Token Counter
Estimate token count in AI models with pricing for major LLM providers
Statistics
Estimated Cost
$2.50 / 1M input tokens
$10.00 / 1M output tokens
What is a Token?
A token is the basic unit of text processing in AI models. Models don't process text by characters or words, but split text into smaller segments called tokens. Each token may be a character, part of a word, or a complete word.
Different models use different tokenization algorithms. GPT-4 uses BPE (Byte Pair Encoding), where English words are typically split into 1-2 tokens. DeepSeek and other Chinese-optimized models have better efficiency for Chinese text.
How to Use
Basic Operations
- Enter or paste text in the input area
- Select target AI model (GPT-4, Claude, Gemini, etc.)
- View token count estimation on the right panel
- Set estimated output length to calculate API costs
Tokenization Rules
- GPT series: ~4 English chars = 1 token, ~1.5 Chinese chars = 1 token
- Claude series: Similar to GPT with slight differences
- DeepSeek series: Optimized for Chinese, ~2 chars = 1 token
- Special characters, punctuation, and line breaks also consume tokens
- Structured text like code and JSON typically has higher token density
FAQ
Q: Why does the estimate differ from API results?
A: This tool uses approximation algorithms. Actual tokenization is complex, involving Unicode handling, special characters, abbreviations, etc. Use estimates as reference; actual count is in the API's usage field.
Q: What's the difference between Chinese and English token counting?
A: English words average about 4 characters per token, while Chinese characters vary by model: GPT ~1.5 chars/token, DeepSeek ~2 chars/token. Chinese-optimized models are more efficient.
Q: How can I reduce token usage?
A: You can reduce tokens by simplifying prompts, removing redundant information, and using more concise expressions. Choosing Chinese-optimized models (like DeepSeek) also improves efficiency.
Q: What's the relationship between tokens and characters?
A: There's no fixed conversion. English text typically has a char/token ratio of 3-5, Chinese in GPT is 0.5-1.5. Higher ratios indicate more efficient tokenization.
Q: Do different models count tokens the same way?
A: No. Each model has its own tokenizer and vocabulary. GPT-4, Claude, Gemini use completely different algorithms. DeepSeek is specially optimized for Chinese. Same text may have very different counts across models.