are the frequently asked questions about AI Token? The 20 most common questions that novices get stuck on when using APIs for the first time