Choose the right model for long input
Use it before summarizing books, contracts, transcripts, meeting notes, code repositories, or large research folders. The table shows which models can fit the work without chunking.
AI Tools · Free browser utility
Context Window Comparator
Compare AI model context windows and estimate what can fit before you paste a long PDF, transcript, codebase, research bundle, or chat history into a model.
Approximate conversions: 1 token ≈ 0.75 word, 1 page ≈ 500 words, 1 line of code ≈ 8 tokens. Reserve output tokens because context includes both prompt and response.
Context window chart
How much fits?
Conversion table
| Model | Context | Max output | Approx words | Approx pages | Approx LOC | Fit |
|---|
Why this tool is useful
Avoid silent truncation
Some chat apps remove old conversation or document text when limits are reached. Planning the context budget helps you keep the important material inside the prompt.
Leave room for the answer
A model needs output space to write a useful response. The output reserve field helps you compare total prompt plus answer size, not only the input document.
Plan chunking and retrieval
If no model fits, split the document by section, use retrieval, or summarize first. A smaller, relevant context is often better than dumping everything into one request.
How to use the comparator
- Enter the workload size. Use tokens if you know them, or estimate with words, pages, or lines of code.
- Set output reserve. Keep 8K to 32K tokens for summaries, structured reports, code generation, or multi-step analysis.
- Filter by vendor. Compare only the providers you can actually use in your project.
- Check the Fit column. Models marked Fit can handle the input plus the reserved output budget in one request.
- Use log scale for big gaps. Log scale makes 128K, 400K, 1M, and 2M models easier to compare on the same chart.
How it works
This utility runs as static HTML, CSS, and vanilla JavaScript. It uses local model metadata, converts rough pages and words into tokens, and highlights models that can fit the requested workload plus an output reserve.
Model limits and pricing change frequently. Last reviewed: April 28, 2026. Verify against official vendor documentation before making production or paid-campaign decisions.
Frequently asked questions
What is a context window?
A context window is the maximum amount of input and output tokens a model can consider in one request.
Does a larger context window mean better answers?
Not automatically. Larger context helps fit more material, but quality still depends on relevance, ordering, prompt structure, and reasoning ability.
Why reserve output tokens?
Most model limits include both your prompt and the model response. If your input fills the entire window, the model has little room to answer.
What happens when I exceed the limit?
Most APIs reject the request or require you to shorten the input. Some apps truncate earlier history or document sections.
Are page and word estimates exact?
No. Token counts vary by language, formatting, code, tables, and tokenizer. Use these numbers for planning, then confirm exact tokens with the token counter.