AI Context Window Visualizer
Compare token limits across 14 major AI models. Paste your text to instantly see which models can fit your content — and which can't.
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What Fits in Common Context Windows
| Context Size | Approx Words | What Fits |
|---|---|---|
| 4K | ~3,000 | A few pages of text, short code files, simple chatbot turns |
| 16K | ~12,000 | A short book chapter, small codebase, multi-turn chat history |
| 32K | ~24,000 | A short novel chapter, a full module of code, long research paper |
| 128K | ~96,000 | A 300-page book, 100+ code files, full spec + implementation |
| 200K | ~150,000 | ~400 pages, a medium codebase, an entire product documentation set |
| 1M+ | ~750,000+ | All 7 Harry Potter books, 1,000+ code files, hours of transcripts |
Frequently Asked Questions
What is a context window and why does it matter?▼
A context window is the total number of tokens an LLM can process in a single request, combining input (system prompt + conversation history + documents) and output (the model's reply). If your total tokens exceed the context window, the model truncates old context or rejects the request. Choosing a model with the right context window size prevents costly failures in production.
How do I count tokens?▼
The standard approximation is 1 token ≈ 4 characters or about 0.75 words for English text. Code is often denser — Python/JS typically runs 3-4 chars/token. This tool uses the 4 chars/token approximation. For exact counts, use tiktoken (OpenAI) or the Anthropic tokenizer API. Gemini and other non-OpenAI models have similar but not identical tokenizers.
What fits in 1 million tokens?▼
1 million tokens ≈ 750,000 words ≈ the entirety of Harry Potter (all 7 books). Practically: a full codebase of ~1,000 files, a 700-page technical manual, or ~10 hours of meeting transcripts. Gemini 1.5 Pro and Claude 3.5 support 1M+ tokens, enabling whole-repository analysis in a single call.
What are input vs output token limits?▼
Most models have separate input and output limits. Claude 3.5 Sonnet allows 200K input tokens but only 8,192 output tokens. GPT-4o allows 128K input but 16,384 output. Output limits matter for long-form generation tasks (documents, code, analyses). If you need longer outputs, use streaming and chain multiple calls.
Does a larger context window cost more?▼
Yes. Pricing is typically per input token and per output token. A 100K-token prompt costs roughly 100x more than a 1K-token prompt at the same per-token rate. Using prompt caching (Anthropic/OpenAI) can reduce repeated large-context costs by 50-90%. Always benchmark your actual context usage before choosing a model tier.
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