AI Token Counter
Count tokens and estimate API costs for GPT-4, Claude, Gemini, and other AI models
Input Text
Select Model
Cost Estimate
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Complete Guide to AI Token Counting
What are Tokens?
Tokens are the fundamental units that AI language models use to process text. They're not exactly words or characters, but pieces of text that the model's tokenizer splits your input into. Understanding tokens is crucial for estimating API costs and staying within context limits.
Key Token Facts:
How to Use This Tool
Enter Your Text
Paste your prompt, system message, or any text you want to analyze. The token count updates in real-time as you type.
Select Your Model
Choose from GPT-4, Claude, Gemini, Llama, and many other models. Each model has different pricing and context limits.
View Cost Estimation
See the estimated cost based on current API pricing. Adjust the expected output token count to get accurate total cost estimates.
Check Context Usage
The context usage bar shows how much of the model's context window your input uses. Stay below the limit to avoid truncation.
Understanding Token Pricing
AI model pricing is typically based on the number of tokens processed. Most providers charge separately for input (your prompt) and output (the model's response) tokens.
Input Tokens
- • Your prompt or question
- • System messages
- • Context or conversation history
- • Usually cheaper than output
Output Tokens
- • The model's response
- • Generated content
- • Reasoning and explanations
- • Usually 2-4x more expensive
Context Windows Explained
A model's context window is the maximum number of tokens it can process in a single request (input + output combined). Different models have different limits:
| Model | Context Window | ~Pages of Text |
|---|---|---|
| GPT-4o | 128K tokens | ~200 pages |
| Claude 3.5 Sonnet | 200K tokens | ~300 pages |
| Gemini 1.5 Pro | 2M tokens | ~3000 pages |
| GPT-4o Mini | 128K tokens | ~200 pages |
Tips for Optimizing Token Usage
💡 Cost Optimization Tips
- Be concise: Remove unnecessary words and whitespace from prompts
- Use shorter models: GPT-4o Mini costs ~15x less than GPT-4o for similar quality
- Limit output: Use max_tokens parameter to cap response length
- Cache responses: Store common responses to avoid repeated API calls
- Batch requests: Combine multiple small requests into one when possible
Token Counting Accuracy
ℹ️ About This Tool's Estimates
This tool provides estimates based on common tokenization patterns. Actual token counts may vary because:
- • Different models use different tokenizers
- • Non-English text may tokenize differently
- • Code and special characters have unique tokenization
- • Newer models may use updated tokenizers
For precise counts, use official libraries like tiktoken for OpenAI or provider-specific tokenizers.
Common Use Cases
Developers
- • Estimate API costs before deployment
- • Optimize prompt engineering
- • Stay within context limits
- • Budget planning for AI features
Content Creators
- • Plan content generation costs
- • Compare model pricing
- • Optimize long-form content
- • Track usage for budgeting