🔢 ChatGPT Token Counter
Estimate tokens for ChatGPT, Claude, and other AI models with our Token Counter. Get token count estimates, API cost predictions, and prompt optimization insights for better LLM budgeting and efficiency.
Token Analysis Results:
Token Analysis Results
Tokens
Estimated Cost
Model
Context Used
Example token count preview for GPT-4 model analysis
How to Use This ChatGPT Token Counter
How to Count AI Model Tokens:
- Paste your prompt or text content into the input area
- Select the AI model you're targeting (GPT-4, GPT-3.5, Claude, etc.)
- View real-time token counts and cost estimates as you type
- Use the analysis to optimize prompt length and reduce API costs
- Copy token count results for documentation and budgeting
Pro Tips: Different models use different tokenization methods. GPT models typically count ~4 characters per token, while Claude uses a different tokenizer. Use this tool to stay within model limits and estimate costs accurately!
How It Works
Understanding AI Token Counting:
AI models like ChatGPT and Claude process text by breaking it into tokens, which are roughly equivalent to words or word fragments:
- Tokenization: Text is split into tokens using model-specific algorithms
- Character Estimation: Most models use ~4 characters per token on average
- Model Limits: Each model has maximum token limits (GPT-4: 8K-128K, Claude: 200K+)
- Cost Calculation: API pricing is based on input + output token usage
- Optimization: Shorter prompts cost less and often perform better
- Context Windows: Stay within model limits to avoid truncation
Usage Examples: Optimize prompts for ChatGPT API, estimate Claude conversation costs, stay within model context limits, and budget for large-scale AI applications.
When You Might Need This
- • Estimate ChatGPT API costs before submitting prompts for accurate budget planning
- • Count Claude tokens to stay within context limits and optimize conversation length
- • Analyze prompt token usage for GPT-4 and GPT-3.5 cost comparison and optimization
- • Budget large-scale AI applications by calculating token usage across multiple requests
- • Optimize prompt engineering workflows by minimizing unnecessary tokens and costs
- • Track token consumption in AI chatbots and conversational applications for cost control
- • Estimate API billing for content generation, summarization, and text processing tasks
- • Analyze multi-turn conversation costs for customer service and support automation
- • Calculate token efficiency for prompt templates and reusable AI content workflows
- • Monitor context window usage to prevent truncation in long-form AI interactions
Frequently Asked Questions
How accurate are the token count estimates for different AI models?
Our estimates provide useful approximations using common tokenization patterns. GPT models typically average ~4 characters per token, while Claude uses different tokenization. Estimates are generally within 10-20% of actual counts for standard text, with more variation for special characters, code, or non-English content. For exact counts, use the official model tokenizers.
Can I use this to estimate costs for OpenAI and Anthropic APIs?
Yes! The tool provides cost estimates based on current API pricing for GPT and Claude models. Cost calculations include both input and output tokens, though final costs may vary due to actual tokenization differences, model updates, and regional pricing variations.
Does the counter work with code, prompts, and non-English text?
Yes. Our estimator works with code snippets, complex prompts, emojis, and international languages. Different content types may have varying tokenization patterns, and our tool provides reasonable approximations across various AI models, though results may be less accurate for specialized content.
What's the difference between context limits for different AI models?
GPT models typically range from 8K to 128K+ tokens, Claude models support 200K+ tokens, and Gemini models vary by version. Our tool estimates how much of each model family's context window your text uses, helping you choose appropriate models and avoid truncation issues.
How can I optimize my prompts to reduce token usage and costs?
Use concise language, remove unnecessary words, avoid repetition, and structure prompts efficiently. Our tool shows real-time token counts as you edit, making it easy to experiment with shorter versions. Consider using GPT-3.5 for simpler tasks and GPT-4 for complex reasoning to optimize costs.