Paste any text to instantly estimate token count and API costs across GPT-4o, Claude, Gemini, and more.
| Model | Input Cost (this text) | Cost × 1,000 req/day / mo |
|---|
LLMs don't process text character by character — they use tokens, which are chunks of text. The exact size varies: common words are usually 1 token, rare or long words might be 2-4 tokens, and special characters/code often tokenize differently.
English prose: ~4 characters per token or ~0.75 words per token
Code: ~3-4 characters per token (symbols and brackets are often individual tokens)
Numbers: Each digit is often 1 token; large numbers can be 2-4 tokens
Non-English: CJK characters are often 2-3 tokens each; European languages similar to English
Most current models support 128K-200K token context windows. Claude supports 200K. GPT-4o supports 128K. You pay for every token in your context window on each request — long conversations get expensive quickly.
Practical optimizations: (1) Be concise in system prompts — every word costs money at scale. (2) Use structured output formats (JSON) rather than verbose descriptions. (3) Truncate conversation history — only keep the last N turns. (4) Compress retrieved documents before including them in context.
This tool estimates tokens using a 4-characters-per-token heuristic, which is accurate to within ~10% for typical English text. For exact counts, use the official tokenizers: tiktoken for OpenAI models, anthropic.tokenizer for Claude. The heuristic is less accurate for code, non-English text, and text with many special characters.
Yes. Every token in your context window (system prompt + conversation history + current message) is billed as input tokens on every request. A 200-token system prompt costs 200 tokens × number of daily requests. For 10,000 daily requests, that's 2M extra input tokens per day.
GPT-4o: 128,000 tokens. GPT-4o-mini: 128,000 tokens. Claude 3.5 Sonnet: 200,000 tokens. Gemini 1.5 Pro: 2,000,000 tokens. Gemini 1.5 Flash: 1,000,000 tokens. Mistral Large: 32,000 tokens. Llama 3.1 70B: 128,000 tokens.
In 2026, a common budgeting mistake is counting only visible prompt + output tokens. Many newer reasoning models also consume hidden deliberation/reasoning tokens, and some providers now expose them separately in usage metadata while still charging for them. If your app suddenly looks 20–60% more expensive than your token estimate, check whether the model adds internal reasoning or tool-planning tokens. For accurate forecasts, compare this page’s token count with the provider’s returned usage fields from a real API call, not just the raw text length.