{"id":54566,"date":"2026-07-08T10:23:36","date_gmt":"2026-07-08T02:23:36","guid":{"rendered":"https:\/\/www.1ai.net\/?p=54566"},"modified":"2026-07-08T10:30:35","modified_gmt":"2026-07-08T02:30:35","slug":"token%e6%98%af%e4%bb%80%e4%b9%88%e6%84%8f%e6%80%9d%ef%bc%9f%e5%a4%a7%e7%99%bd%e8%af%9d%e8%ae%b2%e6%b8%85%e6%a5%9a%e4%bb%80%e4%b9%88%e6%98%aftoken","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/54566.html","title":{"rendered":"What does Token mean? Tell me what Token is"},"content":{"rendered":"

Token is what it is, and Token has become a new base of economic elements and strategic resources\u3002<\/strong><\/p>\n

At the beginning<\/strong><\/p>\n

The wave of the AI waves has sprung up, the applications are changing. No one will doubt the correctness of the phrase \u201cAI will go into the lives of all of us\u201d. But in all the discussions about AI, you must have heard a word very often \u2014 \u201cToken\u201d. So, what the hell is Token<\/p>\n

Token is a very central concept in computer and Internet technology. Token, we need to know three different applications of Token:<\/p>\n

Token in authentication (also known as access token)<\/p>\n

2. Token in Large Language Model (LLM) (in plain text language model, for example, where Token is a text unit) [ This application is the focus of today's talk<\/p>\n

Token in block chains and encrypted currency<\/p>\n

Token Application Site One<\/strong><\/p>\n

Let's start with the first application scenario: Token in the ID<\/strong><\/p>\n

It's the most common thing we've ever seen on the Internet. When we log in on a website or an app, the server will send you a Token<\/p>\n

For example:<\/p>\n

yJhbGciOiJIUziI1NiIsIncCI6IkpXVCJ9.eyJ1c2VySWiQiOjEsIm5hbWiOiOiJKb2huIERvZImV4cCI6MTcxNjI3MjAwMH0SflKwRJSEKF2QT4fpMeJf36Pok6yJV_adQsw5c<\/p>\n

Of course you might see a shorter Token, obviously I gave this Token longer. Although Token has an industry-wide format paradigm, Access Token does not have a uniform \u201cfixed look\u201d in the identification. We do not have to worry about whether Token's appearance format is uniform, but simply understands it as a set of letters\u3002<\/p>\n

Its working principles are as follows:<\/strong><\/p>\n

You entered the account code login<\/p>\n

2. The server is validated and an encrypted string (i.e. Token) is generated for your browser or App\u3002<\/p>\n

3. Since then, every time you ask for data (e.g. to refresh the circle of friends), you automatically bring this token\u3002<\/p>\n

When the server saw Token, it knew, \u201cOh, this is Zhang San, he has already entered\u201d and did not need you to repeat the password every time you operated\u3002<\/p>\n

When you look at the working principles, do you find that Token, as an access token, is not as mysterious as it is, is an encrypted token, to facilitate access to websites or APP information resources after a successful login\u3002<\/p>\n

Token Application Site II<\/strong><\/p>\n

\u00a0Let's look at the second application scenario: Token in the Big Language Model (LLM)<\/strong><\/p>\n

When we talk to Deepseek, ChatGPT or Gemini, we need to give him a word about what AI did for us, and at this point, AI is not read directly to our usual word or word. Because computers can't understand Chinese or English words directly, we have to start with text dismantling. Dismantling is usually in two categories, so let's just say this\u3002<\/p>\n

In English<\/strong>It is relatively simple, as English words are usually separated by spaces or punctuals, so the split by spaces and punctuals is sufficient. For example, the sentence \u201cI love AI!\u201d will be initially decomposed [\u201cI\u201d, \u201clove\u201d, \u201cAI\u201d, \u201c!\u201d)\u3002<\/p>\n

