Token estimation
Most LLMs use subword tokenization. A common rule of thumb: 1 token ≈ 4 characters of English text, or 100 tokens ≈ 75 words. Code tends to produce more tokens per character due to symbols and indentation.
Token Counter & Cost Estimator
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Estimate LLM token counts and API costs for text or code. Supports GPT-4o, Claude 3.5 Sonnet, and open-source models. Works entirely in your browser.
Your data stays in your browser.
All token estimation happens locally. Nothing is uploaded to DevPouch or any AI provider.
Paste or type text to estimate token count, character count, word count, and API cost.
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Tokens (est.)
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Characters
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Words
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Lines
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Estimated API Cost
Input (prompt)
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Output (completion)
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Total
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Tokens estimated at ~4 characters per token. Actual tokenization varies by model tokenizer.
How token estimation works
Cost calculation
Cost = (tokens ÷ 1,000) × price per 1K tokens. Prices shown are per-million-token rates converted to per-thousand. Actual pricing varies by provider and region.
Privacy-first
All calculations run in your browser using JavaScript. Your text never leaves your device and is never sent to any AI provider or server.
Frequently Asked Questions
- No. All token estimation and cost calculation happens entirely in your browser. Your text never leaves your device.
- The estimate uses ~4 characters per token, which is accurate within ~10-15% for most English text and code. For exact counts, use the official tokenizer for your specific model (e.g., tiktoken for OpenAI models).
- Yes. Once the page is loaded, all calculations run locally. No internet connection is required.
Understanding LLM Tokenization and API Costs
What is a token?
Tokens are the basic units LLMs read and write. A token can be a single character, a word, or part of a word. For example, "apple" is one token, but "tokenization" might be split into two or three. Roughly, 1,000 tokens equals about 750 words of English text.
How pricing works
Most LLM providers charge per 1,000 tokens, with separate rates for input (your prompt) and output (the model's response). Longer prompts and longer responses cost more. Estimating tokens before calling an API helps you budget and avoid surprises.
Why estimation varies
Different models use different tokenizers. GPT-4o uses a different vocabulary than Claude or Llama. Our estimator uses ~4 characters per token as a rule of thumb, which is accurate within about 10-15% for most English text and code.
Why Client-Side Token Estimation Matters for Developers
No data leaves your browser
Every calculation happens locally using JavaScript. Your text is never uploaded to DevPouch or any AI provider, keeping sensitive code and confidential content private.
Instant feedback
As you type, the token count, character count, and estimated cost update in real time. Tweak your prompt and see the cost impact immediately before sending anything to an API.
Works offline
Once loaded, the page runs entirely in your browser. No internet connection is required, so you can estimate tokens and costs anywhere, even on restricted networks.
Optimizing Prompts for GPT-4 and Claude
Be specific and concise
Clear instructions reduce ambiguity and often need fewer tokens. Remove filler words and redundant context to keep costs down while improving output quality.
Use examples sparingly
Few-shot prompting (including examples) improves accuracy but adds tokens quickly. Include only the minimum examples needed to convey the pattern.
Set output limits
Specify desired response length (e.g., "in under 200 words") to bound output tokens and control cost. The model will respect concise constraints when you state them clearly.