Paste the text you want to analyze into the left input box, and the tool will display character count, word count, line count, and estimated token count in real time. It also estimates API costs for popular large language models based on the current text length, helping you plan usage and budget effectively.
When writing or preparing prompts, this tool helps estimate token consumption. The left panel shows estimated token counts for Chinese and English separately, while the right cost panel calculates fees for different models. This is especially useful for scenarios requiring precise API cost control.
This tool uses a statistical estimation algorithm, with an error margin typically within ยฑ10% compared to OpenAI's official tiktoken. For precise billing, refer to the usage field returned by the API.
Token counters are increasingly important in the large language model era. Content creators can use this tool to estimate token consumption across different models when writing long articles, helping choose the right model and parameters. Developers designing prompt engineering need to precisely control input length to stay within the model's context window.
For enterprise users, API cost control is critical. This tool lets you estimate costs before calling the API, avoiding unexpected bills. Educators can also use it to explain LLM billing mechanics to students, making the abstract concept of tokens tangible and intuitive.
Tokens are the smallest units of text processed by large language models. Unlike humans who understand text in words or characters, models need to split text into token sequences. In English, a word usually corresponds to 1 token, but a word like "unbelievable" might be split into "un", "believ", "able". In Chinese, characters are typically 1-2 tokens each due to the large character set.
Current mainstream models use BPE (Byte Pair Encoding) for tokenization. This algorithm progressively merges the most common character combinations in a corpus into larger units. OpenAI's GPT series uses the cl100k_base tokenizer, while Claude uses its own, so the same text may yield slightly different token counts across platforms.
What is a Token and why do LLMs charge by tokens?
A token is the basic unit of text processed by large language models. Unlike character-based billing, models use tokens because the Transformer architecture processes text as a sequence of tokens. English words are usually 1 token, while Chinese characters are typically 1-2 tokens. Token-based billing more accurately reflects the actual computational cost.
How accurate is this token counter?
This tool uses a statistical estimation algorithm. Results are for reference only, with an error margin of approximately ยฑ10% compared to OpenAI's official tiktoken. For precise billing, refer to the usage field returned by the official API. The estimates are sufficient for daily writing, prompt optimization, and cost estimation.
Do GPT-4 and GPT-3.5 have the same token count?
For the same text, GPT-4 and GPT-3.5 generally have the same token count because they use the same BPE tokenizer (cl100k_base). However, GPT-4o may vary slightly. Different models have different context window sizes, which is an important metric for model capability.
How do I control API token consumption?
Methods to control token consumption include: 1) Streamline prompts by removing redundant descriptions; 2) Use system messages to set roles and constraints; 3) Limit max_tokens to avoid overly long responses; 4) Use streaming output; 5) For long texts, consider chunking or summarization.
What is the difference between token count and character count?
A token is the model's basic processing unit, while character count is simply the number of characters. For example, the English word "hello" is 1 token but 5 characters. Token count better reflects the model's actual computational load, which is why APIs bill by tokens rather than characters.