Context Estimator Guide

Choose practical context windows for AI chats before you load a local model or paste a long document.

Quick Navigation

→ Overview → Basic Workflow → Model Profiles → Reserve → Statuses → Privacy

1. Overview

Context Estimator gives you a fast estimate of how much AI chat context a piece of text or a document may need. It is useful before choosing settings such as 4K, 8K, 16K, 32K, 64K, or 128K in tools like Ollama, LM Studio, or local browser chat.

Estimate, not exact count: Different models tokenize text differently. The tool gives a practical planning estimate without downloading tokenizers.

2. Basic Workflow

1

Paste or upload

Paste text into the input area or choose a local TXT, DOCX, or PDF file.

2

Choose profile and reserve

Select the closest model profile and keep a reserve for system prompt, answer, formatting, and reasoning.

3

Read the comparison table

Use the row marked recommended as the minimum practical context window. If the fit is tight, choose the next larger window.

3. Model Profiles

Generic LLM

A safe default when you do not know which tokenizer your model uses.

Model families

Llama / Mistral, Qwen, Gemma, and GPT-style profiles adjust the estimate for common tokenization patterns.

4. Reserve

A context window must hold the source text, your instruction, system prompt, and generated answer. The reserve slider keeps part of the window free before making a recommendation.

The default 35% is a practical starting point. Increase it for detailed answers or reasoning. Lower it only when you expect a short answer.

5. Statuses

Too small / Tight

The input may overflow, or there may be little room left for a useful answer.

Good / Overkill

Good is the practical target. Overkill means the window is likely larger than needed.

6. Privacy and Limits

Text extraction and estimation run locally in your browser. Pasted text and uploaded files are not sent to ASD123.ai for processing, and document contents are not saved in localStorage or IndexedDB.

PDF extraction depends on readable embedded text. Scanned, image-only, password-protected, or unusual PDFs may need OCR or manual cleanup before estimation.