Context Window Comparison 2026: GPT vs Claude vs Gemini
How context windows differ across major AI models in 2026, what the numbers mean in pages, and why a bigger window is not always better.
The numbers in perspective
Vendors change limits often, so check current documentation. As a rule of thumb, 100,000 tokens is a long novel, and million-token windows can hold entire codebases or document collections.
Bigger is not a free pass
Long-context models still show weaker recall for information buried in the middle of huge prompts, and every token costs money and latency. Trimming noise helps every model.
- Clean PDFs with PDF to Clean Text
- Measure with the Token Counter
- Trim with the Context Compressor
Choose by task
For a single long contract, any large-window model works. For cross-document analysis, a massive window helps. For quick questions, smaller and faster models with focused context are often the better value.
Try it free in your browser
- PDF to Clean Text — Strips headers, footers, page numbers, and hyphenation. Outputs TXT or Markdown.
- Context Compressor — Reduce token usage with compression strategies tuned for LLMs.
- Token Counter — Live token, character, and context-window meter for any prompt. Uses official tiktoken encoders.
Tools mentioned
Frequently asked
How many pages fit in 128K tokens?
Roughly 200 to 300 pages of typical prose, depending on formatting and density.
Does a bigger window mean better answers?
Not automatically. Details in the middle of very long inputs can be overlooked, so focused input still wins.
Keep reading
How to Prepare PDFs for ChatGPT, Claude, and Gemini
A practical guide to extracting clean, AI-ready text from PDFs — born-digital and scanned — so ChatGPT, Claude, and Gemini answer accurately and don't waste tokens on headers, footers, and page numbers.
The Best Way to Feed Long Documents to Claude (and Other Long-Context Models)
Claude's 200K-token context is generous, but you'll still want to clean, compress, and structure long documents before sending them. Here's a step-by-step playbook.
Cleaning Whisper Transcripts for AI Summaries
OpenAI Whisper, Otter, and YouTube transcripts are full of timestamps, filler words, and speaker noise. Here's how to strip them before sending to ChatGPT or Claude — and why it matters.