Context Window Visualizer

About Context Window Visualizer

Which model fits your document? Paste a paper, meeting notes or code — or drag the token slider — and this tool draws every major model's context window as pure-CSS bars: percentage used, fits/overflows, and how many copies it holds. The spread is bigger than most intuitions: from Gemma 3's 128K to Llama 4 Scout's 10M — an 8K paper is nothing anywhere, but a 100K novel already knocks out a chunk of the field. Window data shares its source with the API cost calculator; all math runs locally in your browser.

FAQ

Is a bigger context window always better?

Not necessarily. Huge windows often come with higher per-token pricing (especially beyond standard tiers), and most models lose retrieval accuracy over very long inputs — the known "lost in the middle" effect. The sane heuristic: fit the document plus expected output with some conversation headroom. For whole-corpus workloads, RAG chunking is usually both more accurate and cheaper than brute-forcing the window.

Why is the real headroom smaller than the advertised window?

The advertised window is a shared input+output budget: system prompts, chat history and tool-call results all live inside it, and output needs generation headroom. This tool's percentage measures "your document's share of the window" — treat it as a capacity gauge, not a bill forecast.

Is the token count from pasted text exact?

It's an instant ratio-based estimate (labelled as such) — fine for capacity calls. For exact counts use the companion Tokenizer Visualizer, which loads OpenAI's public tokenizer and counts piece by piece. The two tools are deliberately differentiated and cross-linked.