Estimate token usage and compare potential monthly API costs across popular AI models. Enter a sample prompt, choose expected output usage, and adjust your monthly request volume to see how the estimated cost changes.
TokensCalc performs an estimate rather than calling a model API. Your text is processed in your browser and is not sent to an AI provider by this calculator.
Paste text below to estimate its token count.
Change these values to model a typical request and monthly request volume.
Paste a prompt or sample text that is representative of the requests your application sends to an AI model. The calculator measures the number of characters and whitespace-separated words in the text.
TokensCalc uses a lightweight heuristic to estimate input tokens. The estimate considers both character count and word count. It is intended for planning and comparison, not as a replacement for the tokenizer used by a specific model.
AI APIs generally charge separately for input and output tokens. Use the output slider to represent the approximate number of generated tokens expected from each request.
The calculator multiplies estimated tokens per request by your monthly request volume and applies the displayed model rates to produce an estimated monthly cost.
Based on your current assumptions
Gemini 3.8 Flash, Gemini 3.7 Flash, and Gemini 3.6 Flash are currently shown at Google's introductory pricing through December 31, 2026. Standard pricing takes effect January 1, 2027. Verify current pricing with Google before making purchasing decisions.
Provider pricing shown by TokensCalc is for estimation and comparison purposes. Pricing information was last reviewed on September 20, 2026. Provider prices may change, and actual costs can differ because of caching, batch processing, tools, context, volume pricing, and other provider-specific billing rules.
Always verify the current price with the relevant provider before making purchasing or production budgeting decisions.
Language models do not normally process an entire sentence as one indivisible piece. Text is converted into smaller units called tokens before being processed by the model. Depending on the tokenizer and the text, a token can represent part of a word, a complete short word, punctuation, whitespace, or another piece of text.
This matters because many AI APIs measure usage in tokens rather than characters or words. A longer prompt generally requires more input tokens, while a longer generated response generally requires more output tokens.
Token counts are not identical across every model. Different model families can use different tokenizers, which means that the same text may produce different token counts depending on the model being used.
Input tokens represent the information sent to the model. This can include a user prompt, system instructions, conversation history, retrieved documents, and other information included in a request.
Applications that repeatedly send large amounts of context can therefore accumulate substantial input-token usage.
Output tokens represent the generated response from the model. The amount depends on the requested task, instructions, model behavior, output limits, and the response itself.
When estimating API expenses, it is useful to model input and output usage separately because providers commonly publish separate rates for them.
A simple monthly API estimate can be calculated by considering the tokens used per request, the number of requests, and the provider's applicable price per token.
For example, if an application sends 10,000 requests each month, every request contains an estimated 1,000 input tokens, and each response contains an estimated 500 output tokens, the application would process approximately 10 million input tokens and 5 million output tokens during that month.
The actual bill depends on the pricing rules of the provider and model being used. Additional features such as cached input, batch processing, tools, or other billable services may affect the final amount.
Review prompts and application context for instructions, examples, or repeated information that does not contribute to the requested result.
If an application only needs a short structured response, avoid requesting unnecessarily long explanations. Expected output length can have a significant effect on total usage.
Applications that repeatedly send the same large instructions or documents should investigate provider features such as prompt caching when those features are available.
Record representative prompts, output lengths, request volumes, and actual provider usage before making architectural changes. Estimates are most useful when they are compared with real application measurements.
TokensCalc is designed as a planning tool. It does not submit your prompt to OpenAI, Anthropic, Google, or another model provider to obtain an exact tokenizer result.
The token estimate is based on a simple heuristic using the length and word count of the supplied text. This makes the tool fast and suitable for rough comparisons, but it cannot reproduce every provider's tokenizer.
For production billing calculations, developers should compare estimates with the usage information reported by their chosen API provider. Provider documentation and current pricing pages should be treated as the authoritative source for actual billing.
No. TokensCalc provides an estimate using a lightweight heuristic. Exact token counts depend on the tokenizer used by the model and the complete API request.
The calculator's token estimation runs in the browser. It does not need to send the entered text to an AI model provider to perform its estimate.
Real requests can contain additional context, system instructions, tools, cached content, or other billable components that are not represented by a simple prompt estimate.
It can be used for preliminary budgeting and comparisons. Before committing to a production budget, verify current model prices and compare the estimate with actual usage data from your provider.
Important variables include input tokens per request, output tokens per request, request volume, model pricing, cached content, and any additional API features used by the application.
Not necessarily across different models. Tokenization depends on the tokenizer used by the particular model or model family.