{"id":4476,"date":"2026-03-17T14:56:57","date_gmt":"2026-03-17T06:56:57","guid":{"rendered":"https:\/\/www.aigosearch.com\/?p=4476"},"modified":"2026-03-17T15:54:43","modified_gmt":"2026-03-17T07:54:43","slug":"ai-tokens","status":"publish","type":"post","link":"https:\/\/www.aigosearch.com\/zh\/post\/ai-tokens\/","title":{"rendered":"\u4ec0\u4e48\u662f AI Token\uff1f\u5b9a\u4ef7\u3001\u4e0a\u4e0b\u6587\u7a97\u53e3\u53ca\u56fe\u50cf\/\u89c6\u9891 Token \u8be6\u89e3"},"content":{"rendered":"
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Token \u662f AI \u7cfb\u7edf\u7684\u8d27\u5e01\u3001\u8bb0\u5fc6\u548c\u8ba1\u7b97\u5355\u4f4d\u3002<\/strong><\/p>\n\n\n\n\u4eba\u5de5\u667a\u80fd\u6b63\u5728\u6df1\u523b\u5730\u5f71\u54cd\u5e76\u91cd\u5851\u6211\u4eec\u7684\u65e5\u5e38\u751f\u6d3b\u3002<\/figcaption><\/figure>\n\n\n\n
\u56fe\u50cf\u548c\u89c6\u9891\u4e2d\u7684 AI Token<\/h2>\n\n\n\n
\u968f\u7740 AI \u4ece\u6587\u672c\u5904\u7406\u8fdb\u5316\u5230\u7406\u89e3\u89c6\u89c9\u5a92\u4f53\uff0cToken \u7684\u6982\u5ff5\u4e5f\u968f\u4e4b\u6269\u5c55\u3002\u5f53\u4f60\u4f7f\u7528 GPT-4o \u6216 Gemini 1.5 Pro \u7b49\u591a\u6a21\u6001\u6a21\u578b\u6765\u751f\u6210\u6216\u5206\u6790\u56fe\u50cf\u548c\u89c6\u9891\u65f6\uff0c\u7cfb\u7edf\u5e76\u4e0d\u4f1a\u5c06\u5b83\u4eec\u89c6\u4e3a\u6587\u4ef6\uff0c\u800c\u662f\u5c06\u5176\u89c6\u4e3a\u4e13\u95e8\u7684\u89c6\u89c9 Token\u3002<\/p>\n\n\n\n
\u89c6\u9891 Token \u662f\u5982\u4f55\u8ba1\u7b97\u7684<\/h2>\n\n\n\nOpenAI Sora \u89c6\u9891\u5c55\u793a\u754c\u9762\u7684\u622a\u56fe<\/figcaption><\/figure>\n\n\n\n
\u603b\u800c\u8a00\u4e4b\uff0c\u89c6\u9891 Token \u7684\u8ba1\u7b97\u65b9\u5f0f\u662f\u5c06\u89c6\u9891\u5206\u89e3\u4e3a\u91c7\u6837\u5e27\uff0c\u7136\u540e\u5c06\u6bcf\u4e00\u5e27\u4f5c\u4e3a\u56fe\u50cf\u8fdb\u884c\u5904\u7406\u3002\u603b Token \u4f7f\u7528\u91cf\u53d6\u51b3\u4e8e\u4e09\u4e2a\u4e3b\u8981\u56e0\u7d20\uff1a\u89c6\u9891\u65f6\u957f\u3001\u5e27\u91c7\u6837\u7387\u4ee5\u53ca\u6bcf\u4e00\u5e27\u7684\u5206\u8fa8\u7387\u548c\u590d\u6742\u5ea6\u3002\u968f\u7740\u8fd9\u4e9b\u56e0\u7d20\u7684\u589e\u52a0\uff0cToken \u6d88\u8017\u4f1a\u8fc5\u901f\u589e\u957f\uff0c\u4f7f\u89c6\u9891\u6210\u4e3a AI \u7cfb\u7edf\u4e2d\u8d44\u6e90\u6d88\u8017\u6700\u9ad8\u7684\u6570\u636e\u8f93\u5165\u7c7b\u578b\u4e4b\u4e00\u3002<\/p>\n\n\n\n
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If you\u2019ve used AI tools like ChatGPT, Claude, or Gemini, you\u2019ve probably seen the term \u201ctokens\u201d everywhere. Whether it\u2019s API pricing, model limits, or \u201ccontext windows,\u201d tokens are at the core of how AI actually works. So what exactly are tokens\u2014and why do they matter so much? This guide breaks it all down in a clear, practical way. What Are Tokens? At a basic level: A token is the smallest unit of text that an AI model processes. It\u2019s not exactly a word or a character. A token can be: Example: I love AI tools Tokenized as: [“I”, “love”, “AI”, “tools”] \u2192 4 tokens But a more complex word: unbelievable Might become: [“un”, “believ”, “able”] \u2192 3 tokens Why Not Charge by Word Count? A common question arises: why don\u2019t AI platforms simply charge by word or character count, much like traditional translation services? The shift to token-based billing is driven by three fundamental technical necessities. First, language standardization is nearly impossible with word counts. While English relies on clear spaces between words, languages like Chinese do not, and others like Japanese or Korean possess highly complex morphological structures. Tokens provide a universal metric that standardizes processing costs across all human languages. Second, tokens represent how models actually think. AI models do not see sentences or words as humans do; instead, they process sequences of mathematical vectors. The workflow moves from $Input \\rightarrow Tokenization \\rightarrow Vectors \\rightarrow Model$, and finally back to $Output Tokens$. In this architecture, tokens are the true computational unit of the system’s brain. Finally, tokenization allows for more accurate pricing based on actual computational complexity. Simple, common words might only require a single token, whereas rare technical terms or complex coding strings require multiple tokens to break down. By billing based on tokens rather than characters, platforms can ensure that pricing accurately reflects the real-world GPU power and compute consumed by the model to generate a specific response. Tokens vs Words: What\u2019s the Difference? Content Type 1 Token \u2248 English ~0.75 words Chinese ~1 character Mixed text 1\u20134 characters Example: In many cases, Chinese content is more token-efficient. How AI Pricing Works The fundamental formula for AI billing is simple: Input Tokens + Output Tokens = Total Usage. To visualize this, consider a typical interaction where you ask the system to perform a task. If your prompt is “Write an SEO article,” that short instruction might account for 10 input tokens. The AI then generates a comprehensive response that could span 500 output tokens. In