See how your text fits across every LLM's context window with a live token visualizer
Each highlighted chunk is ~1 token. Darker = more tokens for that word — good signal for long words or non-English text that tokenizes heavily.
Every LLM has a fixed maximum 'context window' — the total tokens it can see at once (input + output). Hitting that ceiling means silent truncation and hallucinations. This tool measures your content across every frontier model simultaneously so you can pick the cheapest one that fits, and visualizes which parts of your text are token-heavy.
Full-codebase Q&A, hour-long meeting transcripts, long-document summarization. Most tasks fit in 100k-200k — beyond that, per-token costs dominate.
Most Western-trained tokenizers allocate 1 token per CJK character, vs. ~4 chars/token for English. Claude and Gemini do slightly better (~1.2 chars/token CJK).
Cached tokens price at 10-25% of standard input tokens, so yes — for repeated system prompts or shared context, enable caching. We don't model cached vs. fresh here; assume fresh.
Yes, common source types: .py .ts .tsx .js .jsx .go .rs .java .rb etc. Token density for code is often higher than prose.
Darker = more tokens for that word — helpful for spotting unusual long words or non-English text that expands heavily.
