AI Workbench
Context Window Calculator
Subtract system, prompt, history, and tool tokens from a model context window.
Inputs
Change values
Related
Related tools
API example
Use this tool from code.
API access is free during beta, no key required, and rate-limited for reliability.
Request
POST endpoint
POST /api/tools/context-window-calculator
Content-Type: application/json
{
"inputs": {
"contextWindow": 128000,
"systemTokens": 1800,
"promptTokens": 4200,
"historyTokens": 36000,
"toolTokens": 12000
}
}Response
Example output
{
"tool": "context-window-calculator",
"result": {
"summary": "74,000 tokens remain for output and safety margin.",
"outputs": [
{
"label": "Used tokens",
"value": "54,000"
},
{
"label": "Remaining tokens",
"value": "74,000"
},
{
"label": "Context used",
"value": "42.19%"
}
]
}
}About this tool
Context Window Calculator guide
How to use the Context Window Calculator
Subtract system, prompt, history, and tool tokens from a model context window. Use this AI workbench utility when you need to estimate cost, shape prompts, or plan model workflows without building a spreadsheet from scratch. Enter realistic values for context window tokens, system tokens, prompt tokens, history tokens, tool/result tokens, then run the tool and compare the output against the decision you are trying to make. The example starts with context window tokens of 128000, system tokens of 1800, prompt tokens of 4200, history tokens of 36000, but the stronger workflow is to change one input at a time so you can see which assumption actually drives the result.
What the result means
The output is a planning aid for model work, not a guarantee from any provider. Tokenizers, model pricing, latency, context handling, and provider limits vary, so the result should be treated as a close operating estimate until checked against the model and API you will actually use. The useful signal is often not just the headline number; it is how much that number changes when one input moves. If the result is fragile, document the assumption and rerun the calculator with a conservative case before using it in a plan, report, trade, launch, or implementation decision.
When to use this AI tool
Use it while scoping automations, preparing prompts, pricing batch runs, chunking transcripts, estimating vector storage, or building model-powered product features. It is most useful before implementation when you need to know whether an idea is cheap enough, fast enough, or clear enough to build. This page fits searches such as context window, llm context, token budget because it keeps the fields visible, loads a working example, and returns copy-ready output without sign-up. Use the result to tighten your next question, narrow a range, or decide whether a more detailed model is worth building.
Common mistakes to avoid
Do not paste private prompts, customer data, credentials, or proprietary datasets into an online tool unless you control the deployment. Watch for hidden costs such as retries, tool calls, embeddings, failed requests, moderation, image variants, transcription minutes, and long context windows. Keep the input assumptions with the output so the number is explainable later. A clean result with hidden assumptions is worse than a rough result with clear assumptions, because nobody can audit what changed when the real-world numbers move.
How to verify the output
Confirm important estimates against current provider docs, API responses, usage dashboards, model-specific tokenization, and staging traffic before committing budget or product promises. If the result will influence money, production systems, customer promises, or public claims, rerun it with cautious values and check the relevant source data. Good utility tools speed up judgment; they should not hide the judgment step.
FAQ
Questions about this tool
Is this AI estimate exact?
No. It is a deterministic estimate based on the values you enter. Real-world systems, providers, markets, and reporting tools may use different rules or fresher data.
Which input should I adjust first?
Start with context window tokens, then change system tokens. Moving one input at a time makes it easier to see which assumption has the largest effect on the output.
Can I use this result for an important decision?
Use it for planning and scoping. Confirm production costs, limits, latency, and token counts with the provider and model you intend to run.
Why does my result differ from another tool?
Different tools may round differently, include different assumptions, or use a different source of truth. Compare the inputs and definitions before comparing the final number.