Methodology
A transparent text-to-token heuristic estimates the prompt size, which is multiplied by executions and the model's input price.
Token and API Costs
Estimate the recurring input cost of a pasted prompt across executions and source-linked model prices.
Interactive Tool
| Result | Value | How to read it |
|---|---|---|
| Characters | 78 | Including whitespace and punctuation. |
| Words | 13 | Whitespace-delimited word count. |
| Estimated tokens | 20 | Planning heuristic: approximately four characters per token. |
| Recurring input tokens | 2M | 100,000 executions. |
| Estimated monthly input cost | $20.00 | $10.00 / 1M for Claude Fable 5. |
| Context used | 0% | Against 1M tokens. |
This is a transparent planning heuristic, not a tokenizer-exact count. Use provider usage fields or the exact tokenizer for billing reconciliation.
Inputs
| Input | How it is used |
|---|---|
| Prompt text | The system prompt, schema, or repeated instruction block. |
| Executions | How often the prompt is sent each month. |
| Model | Supplies sourced input pricing and context capacity. |
A transparent text-to-token heuristic estimates the prompt size, which is multiplied by executions and the model's input price.
Use the delta between prompt revisions rather than treating the estimate as an invoice. Provider usage fields remain the billing source of truth.
Boundaries
Catalog values retain their source and freshness on the linked model, provider, benchmark, or comparison page. Editable scenario assumptions are not Model Markets measurements.
Continue the Analysis
| Tool | Next question |
|---|---|
| Token Counter | Get a fast planning estimate before sending a prompt or document to a tokenizer-backed API. |
| Caching Savings | Separate reusable prefix economics from total input volume before adopting prompt caching. |
| Coding Agent Cost | Convert an agent's observed hourly token burn into a monthly cost scenario. |
Questions
Make the cost of a system prompt, tool schema, or repeated instruction visible before scale. It returns estimated prompt tokens, per-call input cost, monthly recurring cost, and annualized spend.
Where the calculation needs model facts, it uses the current Model Markets catalog snapshot updated 2026-09-02. User-entered assumptions remain clearly editable, and unsupported values stay unknown rather than being inferred.
Exact tokenization varies by model. Tool and message wrappers can add tokens outside the pasted text. Caching can materially reduce repeated-prefix cost when eligibility rules are met. Open the linked canonical records and primary sources before making a production or purchasing decision.