LLM Token Counter & Cost Calculator

An LLM token counter estimates how many tokens your text will consume when processed by large language models like GPT-4, Claude, Gemini, or Llama. Tokens are the fundamental units that LLMs use to process text — they're typically word fragments, whole words, or punctuation marks. Understanding token counts is essential for managing API costs, staying within context window limits, and optimizing prompts.

Different models use different tokenization algorithms. OpenAI's GPT-4 and GPT-4o use the cl100k_base tokenizer, Anthropic's Claude models use their own tokenizer, and Meta's Llama models use SentencePiece. Each produces slightly different token counts for the same text. A word like "indescribable" might be 1 token in one model but 3 tokens in another, while common words like "the" are almost always 1 token.

This token counter uses BPE-like estimation rules to provide accurate counts across all major models simultaneously. It detects whether your input is code or prose (which affects tokenization patterns), shows per-model estimates with confidence ranges, and calculates real-time API costs. Everything runs in your browser — your prompts and data stay private.

1. Paste your text or prompt — Enter the text you want to analyze. The counter works with any content: prompts, code snippets, API payloads, documents, or conversation histories.
2. Review the content detection — The tool automatically detects whether your input is code, prose, or mixed content, which affects token estimation accuracy since code typically tokenizes differently than natural language.
3. Compare model estimates — View token counts for GPT-4o, GPT-4, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, and Llama 3 70B side by side, each with a ±10% confidence range.
4. Toggle input/output pricing — Switch between input and output token pricing to estimate costs for both sending prompts and receiving completions. Output tokens typically cost 2-5x more than input tokens.
5. Optimize and iterate — Use the character, word, and line counts alongside token estimates to refine your prompts and stay within budget.

• Multi-model estimation — Get token counts for 6 popular LLM models at once: GPT-4o, GPT-4, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, and Llama 3 70B.
• BPE-aware algorithm — Uses Byte Pair Encoding heuristics that model how real tokenizers split text, including special handling for common words, camelCase identifiers, numbers, and punctuation.
• Content type detection — Automatically distinguishes between code, prose, and mixed content to adjust estimates, since code tokenizes ~5-15% differently than natural language.
• Real-time cost calculation — Shows estimated API costs using current public pricing for each model, with separate input and output token rates.
• Confidence ranges — Every estimate includes a ±10% confidence interval so you can plan for worst-case token consumption.
• 100% client-side — Your prompts, code, and data never leave your browser. No server requests, no logging, no tracking.

• Prompt engineering — Check token counts while crafting prompts to ensure you stay within context window limits (e.g., 128K for GPT-4o, 200K for Claude 3.5 Sonnet).
• API cost estimation — Calculate how much an API call will cost before sending it, especially for long documents or batch processing workflows.
• Context window management — When building chatbot or RAG applications, monitor cumulative token usage across conversation turns to avoid hitting limits.
• Model comparison — Compare token efficiency and costs across models to choose the most cost-effective option for your use case.
• Budget planning — Estimate monthly API costs by measuring token counts on representative samples of your production data.