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AI Token Counter

Analyze text token counts for various LLM models including GPT-4 and Claude.

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LLM Token Counter & Cost Estimator (GPT-4, Claude)

Optimize your AI prompts and control costs. The Token Analyzer accurately counts tokens for major Large Language Models (LLMs) including OpenAI's GPT-4, GPT-3.5, and Anthropic's Claude. It is not just a word counter; it uses the specific tokenizer logic (like cl100k_base or p50k_base) to show you exactly how the model sees your text. Essential for developers optimizing context windows and managing API budgets.

How it Works

  • 1Paste your prompt or text into the analyzer.
  • 2Select the Tokenizer model (Auto, GPT-4, GPT-3.5, Legacy).
  • 3The tool calculates the Token Count instantly.
  • 4See a visual breakdown of how text is chunked into tokens.
  • 5Check estimated API costs for different models.

Key Features

Accurate BPE (Byte Pair Encoding) tokenization.
Visual Token Map: See exactly where words are split.
Multiple Model Support: GPT-4, GPT-3.5-Turbo, text-davinci-003.
Cost Calculator: Estimate input/output pricing.
100% Client-side Logic (using WASM/JS ports of tiktoken).

Common Use Cases

Prompt Engineering: Fit massive contexts into the 8k/32k limits.Cost Management: Estimate the bill before running a batch job.Model Comparison: See how different tokenizers handle code vs text.Debugging: Fix 'Context Length Exceeded' errors.

Frequently Asked Questions

Everything you need to know about using this tool effectively and securely.

Q.Why is token count different from word count?

LLMs read 'tokens', which can be parts of words. On average, 1000 tokens is about 750 words. Common words are 1 token, but complex or foreign words may be multiple tokens.

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AI Token Counter - GPT-4, Claude, Llama Tokenizer | CleanTextLab