Token
A token is the basic processing unit of text in natural language processing and large language models, produced by a tokenizer splitting raw text into units that may correspond to a word, subword, character, or punctuation mark. Models perform computation in units of tokens for both input processing and output generation. Token count directly affects a model's context window limit, inference speed, and API usage costs. Token segmentation rules vary across languages and models.
Core Facts
The context window of a large language model determines the upper limit of information the model can process in a single inference.
Large model APIs are typically billed by the number of Tokens, with approximately every 750 English words or 400 Chinese characters corresponding to 1,000 Tokens.
Token是大语言模型处理文本的基本单位，大约对应0.75个英文单词或1.5个汉字
Timeline (last 90 days)
LLM 的 Token 成本大致随上下文长度线性增长，而调用次数随 Agent 数量增加，两者相乘导致非线性膨胀。
The context window is measured in Tokens and determines how much code or documentation the model can process at once; long file analysis often requires segmented processing.
A token is the basic unit of text processed by an LLM, and it can be a complete word, part of a word, punctuation, or a space.
The same sentence translated into Hindi, Japanese, or Arabic may have two to three times the number of tokens as the English version.
All LLM API price lists are priced per million tokens, with input and output charged separately: cost = input_tokens/1,000,000 × input_price + output_tokens/1,000,000 × output_price.
For English prose and OpenAI's current tokenizer, 1 token ≈ 4 characters, 1 token ≈ ¾ of a word, and a page of dense A4 text is about 600–800 tokens.
Features involving role switching, cross-organization access, and token passing are high-incidence zones for logic vulnerabilities; such problems are difficult for automated tools to discover, have a high severity level, and correspondingly pay higher bounties.
The complete definitions of 200 tools of medium complexity average about 30,000-80,000 Tokens.
Token reduction brings three chain effects: lower cost, improved latency, and enhanced scalability.
Reasoning models and verbose system prompts can significantly change the actual number of tokens consumed by a task.
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All Facts (20)
The context window of a large language model determines the upper limit of information the model can process in a single inference.
90%VerifiedLarge model APIs are typically billed by the number of Tokens, with approximately every 750 English words or 400 Chinese characters corresponding to 1,000 Tokens.
90%VerifiedToken是大语言模型处理文本的基本单位，大约对应0.75个英文单词或1.5个汉字
90%VerifiedThe computational cost of large language models depends primarily on the number of tokens, model parameter scale, and context window size.
85%VerifiedApproximately every 1 Chinese character corresponds to 1–2 tokens, and every English word corresponds to 1–4 tokens.
80%VerifiedOpenAI's API billing distinguishes between input tokens and output tokens, with output tokens typically priced higher.
80%VerifiedEach Chinese character typically requires 1–2 tokens.
80%VerifiedAI Agent development is transitioning from a proof-of-concept stage to an engineering stage focused on reliability, cost efficiency, and scaled operations.
80%VerifiedAn Agent's token consumption for a single task may be 5–10 times that of an ordinary conversation, due to the involvement of multiple rounds of large model calls.
80%VerifiedThe vocabulary of a standard autoregressive language model typically contains 32,000 to 128,000 tokens.
80%Verified大模型API中输出Token的单价通常是输入Token的2-4倍
80%VerifiedApproximately every 750 English words or 500 Chinese characters corresponds to 1,000 tokens.
80%VerifiedA large model is essentially a probability-based text generator that produces content by predicting the next token.
80%VerifiedAPI call fees are priced separately for input tokens and output tokens, and the unit price of output tokens is typically higher than that of input tokens, because the output stage requires autoregressive inference.
75%VerifiedA token is the smallest semantic unit segmented by subword tokenization algorithms such as BPE, and is not simply equivalent to a single character or word.
75%VerifiedIn large language models, a Token is the smallest unit of text that a model processes
75%VerifiedIn English, one token corresponds to approximately 0.75 words.
75%VerifiedLong conversations package and resend the entire context history to the model, which is counted toward the token usage of each request; the longer the conversation, the more tokens a single request consumes.
75%VerifiedThe pricing of large model APIs is typically based on a token billing mechanism.
75%VerifiedThe internal chain of thought in reasoning models generates a large number of output tokens, making the actual cost of completing complex tasks often far higher than what a per-token price comparison would suggest.
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