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© 2026 QuickToolz. Made with ❤️, Built for Speed.

Version: 3.1.0

Developed by Heliconia Solutions Pvt. Ltd.
  1. Home
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  3. /Context Window Visualizer
AI Tools

Context Window Visualizer

Free context window visualizer — see how much fits in GPT, Claude, Gemini, and Llama context windows.

Context window visualizer

25,800
Used
174,200
Remaining
183,700
Available prompt
200,000
Model cap
Claude 3.5 Sonnet — 200,000 ctxHealthy · 12.9% used
System300
Prompt9,500
Examples1,200
Retrieved12,000
Tools800
Output reserve2,000
Remaining174,200

Budget rule

Keep 15-25% headroom in context windows. Retrieval chunks, tool schemas, and output reserve can crowd the prompt faster than expected — especially on 8k and 32k models.

Was Context Window Visualizer useful?

Your vote helps us prioritize improvements.

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Overview

About Context Window Visualizer

The QuickToolz Context Window Visualizer shows how much text (books, transcripts, code) fits inside each model’s context window. Drop in a sample doc and see whether it fits in GPT-4o (128k), Claude 3.5 Sonnet (200k), Gemini 1.5 Pro (2M), or Llama 3.1 (128k).

Features

What makes Context Window Visualizer great

Everything you need, nothing you don’t. Built for speed and simplicity.

  • Visual bar

    Instantly see how much window your prompt eats.

  • All major models

    GPT-4o, GPT-5, Claude 3.5, Gemini 1.5/2, Llama 3.1, Mistral Large.

  • Overflow warnings

    Highlighted when your input exceeds the chosen window.

How to use

Get started with the Context Window Visualizer in just seconds.

Everything you need, nothing you don’t. Built for speed and simplicity.

  1. 01

    Paste or upload text

    Drop in a document, transcript, or codebase sample.

  2. 02

FAQ

Frequently asked questions about Context Window Visualizer.

Got questions? We’ve got answers. Common questions about Context Window Visualizer.

GPT-4o supports 128 000 input tokens with up to 16 384 output tokens.

Gemini 1.5 Pro currently leads at up to 2 000 000 tokens, followed by Claude 3.5 (200k) and GPT-5/4o (128k).

No — recall degrades in the middle of very long contexts ("lost in the middle"). RAG and careful prompt design often beat naive long-context stuffing.

Pick a model

Compare context windows across providers.

  • 03

    See fit

    Visual bar shows tokens used vs. capacity.