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Paste a prompt or document and see whether it fits inside the context window of ten major models (GPT, Claude, Gemini, Llama, Grok, and DeepSeek) as a visual fill bar rather than a number you have to interpret.
See how your prompt fills each model's context window. Paste your text and set your expected output to instantly check which models can handle your workload and how much room is left.
A context window is the total budget for input and output combined, and that catches people out constantly. If your prompt fills 95% of the window, there is no room left for the model to answer. You get a truncated response or an outright error, and the failure looks like a model problem rather than an arithmetic one. The visual fill makes the remaining headroom obvious.
The advertised window is also not the effective one. Retrieval accuracy degrades toward the middle of long contexts across every model family that has been measured: the needle-in-a-haystack behavior that shows strong recall at the start and end of the input and weaker recall in between. A two-million-token window is not permission to stop doing retrieval; it is permission to stop chunking so aggressively.
The practical rule that follows: put the most important material at the beginning or the end of your context, not buried in the middle. If you are stuffing a long document and getting inconsistent answers about content from its centre, that is the effect you are seeing, and no amount of prompt rewording fixes it.