Token Checker

Quickly estimate token counts for prompts and responses to help developers, AI engineers, and researchers plan usage, costs, and model behavior.

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About This Tool

Token Checker provides quick tokenization estimates for text inputs used with AI models. It accepts plain text, code-like prompts, or doc snippets and returns the total token count, an estimated token budget for the selected model, and guidance on rounding costs. The tool targets developers, data scientists, product managers, and researchers who need visibility into token budgets before running experiments, deploying features, or negotiating API limits. By turning raw text into token units, it helps teams anticipate usage patterns and avoid unexpected overages.
Conceptually, it applies model-specific tokenization rules to count tokens rather than words. It supports common encodings and can simulate counts for multiple models to compare relative efficiency. Core features include precise per-input token counts, estimated cost per 1k tokens, optional breakdown by input segments, and exportable outputs in JSON or CSV for integration with dashboards and data pipelines.
Inputs include text (string, required), model (string, optional), and price_per_1k_tokens (decimal, optional). Outputs include token_count (integer), estimated_cost (decimal), model_token_rate (decimal), and a breakdown by segment if enabled. Formatting adheres to standard numeric forms.
Token counts follow tokenizer rules per model family; costs computed as token_count * price_per_1k_tokens / 1000, with rounding to two decimals. If model is not specified, a default model is used for generic estimates.
Handles empty input by returning zero tokens. If text exceeds practical limits, it truncates for reporting while preserving an exact token_count for full text. If price data is unavailable, estimates fall back to a neutral rate (0).
Supports English-language prompts; currency in USD by default, with optional currency override. Units use tokens as abstract units; locale-sensitive formatting is not required unless requested.
Assumes standard GPT-like tokenization for estimation; not a replacement for official API token counts. Excludes real-time API constraints or rate limits; no UI references here.

How to Use

  1. Provide inputs: paste the text or prompts to analyze.
  2. Select model/tokenization rules: choose the model family or encoding to apply.
  3. Run calculation: the tool computes token counts and estimated costs.
  4. Review outputs: view total tokens, cost estimates, and per-segment breakdown if enabled.
  5. Export results: download as JSON or CSV for reporting.
How to use token checker online

Frequently Asked Questions

Find Quick Answers

What inputs does Token Checker accept?
It accepts plain text, code-like prompts, and an optional model name to tailor tokenization. A price per 1,000 tokens may be supplied to generate cost estimates. If the model is omitted, a generic baseline is used for budgeting.
How are tokens counted?
Tokens are counted using model-specific tokenizers rather than word counts. The tool segments input text according to encoding rules and sums tokens across the entire input, returning a total plus optional per-segment breakdown.
What models are supported?
Supports common GPT-style tokenizers and accepts a user-specified model name to adjust tokenizer behavior. When a model is selected, the tokenizer rules align with that model to improve estimate accuracy.
How are costs calculated?
Costs are token_count × price_per_1k_tokens ÷ 1000 and rounded to two decimals. If price data is missing, the estimate defaults to zero. These are estimates and not exact API charges.

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