AI Box
Runs on your deviceNothing you type here is sent anywhere — the whole tool is JavaScript in this tab.
Token Counter
Toolsda's Token Counter tokenises a prompt and estimates its cost across GPT-4, Claude and Gemini before you send it — useful for checking a prompt against a model's context window limit. Tokenisation happens entirely in this tab; your text is never uploaded.
Your prompt
0 characters0 words0 lines
Try a sample:
Tokens & cost
0.0% of 128K limit used — 128,000 tokens remaining
Characters
0
Words
0
Cost (USD)
$0.00
Cost (INR)
₹0.00
Estimated Cost
Input cost$0.00
Output est. (same length)$0.00
100 requests$0.00 / ₹0.00
Fits comfortably in GPT-4o's context window
What is a Token?
A token is the small chunk of text a language model actually reads — not quite a word, not quite a character. Before generating a reply, models like GPT-4o and Claude break your input into these pieces using a fixed vocabulary learned during training. Common short words are usually one token each, while longer or unusual words, numbers, and punctuation often split into two or more. For example, the phrase "ChatGPT is awesome" comes out to just 4 tokens with GPT-4o's tokenizer. Because API pricing and context limits are both measured in tokens — not words — knowing your token count before you hit send is the easiest way to avoid a surprise bill or a request that gets rejected for being too long.
Token vs Word vs Character
| Text | Words | Tokens (GPT-4o) | Characters |
|---|---|---|---|
| "ChatGPT is awesome" | 3 | 4 | 18 |
| "The quick brown fox jumps over the lazy dog." | 9 | 10 | 44 |
| "Tokenization splits text into sub-word pieces before a language model processes it." | 12 | 15 | 83 |
Model Comparison
| Model | Context Window | Input Price / 1K | Best For |
|---|---|---|---|
| GPT-4o (OpenAI) | 128K tokens | $0.005 | Fast, cheap, multimodal everyday use |
| GPT-4 Turbo (OpenAI) | 128K tokens | $0.0100 | Long-context reasoning tasks |
| GPT-4 (OpenAI) | 8K tokens | $0.0300 | Highest-quality legacy GPT-4 output |
| GPT-3.5 Turbo (OpenAI) | 16K tokens | $0.0005 | High-volume, low-cost chat |
| Claude 3.5 Sonnet (Anthropic) | 200K tokens | $0.003 | Coding and long-document analysis |
| Claude 3 Opus (Anthropic) | 200K tokens | $0.0150 | Complex, nuanced reasoning |
| Gemini 1.5 Pro (Google) | 2M tokens | $0.0035 | Massive documents, 2M token context |
| Gemini 1.5 Flash (Google) | 1M tokens | $0.00035 | Cheap, high-throughput requests |
| Llama 3 70B (Meta) | 8K tokens | $0.00059 | Self-hosted / open-weight deployments |
5 Tips to Reduce Tokens
- Cut filler words and hedging phrases — models don't need "please kindly note that" to follow instructions.
- Replace long examples with a short, representative one instead of pasting five similar samples.
- Use bullet points instead of full sentences for structured instructions or data.
- Trim repeated context in multi-turn chats — summarize earlier turns instead of resending them.
- Strip unneeded whitespace, comments, and formatting from code before pasting it into a prompt.