Real-time token count statistics for GPT / Claude / Gemini / Llama / Qwen and other major LLMs
Text Statistics
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Characters
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Excl. spaces
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Words
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Sentences
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Lines
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UTF-8 Bytes
Text Composition
Token Estimation Comparison
Model
Tokens
Compare
Context Window
Token counts are estimates based on each model's tokenizer characteristics; actual values may differ by 5-15%
Model
Input Cost
Output Cost
Unit Price ($/MTok)
Costs are estimated based on official platform pricing, for reference only
Token Optimization Tips
1. Remove extra spaces and blank lines to reduce meaningless tokens 2. Use concise expressions instead of verbose descriptions to reduce prompt length 3. Code text typically has higher token density; simplify comments and variable names 4. Chinese text has better token efficiency in Gemini / Qwen 5. English text varies less across models; Chinese varies more significantly