ToolBento guide
How to estimate AI prompt tokens and cost before you send
Plan LLM prompts with rough token counts, expected output size, context-window fit, and approximate model cost before calling an AI API.
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Open the matching ToolBento utility →Why token estimates matter
AI models do not price or limit prompts by ordinary word count. They use tokens, which are small chunks of text that vary by model, language, code, emojis, and formatting. Estimating tokens before you send a prompt helps you avoid context-window errors, surprise API bills, and prompts that leave too little room for the answer.
What the ToolBento estimator checks
ToolBento's AI Token Estimator gives a practical planning estimate for input tokens, expected output tokens, total tokens, whether the request fits the selected context window, and an approximate cost when public list-rate style pricing is available for the chosen model family. It also shows basic text stats such as characters and words or segments so you can compare prompt revisions.
Inputs in plain language
Paste your prompt, system message, conversation, code snippet, or draft into the Prompt or conversation text box. Choose a Model family such as GPT-4o, GPT-4o mini, Claude Sonnet, Gemini Flash, Llama / open model, or Generic estimate. Enter Expected output tokens as the rough maximum answer size you want the model to produce, for example 600 for a short response or several thousand for a long report.
Use Estimate tokens before an API call
After filling in the text, model family, and expected output size, press Estimate tokens. The result summarizes the estimated input tokens, expected output tokens, total tokens, context window, and fit status. If it says the request does not fit, reduce pasted context, summarize older conversation turns, or lower the expected output before sending the real request.
How to use the cost estimate
The cost line is useful for planning batches, prototypes, and repeated automations, but it is still an estimate. Actual billing can differ because providers change prices, apply caching rules, bill tool calls differently, or tokenize text with model-specific rules. Treat the number as a quick budget signal, then confirm final pricing in your AI provider dashboard.
Prompt-size cleanup tips
If a prompt is too large, remove duplicate examples, shorten boilerplate instructions, keep only the relevant part of long documents, and replace raw logs with summaries. For code tasks, include the files and errors that matter most instead of pasting the entire repository. Re-run Estimate tokens after each cleanup pass to see whether the prompt now fits.
Privacy and accuracy notes
The estimator is designed for browser-side planning and does not need an account, but you should still avoid pasting production secrets, private keys, or sensitive customer data into any web page. Token counts are approximate rather than exact; exact values depend on the model tokenizer and final request format.