Context Window Fit Checker
Paste any document or enter a token count — see exactly which models can handle it, at what cost, and with what "lost in the middle" risk.
How to Use
Paste your document
Paste or type your document, prompt, or codebase. Token count is computed automatically using the o200k_base tokenizer (GPT-4o compatible).
Or enter token count
If you already know the token count, enter it directly to skip tokenization. Useful for large files that exceed what you can paste.
See which models fit
The table shows all active models sorted by fit status. Green = fits. Red = too large. "% context used" and "lost in the middle" risk are shown for every model.
Consider "lost in the middle"
Research shows LLMs recall content at the beginning and end of context well, but poorly recall content in the middle. High % context usage means higher lost-in-the-middle risk.
Context window vs "lost in the middle"
Just because a document fits in a context window doesn't mean the model will recall all of it accurately. Research shows recall drops sharply for content in the middle of long contexts. Practically: if your document fills more than 60% of the context window, consider chunking it and using RAG, or selecting a model whose window gives you more headroom. The RAG Cost Calculator can help compare the cost trade-off.
How to Cut This Cost
Use RAG instead of stuffing the full document. Retrieve only the relevant 3–5 chunks — typically 1,000–3,000 tokens — instead of the full 50K document.
Compress document context with a summarization step before sending. Lossy but fast — works well for background context, not verbatim citation tasks.
Split large documents and process in parallel with a cheaper model, then merge results. Avoid paying frontier model prices for mechanical extraction.
FAQ
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Last verified: 2026-08-20 · methodology · data sources