\"5bb9e74aj00thu3ut00c0d000rs00app\"<\/p>\n

there are, of course, more troublesome places in english where there are words that have not been recorded or that are more problematic, and where, for the sake of economy and precision, large models can be decomposed in a prefixed manner, such as unhappy, which may be decomposed to [`un', `happy', `ly'] (prefixed to `un-', root `happy', suffixed `-ly)\u3002<\/p>\n

\"e3195491j00tu3vh00dvd000rs00b6p\"<\/p>\n

Let's speak Chinese<\/strong>The Chinese language is complicated by the fact that the Chinese sentence is a continuous text, so the task of dismantling becomes the boundary of finding the right words by meaning\u3002<\/p>\n

For example, I love artificial intelligence. I, love, people, people, lovers, intelligence, artificial intelligence, etc. are all words that exist and that make sense, even the word \u201cwise\u201d can be a word. It's time for a much stronger algorithm to be properly decomposed into [\"I,\" \"love,\" \"Advanced Intelligence\"], which is the severing technique. The first is that it is obscure, the second is that it is not the focus of this discussion, and you need only know that we need more sophisticated algorithms in order to decipher the Chinese language\u3002<\/p>\n

\"e0ca7ej00thu3vm000rd000rq0055p\"<\/p>\n

SIMILARLY, THERE ARE CASES IN WHICH THIS WORD IS NOT INCLUDED IN CHINESE, SUCH AS \u201cTWILIGHT\u201d, WHERE HE HAS BEEN DECODED INTO SMALLER DIGITAL CODES, SUCH AS OPEN AND FIRE CODES (NOTE: I AM HERE FOR EXAMPLE, WHERE THERE MAY BE A PRACTICAL SOURCE, WE NEED ONLY KNOW THAT IT HAS BEEN DECODED BY LARGE MODELS INTO SMALLER UNITS ACCORDING TO OUR OWN RULES). WE NEED TO KNOW THAT THE MODEL ONLY KNOWS NUMBERS, SO WHEN YOU ENTER A SENTENCE, YOU START WITH THE DECOMPOSITION STEPS ABOVE, AND THE SEQUENCES THAT ARE BROKEN INTO WORDS (E.G.: [\"I,\" \"LOVE\", \"ADVANCED INTELLIGENCE\")], YOU TAKE A KEY STEP: CHECK THE DICTIONARY AND BE UNIQUE. THE MODALITIES ARE AS FOLLOWS:<\/p>\n

The large model has a fixed Vocabulary library, which typically contains 30,000 to 100,000 Tokens (and perhaps more). Each Token corresponds to a single index number. The word \u201cI\u201d corresponds to the figure of 1,500, the word \u201clove\u201d corresponds to the figure of 3210 and the word \u201cphysical\u201d corresponds to the figure of 8890. Attention, everyone. I assumed that figure. Anyway, what we need to understand is that every word has its own number, and we call them Token ID. Let me give you an example to understand:<\/p>\n

When you enter the phrase \"what physics is,\" you'll be decomposed [\"physics,\" \"yes,\" \"what,\" three separate words (i.e. Token), which we call the Token sequence. As we understand from the presentation, there's a \"map vocabulary\" in the big model, and each word corresponds to a single digit ID, and the three words are matched by a map to a string of 1,500, 3210, 8890\u3002<\/p>\n

\"f1bff353j00thu3w600dyd000rv00c2p\"<\/p>\n

These numbers are just word code names, computers can handle, but they don't know what they mean -- and then they're going to put them on the Embeding floor, and this step is really easy to understand. For example, if the word \u201cphysics\u201d had not passed through Embeding, he would have been an empty symbol, a word that had nothing to do with any other term, and if the word \u201cphysical\u201d could not be linked to other information, then we could not deal with any task. For example, when a person's mind is just the word \"physical,\" you ask him, \"What is physics?\" Can he answer your question? Obviously not. Big models are the same. So this step is quite important, and he wants to make the word physics no longer an empty stand-alone symbol, but rather to include \u201cthousands of dimensions\u201d. For example, physics is independent, but its characteristics are science, experiments, formulas, etc. Here, the coordinates of physics are very close to mechanics, quantum, gravity, magnetic field, etc., and very far from apples, running, etc\u3002<\/p>\n

