What is context window packing?

Packing means concatenating several short inputs into one prompt so a single API call processes many documents at once. It reduces the number of round trips, which cuts per-request overhead and latency, especially for classification or extraction over many tiny inputs.

Does packing actually reduce token cost?

Not the raw content tokens — you still pay for every document's tokens. What packing saves is call overhead: fixed system-prompt tokens repeated per call, network round trips and rate-limit pressure. It also lets the model share one system prompt across many documents.

Why reserve tokens from the window?

You must leave room for the system prompt and for the model's response. If you fill the entire input window you have nothing left for output. The reserved field subtracts those tokens before computing how many documents fit.

Is there a downside to packing too many documents?

Yes. Very long prompts can dilute attention, increase the chance of cross-document confusion, and make output parsing harder. Keep batches modest and use clear separators so the model can tell documents apart.

Is my data sent anywhere?

No. Every calculation runs entirely in your browser. Nothing you enter is uploaded, stored or logged.

Context Window Packing Optimizer

Context window packing optimizer

When you run an LLM over thousands of short documents — tagging support tickets, classifying reviews, extracting fields from records — sending one API call per document is wasteful. Each call repeats your system prompt, pays network overhead and burns rate limit. Packing several documents into one context window call fixes that. This tool tells you exactly how many short documents fit in a single call and how many calls you save.

How it works

The model’s context window has a fixed size, but you cannot use all of it for documents. You must reserve tokens for your system prompt and for the model’s output. The usable window is:

usable = context_window − reserved_tokens

Each document occupies its own tokens plus a small separator / instruction overhead (a delimiter like --- or a per-item instruction). So the number of documents that fit per call is:

docs_per_call = floor( usable ÷ (doc_tokens + separator_tokens) )

Dividing your total document count by that gives the packed call count. The gap between that and one-call-per-document is your saving.

Tips and notes

Packing reduces calls, not raw content tokens — you still pay for every document’s tokens. The real wins are fewer repeated system prompts, less network round-trip latency and far less rate-limit pressure. Use clear, consistent separators so the model never confuses one document with the next, and ask for a structured output (one result per document, in order) so you can split the response reliably. Don’t over-pack: extremely long prompts can dilute the model’s attention and make individual results less accurate. Start with a modest batch size, verify accuracy holds, then scale up.