this scenario, your total billed amount for the transaction would be 510 tokens. This breakdown is crucial because most providers price input and output tokens at different rates, as generating new text typically requires more computational power than reading the provided instructions. Why Output Tokens Cost More On many platforms: Reason:Generating text requires more computation than reading it What Is a Context Window? Another key concept: The context window is the maximum number of tokens a model can \u201cremember\u201d at once. Examples: Real Example: Conversation history: Turn 1: 100 tokens Turn 2: 200 tokens Turn 3: 300 tokens Why Context Window Matters The context window is a critical factor because it directly defines the boundaries of an AI’s operational capacity. First, it dictates the limits of content length that the model can handle at once. Whether you are generating long-form articles, analyzing thick PDF documents, or maintaining extensive multi-turn conversations, the context window determines how much information can be processed before the model starts losing track of earlier data. Second, the size of this window significantly affects the overall quality of the AI’s memory and performance. A larger context window allows for a deeper understanding of complex relationships within the data, leading to more coherent and contextually relevant responses. When a model can “see” more of the conversation history or document at once, it is less likely to hallucinate or contradict itself. Finally, the context window has a direct impact on cost. Utilizing more of the available context means processing a higher volume of tokens, which inevitably leads to increased token usage and higher operational expenses for each request.More tokens \u2192 higher cost Tokens are the currency, memory, and computation unit of AI systems. AI Tokens in Images and Videos As AI evolves from processing text to understanding visual media, the concept of tokens has also expanded. When you use multimodal models like GPT-4o or Gemini 1.5 Pro to generate or analyze images and videos, the system doesn’t see them as files, but as specialized visual tokens. How Image Tokens Are Calculated When you upload an image to an AI model, it does not interpret the picture as a whole the way humans do. Instead, the image is first transformed into a structured format that the model can process mathematically. The process begins by dividing the image into a grid of small regions, commonly referred to as patches or tiles. Each patch represents a fixed-size block of pixels, such as 16\u00d716 or 32\u00d732 pixels, depending on the model design.After this division, each patch is converted into a numerical representation known as an embedding. This embedding captures important visual features like colors, edges, textures, and patterns. In this sense, each patch functions similarly to a token in text processing. Just as a sentence is broken into tokens for a language model, an image is broken into patches for a vision model. The total number of patches generated from an image directly affects how much computation is required. For billing purposes, most AI platforms simplify this underlying process by using either a fixed token cost or a resolution-based pricing system. Lower-resolution images are often assigned a standard token range, typically somewhere between 85 and 800 tokens per image. This allows platforms to provide predictable pricing without exposing users to the complexity of patch-level calculations.When dealing with higher-resolution images, the calculation becomes more detailed. Instead of processing the image as a single unit, the system divides it into multiple tiles. Each tile is<\/p>","protected":false},"author":1,"featured_media":4479,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"none","_seopress_titles_title":"What Are AI Tokens? Pricing, Context Window & Image\/Video Token Explained","_seopress_titles_desc":"Learn what AI tokens are and how they work. Understand token-based pricing, context windows, and how image and video tokens are calculated to optimize cost and performance.","_seopress_robots_index":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-4476","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-post"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/posts\/4476","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/comments?post=4476"}],"version-history":[{"count":10,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/posts\/4476\/revisions"}],"predecessor-version":[{"id":4495,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/posts\/4476\/revisions\/4495"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/media\/4479"}],"wp:attachment":[{"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/media?parent=4476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/categories?post=4476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aigosearch.com\/zh\/wp-json\/wp\/v2\/tags?post=4476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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