Now, please close your eyes and imagine a web where there are many interwoven points, each of which has a feature word: mechanics, quantum, gravity, magnetic field, apples, running, puppy, etc. And the word \"physics\" is at the heart of your brain, and imagine and fill it in this network with the word \"physics\" at the heart, so which words are closer to it and which ones should be farther away from it. By the same token, the large model is such that the different characteristics are distributed in a network centred on the word \u201cphysical\u201d so that the large model can calculate the relationship between these words and words in the recent past\u3002<\/p>\n

Now these words are closer than before. However, there is no link between them, at which point the large model will analyse the relationship between the word and the word and link the relevant word through something called the \u201cfocus mechanism\u201d. Taking the phrase \u201cwhat physics is\u201d, the analysis shows that \u201cphysics\u201d is the dominant language, \u201cwhat\u201d is the binary, and \u201cis\u201d connects them. This is when the model combines three independent vectors, which are no longer isolated individuals, but rather a semantic whole (technically, we call it \u201cexpression\u201d), through a focus mechanism, where the model understands our problem, \u201cwhat is physics\u201d\u3002<\/p>\n

After that, the model begins to move into the prediction chain, where it performs complex calculations in mathematical space, and calculates what is the next most probable Token, based on the current language vector. For example, it predicts that the next word is \u201cexperimental\u201d or \u201crelationship\u201d etc. The model remixes the calculated probabilistic vector back to ID (the corresponding number) and then returns text that humans can understand through the word map. That's what we want\u3002<\/p>\n

I will further elaborate on this by means of an example of an interactive process decomposed from Token:<\/strong><\/p>\n

Step 1: Your input (Prompt)<\/strong><\/p>\n

You enter text: \"Please give me a sentence with a cat, give me a sentence for two cats, a big model with words and Token:<\/p>\n

[ \u201cPlease\u201d, \u201cFull\u201d, \u201cI\u201d, \u201cUse\u201d, \u201cCat\u201d, \u201cFull\u201d, \u201cPut\u201d, \u201cGive\u201d, \u201cTwo\u201d, \u201cCat\u201d, \u201cCat\u201d, \u201cSent\u201d<\/p>\n

So the number of Tokens entered: 13<\/p>\n

Step 2: Model output (Reponse)<\/strong><\/p>\n

Text: \"I particularly like kittens\" and \"cats are really lovely creatures.\" Phrases and Tokenization (example):<\/p>\n

First sentence: [\u201cI\u201d, \u201cSpecial\u201d, \u201clike, \u201cCat\u201d) 4 Token<\/p>\n

Convergence: [\"and\"] \u2192 1 Token<\/p>\n

Second sentence: [\"Cat\", \"Really\", \"Yes\", \"A\", \"A\", \"A,\" \"Cute\", \"A\", \"Bio.\" 9 Tokens<\/p>\n

Number of output Tokens: 4 + 1 + 9 = 14<\/p>\n

So the total consumption of this interactive Token is equal to the number of Tokens + output Tokens = 13 + 14 = 27 Tokens. By the way, Cache also increases consumption as dialogue grows, which is why dialogue takes longer and more\u3002<\/strong><\/p>\n

In this example, you pay for a total of 27 text units (Tokens). The model converts the 27 units (e.g. \u201cPlease\u201d, \u201cCat\u201d, \u201cCat\u201d, \u201cCat\u201d, \u201cCute\u201d) internally to 27 IDs and 27 high-dimensional vectors, respectively, and calculates on their basis, resulting in two sentences that you see. Every word you can read, word, node, as long as it takes up a unit of the model, it's a Token\u3002<\/p>\n

