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)
Converting raw webpage HTML into clean Markdown can remove structural noise and fit more useful information into a limited context window.
The first stage covers the essential differences between large-model application development and traditional backend development, the boundaries of model capabilities, the role of tokens and context windows, the impact of the temperature parameter on output, API calling and billing logic, and what each mainstream model excels at.
The third stage of engineering deployment involves details such as wrapping interfaces with FastAPI, streaming output, session state management, logging instrumentation, building an evaluation (Eval) system, test cases covering model randomness, cost and token bill control, online performance tracking, and bad-case recycling.
Progressive disclosure puts metadata first, compressing first-layer overhead to just dozens of tokens per skill.
QQBridge starts a local HTTP service and generates a random token as an access credential; messages on the QQ side are relayed through this service, and callers must carry the correct token to use the interface.
PStack splits principles into independent skills rather than putting them into the system prompt because of Token cost: it avoids carrying a lengthy prefix on every request and enables on-demand loading.
In Transformer-based large language models, a token is the smallest billing and processing unit after text is segmented, roughly equivalent to 0.75 of an English word or 1 to 2 Chinese characters.
Most model APIs bill by input tokens, and each loop of an Agent resends the entire history, causing the same build log or file content to be billed repeatedly.
Once retrieval fees are removed, token processing costs will further rise as a share of an agent's total expenses, becoming a more visible cost item.
Before splicing web page body text into prompts, developers typically perform HTML cleaning, body extraction, truncation or summarization to compress the number of tokens sent into the model.
40 more timeline events
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%VerifiedThe context window refers to the total number of tokens a model can see in a single inference, including input and output; the computational cost of the attention mechanism grows quadratically with sequence length.
75%VerifiedThe context window determines the maximum number of tokens a model can see in a single inference; when project files exceed the window, the model forgets earlier content, resulting in inconsistent code or even errors.
75%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%