Context Window Visualizer

How to Use the Context Window Visualizer

Paste your text

Drop in the prompt, document or conversation you plan to send. The exact GPT token count appears at the top and updates as you edit.

Scan the bars

Each model shows a bar for how much of its context window your text fills, with the exact percentage and the window size beside it.

Spot what doesn't fit

If your text exceeds a model's window the bar turns red and shows how many tokens you're over — a clear signal to trim, chunk, or pick a bigger-window model.

Leave room for the answer

The window holds your input and the model's reply together. Keep your input well under 100% so there's space for the response you want back.

The Context Window Is a Budget, Not a Suggestion

What "context window" actually means

A model's context window is the maximum number of tokens it can consider at once — and crucially, that budget covers everything in the exchange: your system prompt, the conversation history, any documents you paste, and the model's own reply. It is not just an input limit. Send a prompt that fills 100% of the window and there is literally no room left for an answer; the request gets truncated or rejected. Visualising your text against each model's window turns an abstract number ("1M tokens") into a concrete picture of how much headroom you actually have.

The windows are not interchangeable, either. They range from GPT-4o's 128K tokens to the 1M-class windows of GPT-5, Claude and Gemini — roughly an eightfold difference. A document that's a rounding error in a 1M window can overflow a 128K one entirely. That's why this tool prices your text against every model side by side: the same paste that "fits comfortably" in one model is "over by 40,000 tokens" in another, and seeing both at once is how you choose the right model for the job.

"The window holds the question and the answer. Fill it with your prompt and you've left the model nowhere to reply."

Why GPT is exact and the others are estimates

OpenAI open-sourced its tokenizer (tiktoken), so this tool can split your text exactly the way a GPT model will — the GPT bars are precise. Anthropic and Google use their own tokenizers and don't publish a browser library, so for Claude, Gemini and Mistral we apply the tiktoken count as a close approximation and mark those bars est. In practice a given string is often ~15–20% more tokens on Claude than on a GPT tokenizer, so treat a Claude bar near the top of its range with caution and confirm with that provider's own token-counting endpoint when you're close to the edge. For the raw count alone, use our Token Counter; to split oversized text into windows that fit, use the Text Chunker.

10 Facts About Context Windows

01

The context window is measured in tokens and covers input and output together.

02

Fill it entirely with your prompt and there's no room left for the reply.

03

Windows range from GPT-4o's 128K to the 1M-class windows of GPT-5, Claude and Gemini.

04

"1M" isn't one number — Claude is 1,000,000, Gemini 1,048,576, GPT-5.x ~1,050,000.

05

GPT-5.4 mini and nano have 400K windows — much smaller than the flagship.

06

Exceeding the window means your input is truncated or rejected, not summarised.

07

Models can degrade on very long context — fitting isn't the same as using it well.

08

OpenAI's tiktoken is open source, so GPT token counts can be computed exactly.

09

The same text is often ~15–20% more tokens on Claude than on a GPT tokenizer.

10

This visualizer runs entirely in your browser — your text is never uploaded.

Frequently Asked Questions

It's the maximum number of tokens a model can process in one request, covering your system prompt, conversation history, pasted documents and the model's reply combined. If the total exceeds the window, the request is truncated or rejected.
Yes. The context window is shared between input and output, so the reply is carved out of the same budget. If your input fills the window, there's no room for an answer — always leave headroom for the response you expect.
OpenAI publishes its tokenizer (tiktoken), so GPT counts are computed exactly in your browser. Anthropic and Google don't release a browser tokenizer, so for Claude, Gemini and Mistral the tool applies the tiktoken count as a close approximation and labels those bars "est."
Usually within about 15–20%. A given string often tokenises to somewhat more tokens on Claude than on a GPT tokenizer. So if an estimated bar sits near the top of a model's window, confirm with that provider's own token-counting endpoint before relying on it.
Either pick a model with a larger window, trim the input, or split it into pieces that each fit. Our Text Chunker can break long text into overlapping chunks sized to a token budget, which is the standard approach for RAG and long-document workflows.
Not always. Models can lose accuracy on very long context, and a huge prompt costs more and runs slower. Fitting within the window is necessary but not sufficient — concise, well-structured context usually beats simply filling the window.
The "1M" figure is approximate marketing. The exact windows differ: Claude is 1,000,000, Gemini 2.5 is 1,048,576, and OpenAI's GPT-5.x are around 1,050,000. This tool uses each model's exact window so the percentages are right, not a shared round number.
No. The tokenizer runs entirely in your browser from a locally-served library. Your text is never uploaded to any server or model; the only network request is your browser fetching the tokenizer file from our own domain.
The exact tokenizer table loads on first use to keep the page fast. You'll briefly see a character-based estimate, then the exact GPT count snaps in. After that first load it's instant for the rest of your session.
Completely free, with no account or sign-up, and no limit on use. It runs in your browser and collects no data.

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