LLM Context Window Token & Cost Estimator
Estimate token usage, context budget, and approximate cost before sending prompts to a large language model.
Estimator
Results
Metrics
No estimate generated.
Introduction
Large language models have finite context windows. Long prompts, documentation, and examples can exceed the available budget or increase cost more than expected.
This estimator helps you plan prompt size before sending content to an API or AI assistant.
How it works
The tool analyzes characters, words, lines, and UTF-8 bytes. It then applies content-type heuristics to produce low, primary, and high token estimates.
- Plain text uses an approximate four-character-per-token baseline.
- Code uses a denser estimate because symbols tokenize differently.
- JSON uses a middle estimate due to punctuation and structure.
- Cost calculations use only the prices you enter.
How to use
- Paste your prompt or document.
- Select or enter a context-window size.
- Reserve expected output tokens.
- Optionally enter pricing per 1,000,000 tokens.
- Run the estimate and review the remaining budget.
Practical example
If a prompt estimates 12,000 tokens and you reserve 2,000 output tokens in a 32,768 token context, the prompt budget is 30,768 tokens. The tool reports roughly 39% usage and about 18,768 tokens remaining.
Use cases
- Preparing long system prompts.
- Estimating support-document summaries.
- Checking whether code snippets fit.
- Planning retrieval-augmented prompts.
- Budgeting API usage for repeated calls.
Best practices
- Leave a safety margin below the context limit.
- Summarize long source material before prompting.
- Reserve enough output tokens for complete answers.
- Test with the actual model tokenizer when precision matters.
- Use your current provider pricing for cost estimates.
Common mistakes
- Assuming one word equals one token.
- Forgetting output tokens when calculating budget.
- Pasting entire repositories without filtering.
- Using outdated pricing.
- Ignoring tokenizer differences between models.
Limitations
- The tool does not call any LLM API.
- It does not use a model-specific tokenizer.
- Cost estimates depend on user-entered pricing.
- Non-English text and code may tokenize differently.
- Provider billing may include minimums, caching, or other factors.
Browser compatibility
The tool works in current versions of major browsers using standard JavaScript text processing.
FAQ
Is my prompt uploaded?
No. All estimation happens locally in your browser.
Are the token counts exact?
No. They are heuristic planning estimates. Use a model-specific tokenizer when you need exact counts.
How is cost calculated?
Cost uses the prices you enter per 1,000,000 tokens. If no pricing is entered, only token and context estimates are shown.
Should I reserve output tokens?
Yes. Reserving output tokens gives a more realistic prompt budget, especially for long completions.