Calculator
Paste your prompt, set expected output, pick an estimation method, and optionally add pricing. Submit to see results above.
Example data table
| Scenario | Words | Chars | Prompt tokens (est.) | Output tokens | Total tokens | Context window |
|---|---|---|---|---|---|---|
| Short instruction | 120 | 650 | 200 | 250 | 450 | 8,192 |
| Detailed brief | 900 | 4,600 | 1,300 | 800 | 2,100 | 16,384 |
| Long policy prompt | 3,200 | 16,000 | 4,000 | 1,200 | 5,200 | 8,192 |
Values are illustrative; tokenization varies by language, symbols, and formatting.
Formula used
- Words = estimated by splitting the prompt text.
- Chars = number of characters in the prompt text.
- Tokens (word-based) = ceil(Words × TokensPerWord).
- Tokens (char-based) = ceil(Chars ÷ CharsPerToken).
- PromptTokens = chosen method (max, avg, word-only, char-only).
- TotalTokens = PromptTokens + ExpectedOutputTokens.
- SafeLimit = floor(ContextWindow × (1 − Buffer%)).
- InputCost = (PromptTokens ÷ 1000) × InputPricePer1K.
- OutputCost = (ExpectedOutputTokens ÷ 1000) × OutputPricePer1K.
- TotalCost = InputCost + OutputCost.
How to use this calculator
- Paste your prompt text in the textarea.
- Enter expected output tokens for the response length.
- Set the model context window and a safety buffer.
- Pick an estimation method; conservative is recommended.
- Optional: enter input and output prices per 1K tokens.
- Press Submit to see results above the form.
- Download CSV or PDF if you need a shareable report.
Why token length matters
Long prompts increase latency because tokenization, attention, and post processing scale with total tokens. This estimator converts your text into two token approximations—word based and character based—and then applies the method you select. Comparing both estimates highlights prompts packed with symbols, tables, code blocks, or mixed languages. The results help you decide whether to compress instructions, move examples to files, or summarize context. It flags when formatting inflates token counts.
Context window planning
Context budgeting prevents truncation, incomplete reasoning, and tool failures. The calculator adds estimated prompt tokens to expected output tokens, then checks the total against a safe limit derived from the context window and a buffer percentage. The buffer accounts for system text, formatting, and any tool schemas. If you exceed the safe limit, reduce output targets, split tasks into steps, or chunk documents. Aim to keep a few hundred tokens free for safety.
Estimating cost reliably
Cost forecasting improves governance when teams run many experiments. Enter separate rates for input and output per thousand tokens, and the estimator computes each component plus the total. This split matters because output is often priced higher, and long answers can dominate spend even with short prompts. Pair the estimate with batch counts to approximate monthly budgets and spot expensive templates early. Treat the value as a planning number, not an invoice.
Choosing an estimation method
Method selection should match your risk tolerance and prompt style. Conservative mode chooses the larger estimate and is best for production workflows, compliance prompts, and tool calls where retries are costly. Balanced mode averages the two signals and suits rapid drafting. Word mode fits plain prose, while character mode better reflects prompts with JSON, long identifiers, or dense punctuation. Recalibrate ratios with your own samples. Consistency matters more than perfection for comparisons over time.
Using estimates in operations
Operationally, teams use these numbers to standardize templates and set guardrails. Track typical prompt tokens, output targets, and remaining headroom per use case, then document thresholds for auto truncation, summarization, or retrieval. Use the example table as a baseline for scenarios and compare changes after edits. Exporting CSV and PDF supports reviews, budgeting approvals, and experiment logs shared across stakeholders. Over time, this creates performance standards for your org.
FAQs
How accurate are the token estimates?
They are approximations based on word and character heuristics. Real tokenization depends on language, punctuation, and encoding. Use the estimate for planning headroom and cost, then validate with a small sample using your provider’s tokenizer when precision matters.
Which estimation method should I pick?
Use Conservative for production prompts, tool calls, or mixed content like code and JSON. Use Balanced for drafting and experimentation. Word-only works for plain prose, while Char-only is helpful when prompts contain long identifiers or many symbols.
What safety buffer should I set?
Ten percent is a practical starting point for most workflows. Increase the buffer when you add system instructions, tools, or structured outputs. If you see truncation or errors near limits, raise the buffer or reduce expected output tokens.
Why does output token planning matter so much?
Many models generate longer answers than expected, and output often costs more per token. Setting a realistic output target reduces overspend and avoids context overflow. If you need long responses, split tasks, summarize inputs, or request concise formats.
How can I reduce prompt tokens without losing quality?
Remove redundant instructions, replace long examples with short patterns, and reference external context via retrieval instead of pasting entire documents. Use bullet constraints, templates, and stepwise prompting. Summarize earlier messages into compact notes before continuing.
How do I use CSV and PDF exports?
After submitting, download CSV for spreadsheets and experiment logs. Download PDF for reviews, approvals, or sharing a snapshot with teammates. Exports capture key totals, limits, pricing inputs, and the chosen estimation method for traceability.