Interactive Context Window Calculator
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Launch Context Window Calculator1. The Rise of Million-Token Windows
Google's Gemini 1.5 Pro was the first model to support a 2 million token context window. This allows sending 1.5 million words of text, hours of video, or entire codebases. This simplifies RAG architectures, as developers can place entire documents directly in the prompt.
2. Recall Degradation (lost in the middle)
Large context windows are not perfect. In needle-in-a-haystack recall tests, models retrieve information placed at the very beginning or end of the prompt with 100% accuracy. However, recall can drop to 70-80% for facts buried in the middle of long contexts.
3. Latency and Cost Implications
Processing a 1 million token context has significant latency. The prefill phase can take 10 to 30 seconds before the model starts generating its first output token. In addition, filling the context window is expensive, requiring careful budget management.
Frequently Asked Questions
Should I use long-context instead of RAG?
For small or highly coupled datasets (like a single project directory), long-context is easier and more accurate. For massive datasets, RAG remains cheaper and faster.
Which model has the largest context window?
Google's Gemini 1.5 Pro supports 2 million tokens, while Claude 3.5 Sonnet supports 200,000 tokens.