Context Window Visualizer
Token count is estimated (≈4 chars/token). Actual counts vary by model and tokenizer.
Input Text
128k
Paste text to visualize context usage
See how much of each model's window your text fills
About
The Context Window Visualizer shows how much of each major LLM's context window your text occupies. Paste any text — a document, a long conversation, a codebase — and see fill percentages across 12 models. Green bars mean plenty of room; amber means nearing the limit; red means the text is too long for that model. Also shows estimated input cost to process the text once.
How to use
- 1 Paste your text into the left textarea.
- 2 Token count, character count, and word count update instantly.
- 3 The right panel shows a fill bar for each model — green (<50%), amber (50–85%), red (>85%).
- 4 "Fits" or "Too long" badge indicates whether the text fits in that model's context window.
- 5 Input cost shows the estimated API cost to process this text once with each model.
- How accurate are the token estimates?
- This tool uses a ~4 characters per token heuristic (English prose). Actual token counts vary by model and content: GPT models use tiktoken (usually 3.5–4.5 chars/token for English), Claude uses byte-level BPE, Gemini uses SentencePiece. For exact counts, use the Token Counter tool with the gpt-tokenizer library.
- What does context window mean?
- The context window (also called context length) is the maximum number of tokens a model can process in a single request — including both the input (your prompt + conversation history) and the output (the model's response). Text longer than the context window must be chunked, summarized, or truncated.
- Why do some models have much larger context windows?
- Larger context windows require more memory (VRAM) during inference due to the KV cache, which stores key-value pairs for each attention head for every token. Gemini's 1M-token window uses architectural innovations like sparse attention and ring attention to distribute the KV cache across hardware.