Token looked as follows before it was converted to a number:<\/p>\n

Input end (13)<\/strong>: Please help me to make my words with a cat, and give me two sentences for a cat<\/p>\n

Output end (14):<\/strong>\"I'm especially fond of cat, cat, cat, cat, cat, cat, cat, cat, cat, baby, baby, baby, baby, baby, baby, baby, baby, baby<\/p>\n

Corresponding to the transformation to Token ID is as follows: [67854, 45212, 2543, 1209, 78431, 3321, 908, 123, 5542, 1029, 78431, 882, 14520,] It is a string of numbers that only machines can read. Each number usually occupies a storage space of 2 bytes (16 bit) or 4 bytes (32 bit)\u3002<\/p>\n

If you go through the big model's backstage logs, you'll see the 27 Token final consumption vouchers go like this:<\/strong><\/p>\n

{<\/p>\n

\"usage\": {<\/p>\n

'prompt_token<\/a>s:13,<\/p>\n

\u201ccomplement_tokens\u201d :14,<\/p>\n

\"total_tokens\": 27<\/p>\n

}<\/p>\n

}<\/p>\n

Token application scenario three<\/strong><\/p>\n

Finally, let's seeThird scenario application: block chain and Token in encrypted currency<\/strong><\/p>\n

Token in the block chain has a very different meaning than we have, and you can interpret it as a \u201ccertificate\u201d or \u201cdigital object\u201d in the digital world. In a simple metaphor, the block chain is a large, open and unmistakable digital book. Token can be understood as the only information recorded in this account that represents the ownership or interest of something\u3002<\/p>\n

I've finished all three of Token's applications, and I'm sure you understand. In the field of AI Large Model Application, Token is the basic unit for costing and usage, which corresponds directly to the total amount of input and output text you consume\u3002<\/p>\n

Having mastered what I said above, I think you can see why Token has become a new base of economic elements and strategic resources. In the industrial age, electricity is the basis, we pay \u201cdegrees\u201d; in the information age, the flow is the basis, we pay \u201cGB\u201d. In the age of artificial intelligence, Token is the smallest measure of intelligence. Token is consumed and produced in essence, whether it be personal creation, business automation or national-level computing competition. Whoever owns Token at a lower cost and handles Token more efficiently has the right to price \u201csmart productivity\u201d\u3002<\/p>\n

Token has made \u201cknowledge\u201d not just a text in a book, but a \u201csyntax asset\u201d that can be directly involved in production. Such assets can be duplicated indefinitely, transmitted instantaneously and accurately mobilized. When we pay for 27 Tokens, we buy not just words, but \"processed intelligence.\" Just as we are no longer concerned about how the generators move, it is the same thing to focus on the bills. I believe that all intellectual work in future societies will be accurately quantified as token. It is no longer a simple technical term, but a strategic resource like oil and rare earth\u3002<\/p>\n

In the physical world, energy persistence is the basic law; but in the digital world, Token is the measure of intelligent persistence\u3002Behind each unit, Token is essentially the sum of computing power, electricity and human knowledge density\u3002<\/strong><\/p>","protected":false},"excerpt":{"rendered":"

Token is what it is, and Token has become a new base of economic elements and strategic resources. It's written at the beginning of the wave of AI waves, with applications coming out of place, and it's changing. No one will doubt the correctness of the phrase \u201cAI will go into the lives of all of us\u201d. But in all the discussions about AI, you must have heard a word very often \u2014 \u201cToken\u201d. So, what the hell is Token? Token is a very central concept in computer and Internet technology. Token, to be clear, we need to know three different applications of Token: 1. Token in identification (also known as access tokens) 2. Token in large language (LLM) models (in pure text language models, for example)<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[144],"tags":[480,1270],"collection":[],"class_list":["post-54566","post","type-post","status-publish","format-standard","hentry","category-baike","tag-ai","tag-token"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/54566","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=54566"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/54566\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=54566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=54566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=54566"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=54566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}