OpenAI Token Counter

What is an OpenAI Token Counter?

An OpenAI token counter calculates the number of tokens in text for OpenAI's GPT models. Tokens are the units OpenAI uses to measure text length—approximately 4 characters or 0.75 words per token. Counting tokens is essential for estimating API costs, staying within context windows, and optimizing prompts.

Why Count OpenAI Tokens?

Token counting is crucial for OpenAI API usage:

• Cost Estimation: Estimate API costs before making requests (OpenAI charges per token)
• Context Window Management: Ensure prompts fit within model context limits (4K-128K tokens)
• Prompt Optimization: Reduce token usage to lower costs and improve efficiency
• Budget Planning: Plan API budgets for AI projects and applications
• Model Selection: Compare token usage across different GPT models

Common Use Cases

API Cost Estimation

Estimate costs before making OpenAI API requests. Different GPT models have different pricing—GPT-4 costs more than GPT-3.5 Turbo. Count tokens to predict expenses accurately.

Prompt Optimization

Optimize prompts to reduce token usage. Fewer tokens mean lower costs and faster responses. Use token counting to identify verbose sections and trim unnecessary content.

Context Window Management

Verify prompts fit within model context windows. GPT-3.5 Turbo has 4K tokens, GPT-4 Turbo has 128K tokens. Token counting helps ensure you don't exceed limits.

Budget Planning

Plan API budgets for AI projects. Calculate token usage for typical workflows to estimate monthly costs and set usage limits.

Model Comparison

Compare token counts across GPT models. Understand how the same prompt tokenizes differently in GPT-4 vs GPT-3.5 Turbo to choose the right model.

OpenAI Models Supported

Our counter supports all major OpenAI models:

• GPT-4o: Latest model with 128K context window
• GPT-4o-mini: Cost-effective variant with 128K context window
• GPT-4 Turbo: Fast GPT-4 with 128K context window
• GPT-4: Standard GPT-4 with 8K or 32K context windows
• GPT-3.5 Turbo: Cost-effective model with 4K or 16K context windows

How Token Counting Works

OpenAI uses different tokenizers for different models:

• cl100k_base: Used by GPT-3.5 Turbo and GPT-4
• o200k_base: Used by GPT-4o and GPT-4o-mini
• Accurate Counting: Our tool uses the same tokenizers OpenAI uses for accuracy

Token Counting Best Practices

• Real-time Counting: Count tokens as you write prompts to stay within limits
• Include System Messages: Count system messages, user messages, and assistant messages
• Estimate Output: Consider output token costs (often more expensive than input)
• Monitor Usage: Track token usage over time to optimize costs
• Model Selection: Choose models based on token limits and pricing

Understanding Token Costs

OpenAI pricing varies by model:

• GPT-4o: $2.50 per 1M input tokens, $10 per 1M output tokens
• GPT-4o-mini: $0.15 per 1M input tokens, $0.60 per 1M output tokens
• GPT-4 Turbo: $10 per 1M input tokens, $30 per 1M output tokens
• GPT-3.5 Turbo: $0.50 per 1M input tokens, $1.50 per 1M output tokens

Privacy and Security

Our OpenAI Token Counter processes all text entirely in your browser. No text or prompts are sent to our servers, ensuring complete privacy for sensitive prompts and data.

Related Tools

If you need other AI or developer tools, check out:

• OpenAI Cost Calculator: Calculate detailed API costs
• Anthropic Token Counter: Count tokens for Claude models
• Regex Tester: Test regular expressions