Key Takeaways
- Context window = total tokens the model can see at once. Claude 3 leads with 200K tokens (roughly 150,000 words).
- The 'lost in the middle' problem means models pay less attention to content in the center of long contexts. Place critical info at the start or end.
- Larger context windows do not eliminate the need for RAG: retrieval still improves accuracy by surfacing the most relevant information.
Context Window Sizes Compared
| Model | Context Window | Equivalent |
|---|---|---|
| GPT-4o | 128K tokens | ~300 pages of text |
| Claude 3.5 Sonnet | 200K tokens | ~500 pages of text |
| Gemini 1.5 Pro | 1M tokens | ~2,500 pages of text |
| Llama 3 (8B) | 8K tokens | ~20 pages of text |
Larger context windows are useful for document analysis (processing entire contracts, reports, or codebases), but cost scales linearly with tokens. Processing 200K tokens of context is 20x more expensive than processing 10K tokens.
Frequently Asked Questions
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