Context-Window Calculator
See whether your prompt plus retrieved context fits the major models’ context windows — and what to do when it overflows.
What you’re sending
Does it fit?
Don’t just reach for a bigger window. Retrieve the few most relevant chunks instead of the whole document, summarise long sections, or split the task — focused context usually beats a stuffed one, and costs less. That’s what a retrieval system is for.
Estimates. Token counts are approximate (~4 chars/token) and context-window sizes are dated June 2026 — confirm with the provider. A fit here is necessary, not sufficient: long-context recall degrades toward the middle.
Good to know
How are tokens estimated?
From the text you paste (~4 characters per token), or you can enter a token count directly. It’s an estimate; verify billing-critical numbers with the model’s own tokenizer.
What’s “output reserve”?
A model’s context window is shared between your input and its answer. Reserve room for the output (e.g. 1,000–4,000 tokens) so the model can actually respond.
Why does fitting in a huge window not guarantee good answers?
Models recall information less reliably when it’s buried in the middle of a very long context. Retrieving the few most relevant chunks usually beats stuffing the whole document in.
Want this guaranteed in your own infrastructure?
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Retrieve the right context, not all of it.
RAGSuite retrieves the most relevant, citation-backed passages for each query — so you stay well inside the window and the answer stays grounded.