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Context Window Calculator

Paste text or enter a token count to see how it measures against each model’s context window, with a clear fits / does-not-fit verdict per model. Helps you pick a model that can hold your whole prompt.

Token count is estimated (~4 chars/token). For an exact figure use the token counter.

Estimated size: 0 tokens

Claude Fable 5Fits · 0% used

Context window: 1M tokens

Claude Opus 4.8Fits · 0% used

Context window: 1M tokens

Claude Sonnet 4.6Fits · 0% used

Context window: 1M tokens

Claude Haiku 4.5Fits · 0% used

Context window: 200K tokens

GPT-5.5Fits · 0% used

Context window: 400K tokens

GPT-5.4Fits · 0% used

Context window: 1M tokens

GPT-4.1Fits · 0% used

Context window: 1M tokens

GPT-4.1 miniFits · 0% used

Context window: 1M tokens

GPT-4.1 nanoFits · 0% used

Context window: 1M tokens

Gemini 2.5 ProFits · 0% used

Context window: 1M tokens

Gemini 2.5 FlashFits · 0% used

Context window: 1M tokens

Remember the context window must hold both your input and the model’s output, so leave headroom for the response. Estimated text counts use ~4 characters per token; exact tokenisation varies by model.

This context window calculator tells you whether your text will fit inside each model’s context window. Paste your prompt or enter a token count, and see a clear fits / too-large verdict per model, with a bar showing how much of the window you’d use.

It answers the everyday question “will my prompt fit?” without trial and error — handy when you’re working with long documents, transcripts or codebases.

What is a context window?

A context window is the maximum number of tokens a model can process in a single request — your input plus the output it generates. If your input is larger than the window, the model simply can’t see all of it at once, so you’d need to split or summarise it.

How to estimate tokens

As a rule of thumb, English text is about four characters — roughly three-quarters of a word — per token. So 1,000 words is around 1,300 tokens. This tool estimates from your text; for an exact figure, use the token counter, which runs a real tokenizer.

Leaving room for the response

The window holds input and output together, so never fill it completely with your prompt — leave headroom for the model’s answer. If you’re close to the limit, choose a model with a larger window, trim the prompt, or move reference material into retrieval rather than the prompt itself.

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