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Context Window

Definition

The maximum amount of text an AI model can process at once, measured in tokens. GPT-4o has 128K tokens.

Why It Matters

Whatever you put in the prompt, instructions, document, chat history, retrieved snippets, has to fit in the context window. Overflow it and you either truncate (losing information) or chunk + retrieve (adds complexity). Context window is the single number that bounds how much background a model can hold in mind at once.

Key Points

  • 1 token ≈ 4 characters of English, ~1.5 tokens per character in code, ~2–3 tokens per character in Asian scripts.
  • KV cache for the context window grows linearly with length, a 200K-token context on a 70B model can require 40–80 GB of VRAM for the cache alone.
  • Most models show degraded recall for information placed in the middle of very long contexts, the 'lost in the middle' phenomenon documented in 2023.
  • GPT-4o: 128K tokens. Claude 3.5 Sonnet: 200K. Qwen 2.5 72B: 128K. Gemini 1.5 Pro: 1M (experimental).
  • Prompt-caching APIs (Anthropic, Google) charge 90 %+ less for repeated prefix tokens, cost-critical for long system prompts sent on every call.

Example

GPT-4o has 128K tokens of context (~96K words / ~300 pages). Claude 3.5 Sonnet has 200K. Qwen 2.5 7B has 32K. Reading a novel one chapter at a time fits comfortably in 32K; analysing the whole novel in one shot needs 100K+.

Common Misconception

Context window size is not the same as reliable usable context. Most models exhibit substantially degraded fact-retrieval accuracy for content placed in the middle 60–80 % of a very long context. If precise recall of a specific passage matters, use RAG rather than relying on long-context stuffing.

Related Terms

  • TokenThe basic unit of text processing in AI models. Roughly 1 token = 4 characters of English text. Used for billing and context limits.
  • LLM (Large Language Model)A neural network trained on massive text datasets that can generate, understand and manipulate human language. Examples: GPT-4, Qwen, Claude.
  • RAG (Retrieval-Augmented Generation)A technique where AI retrieves relevant documents before generating a response, improving accuracy.

Context Window on Rewind.ai

Every model on Rewind.ai shows its context window in the picker. The chat UI auto-trims older turns when you approach the limit; RAG is the alternative when "just paste it" isn't viable.

Explore the Tools

FAQ

Context Window on Rewind.ai is a free AI tool. There's no charge and no sign up needed to start.

Yes. You get 2,500 free tokens per day to use Context Window and every other tool on Rewind.ai. A free account raises that to 5,000 tokens/day. You can buy more starting at $1.

Context Window runs open-source AI models on our GPU servers. Send your request and the result comes back in seconds.

No. You can use Context Window right away without signing up. A free account doubles your daily usage to 5,000 tokens and saves your history.

Anonymous users get 2,500 tokens/day. Free accounts get 5,000 tokens/day. Tokens reset every 24 hours. Each generation costs ~100-5,000 tokens depending on the operation.

Your data is processed on our servers and isn't stored permanently unless you choose to save it. We don't sell or share it.

Yes. Content from Context Window is yours to use for personal or commercial work. The AI models we run are commercially licensed.

Context Window matches the quality of paid services because it runs the latest open-source AI models. The difference is you don't pay per use.

Context Window runs open-source AI models including Qwen 2.5, FLUX and Whisper. We update to newer models as they ship.

Yes. Context Window works in any mobile browser, and the layout adapts to your screen size.

Sign up for a free account to get 5,000 tokens/day, double the anonymous limit. Or buy token packs starting at $5 for 200,000 tokens. See /pricing/ for all options.

Yes. After you generate content, you can download it, copy it, or share it via a unique link. Signed-in users can also view their generation history.

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