AI Context Window Packer

Prioritize context blocks, reserve response tokens, fit evidence into a model budget, and see what gets included, truncated, or excluded.

Context windowReserve for output

Packed tokens

83 / 6500

Remaining

6417

Context blocks

~30 tokens

~23 tokens

Packed context

System requirements: includedRepository architecture: includedHistorical discussion: included

<context source="System requirements" priority="high">
The answer must cite evidence, distinguish facts from assumptions, and never perform external writes without approval.
</context>

<context source="Repository architecture" priority="high">
The application uses React, an Express static server, client-side tools, and a registry-driven static route generator.
</context>

<context source="Historical discussion" priority="low">
An earlier prototype used a different router and was abandoned after performance testing.
</context>

Private by default: this tool runs entirely in your browser. Nothing you paste is uploaded to Execute.Online.

About AI Context Window Packer & Token Budget Tool

Prioritize context blocks, reserve output tokens, fit evidence into a model context window, and see what is included, truncated, or excluded.

How to use Context Window Packer

1Set the total context window and output reserve.
2Add source blocks with titles and priorities.
3Review which blocks are included, truncated, or excluded.
4Copy or download the packed context.

Frequently Asked Questions

Why reserve output tokens?

The model needs space to produce its answer. Reserving output capacity prevents packed input from consuming the entire context budget.

How are blocks selected?

Non-empty blocks are ordered high, medium, then low priority. They are included until the input budget is exhausted, with the final block truncated if necessary.

Are token counts exact?

Counts use a fast character-based estimate. Exact tokenization varies by model, language, whitespace, and content.

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