AI Token Counter — GPT, Claude & Gemini
Count tokens for GPT, Claude, Gemini, and other AI models. Estimate costs per API call with built-in pricing. Free online tool.
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AI Token Counter — Count Tokens for GPT, Claude & Llama
Tokens are the basic units that AI models use to process text. Understanding token counts helps you estimate API costs and optimize your prompts for better results.
Different AI models tokenize text differently. GPT-4 and GPT-4o use the cl100k_base tokenizer, while newer OpenAI models use o200k_base. Claude uses its own tokenizer, and open-source models like Llama use SentencePiece. This tool estimates token counts for each model family so you can plan your API usage accurately.
Token counts directly determine API costs. A prompt with 1,000 input tokens and a 500-token response costs different amounts depending on the model. GPT-4o charges $2.50 per million input tokens, while Claude Opus charges $15 per million. Knowing your token count before sending a request helps you budget and choose the right model for the task.
Context window limits are specified in tokens, not words or characters. GPT-4o supports 128K tokens, Claude supports up to 200K tokens, and Gemini supports up to 1M tokens. When your prompt plus the expected response approaches the context limit, the model may truncate its output or refuse to process the request. Measuring your input tokens helps you stay within bounds.
All token counting happens in your browser — no text is sent to any server. For estimating the cost of your AI usage across different providers, use our LLM Pricing Calculator which compares rates for 50+ models.
How the AI Token Counter Works
- Paste your text or prompt into the input area
- Select the target model (GPT-4, Claude, Llama, etc.)
- The tool tokenizes the text using the model's encoding scheme
- See the total token count and estimated API cost
Understanding AI Tokens
Tokens are the units AI models use to process text — roughly 1 token per 4 characters in English, or about 75 words per 100 tokens. Different models use different tokenizers: GPT models use tiktoken (cl100k_base or o200k_base), while Claude uses its own tokenizer. Token counts directly affect API costs and context window limits. System prompts, conversation history, and the model's response all count toward the total token budget.
When to Use the AI Token Counter
Use this tool when estimating API costs before sending prompts, optimizing prompts to fit within context window limits, comparing how different models tokenize the same text, debugging unexpected token count issues in your AI application, or budgeting for AI API usage across a project or team.
Common Use Cases
- •Estimate API costs before sending prompts to AI models LLM Pricing Calculator — Compare 50+ Models
- •Verify that prompts fit within model context window limits
- •Optimize system prompts by measuring token count and trimming unnecessary content
- •Compare tokenization efficiency across different AI model families
- •Budget monthly AI API spending based on average token usage per request
Expert Tips
- ✱Output tokens cost 2-4x more than input tokens on most AI APIs. Optimize for shorter responses by being specific about the desired output format and length.
- ✱Code typically tokenizes less efficiently than English prose — expect 1.5-2x more tokens per line of code compared to natural language.
- ✱Non-Latin scripts (Chinese, Japanese, Arabic) use significantly more tokens per character. A 100-character Chinese text might be 100+ tokens compared to 25 tokens for 100 English characters.
- ✱Combine this tool with our LLM Pricing Calculator to translate token counts directly into dollar amounts across different models.
Frequently Asked Questions
- Each model family uses a different tokenizer. GPT-4 uses cl100k_base, GPT-4o uses o200k_base, and Claude uses its own tokenizer. The same English sentence might be 20 tokens in one model and 18 in another. Non-English text and code show even larger differences between tokenizers.
- In English, one token is roughly 4 characters or about 0.75 words. The word 'hamburger' is 3 tokens in most tokenizers, while 'the' is 1 token. Non-English languages, code, and special characters typically use more tokens per word.
- Yes. System prompts, conversation history, and the model's response all count toward the context window limit. A 2,000-token system prompt in a 4,096-token context window leaves only 2,096 tokens for the conversation and response combined.
- For GPT models, the count uses the official tiktoken library and is exact. For other models, the count is a close estimate based on the tokenizer's known behavior. Exact counts for Claude and Gemini require their proprietary tokenizers.
Why do different models produce different token counts?▾
How do tokens relate to words?▾
Do system prompts count toward the token limit?▾
Is the token count exact?▾
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