AI/LLM Token Calculator
Calculate tokens and estimate costs for GPT, Claude, Gemini, and other AI models instantly
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Token Analysis
Cost Estimation
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Understanding LLM Tokens
What are tokens?
Tokens are the basic units that language models use to process text. A token can be as short as one character or as long as one word. On average, one token is approximately 4 characters or 0.75 words in English.
How tokenization works
Different models use different tokenization methods. OpenAI models use BPE (Byte Pair Encoding), while other models may use SentencePiece or custom tokenizers. This means the same text might result in different token counts across models.
Cost optimization strategies
- Use prompt caching: Anthropic's Claude models offer up to 90% discount on cached prompts
- Batch processing: Many providers offer 50% discounts for batch API requests
- Choose the right model: Smaller models like GPT-4o mini or Gemini Flash can be 10-20x cheaper
- Optimize prompts: Remove unnecessary words and use concise instructions
Token limits and context windows
Each model has a maximum context window - the total number of tokens it can process in a single request. This includes both your input prompt and the model's output. Larger context windows allow for more complex tasks but may come with higher costs.
When to use different models
- GPT-4o: Best for complex reasoning and creative tasks
- Claude 3.5 Sonnet: Excellent for coding and analysis with prompt caching
- Gemini 1.5 Pro: Ideal for very long documents (up to 2M tokens)
- Llama models: Open-source option with competitive performance
- Smaller models: Perfect for simple tasks, classification, and high-volume processing
Why token counts don't match character or word counts
A tokenizer does not split text on spaces. It learns a vocabulary of frequent character sequences during training and greedily matches your text against that vocabulary. Common English words often become a single token, but a rare word, a long compound, a URL, or a chunk of source code may be broken into several pieces. Whitespace, capitalization, and even a leading space can change how a word is tokenized. That is why "cost" and " cost" (with a space) can be different tokens, and why the same paragraph in English, German, or Japanese produces very different totals: languages that don't use spaces or that fall outside the tokenizer's core vocabulary tend to consume more tokens per character.
How this calculator estimates counts and cost
For OpenAI models, this tool runs the actual GPT Byte Pair Encoding tokenizer in your browser, so the count matches what OpenAI's API would report. For Anthropic, Google, Meta, and other providers, exact tokenizers differ and aren't always public, so the tool falls back to the widely used estimate of roughly four characters per token. Use those figures for budgeting and comparison rather than exact billing. The cost columns multiply your token count by each model's published input price, add an estimated output cost based on output being about half the input length, and let you sort every supported model from cheapest to most expensive for the exact text you pasted.
Turning token counts into a budget
Because API pricing is quoted per million tokens, a single request can look almost free while a production workload adds up quickly. Multiply the per-request total shown here by how many times you expect to call the model each day, then by 30 for a monthly estimate. If that figure is uncomfortable, the biggest levers are choosing a smaller model, enabling prompt caching for repeated context, batching non-urgent requests, and trimming the prompt itself. The comparison table makes the first lever obvious: the same text can cost twenty times more on a flagship model than on a lightweight one, so matching the model to the difficulty of the task is usually the fastest way to control spend.
Frequently Asked Questions
Why do different models report different token counts for the same text?
Each model family is trained with its own tokenizer and vocabulary. OpenAI models use Byte Pair Encoding (BPE), while others use SentencePiece or custom schemes, so the same sentence can be split into a different number of pieces. This tool counts OpenAI models exactly with the GPT tokenizer running in your browser; for other providers it uses the common approximation of about four characters per token, which is close but not identical to their official counts.
Is my text sent anywhere to be counted?
No. Token counting happens entirely in your browser using the bundled GPT tokenizer, and the pricing table is a static file loaded from Fix42. The text you paste is never uploaded, which means you can safely estimate tokens for prompts that contain private or proprietary content.
How is the estimated cost calculated?
Input cost is your token count multiplied by the selected model's published input price. Output cost is an estimate: the tool assumes the model will generate output equal to roughly half your input tokens and prices that at the model's output rate. Real output length varies enormously by task, so treat the total as a planning figure rather than an exact bill.
What does the cached-pricing option mean?
Some providers, such as Anthropic for Claude, let you reuse a previously processed prompt prefix at a large discount (often around 90% off the input price). When a model supports prompt caching, ticking the cached-pricing box recalculates the input cost at that discounted rate so you can see the savings for repeated system prompts or long shared context.
What is a context window and why does it matter?
The context window is the maximum number of tokens a model can consider at once, counting both your prompt and its response. If your input alone approaches the window, there is little room left for the answer. The tool shows each model's context window and warns you when your text exceeds it, which helps you pick a model or trim your prompt before hitting a hard limit.
How can I reduce the number of tokens I use?
Tighten your wording, remove redundant instructions and examples, and avoid pasting large blocks of boilerplate you do not need. For repeated calls, prompt caching and batch pricing lower the effective cost, and choosing a smaller model for simple tasks can cut the per-token price by an order of magnitude. Because tokens roughly track characters, shorter prompts are almost always cheaper prompts.
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