AI Token Counter
Count tokens in your text and estimate API costs for GPT-4, Claude, Gemini, DeepSeek, and other AI models. Optimize your prompts to reduce costs.
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Cost Estimation (0 tokens)
Costs shown for input tokens. Toggle above to switch. Actual tokenization varies by model.
Sample Texts (Click to Load)
About Token Estimation
What is a token? Tokens are the basic units that AI models process. In English, a token is roughly 4 characters or about 0.75 words. Punctuation, numbers, and special characters often count as separate tokens.
Why is this an estimate? Different AI models use different tokenization algorithms. GPT uses BPE (Byte Pair Encoding), while other models may use different methods. Our estimate provides a good approximation for cost planning purposes.
Tips for reducing tokens:
- Remove unnecessary whitespace and formatting
- Use concise language and avoid redundancy
- Summarize long context instead of including full text
- Use system prompts efficiently with reusable templates
How to Use the AI Token Counter
Paste or Type Your Text
Enter the text you want to analyze in the input area. This can be a prompt, system message, document, or any content you plan to send to an AI API.
View Token Count Results
See the estimated token count update in real-time as you type. The counter shows approximate tokens using a universal estimation algorithm that works across major AI providers.
Select Model for Cost Estimate
Choose from GPT-4o, Claude, Gemini, DeepSeek, or other models to see the estimated cost for processing your text. Costs are calculated using current API pricing.
Optimize Your Text
Use the token count to identify ways to reduce costs. Remove unnecessary whitespace, use concise language, and summarize long content to minimize token usage and API expenses.
Pro tip: Your data is processed entirely in your browser. Nothing is sent to any server, ensuring complete privacy.
Understanding AI Tokens
When working with AI APIs like OpenAI, Anthropic, or Google, understanding tokens is crucial for cost management. Tokens are the basic units of text that AI models process - they can be words, parts of words, punctuation, or even individual characters depending on the tokenization algorithm used.
For English text, a helpful rule of thumb is that one token equals approximately 4 characters or 0.75 words. However, this varies significantly with content type: code often has higher token density due to special characters, while simple prose may tokenize more efficiently.
Token Optimization Strategies
- Trim whitespace: Extra spaces and newlines add unnecessary tokens
- Be concise: Remove filler words and redundant phrases from prompts
- Use summaries: Instead of including entire documents, summarize relevant sections
- Reuse system prompts: Cache common instructions to reduce repeated token usage
- Choose the right model: Use smaller, cheaper models for simple tasks
Frequently Asked Questions
What is a token in AI?
A token is the basic unit that AI language models process. In English, one token is roughly 4 characters or about 0.75 words. Punctuation, numbers, and spaces often count as separate tokens. AI APIs charge per token for both input (your prompts) and output (model responses).
How many tokens is 1000 words?
Approximately 1,300-1,500 tokens for standard English text. The exact count depends on word length and complexity. Technical content with many special characters or code may have higher token counts per word.
How accurate is this token counter?
Our token counter provides estimates within 5-15% of actual tokenization for most text. Different AI models use different tokenization algorithms (GPT uses BPE, others may vary), so exact counts differ slightly. This estimate is reliable for cost planning.
How can I reduce my token count?
Remove unnecessary whitespace and formatting, use concise language, avoid redundancy, summarize long context instead of including full text, and use efficient system prompts. Also consider if the full context is needed or if relevant excerpts would suffice.
Why do input and output tokens have different prices?
Output tokens cost more because generating new text requires more computational resources than processing input. The model must run multiple inference passes to produce each output token, while input processing is more efficient.
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