Token Limit
A token limit is the maximum number of tokens a model can read in a single request, prompt plus response combined.
What is a Token Limit?
A token limit is the maximum context window an LLM can process at once, measured in tokens (roughly 0.75 words per token). This constraint fundamentally shapes how AI search engines retrieve and synthesize information.
Token Limits by Platform
- GPT-4 Turbo: 128,000 tokens (~96,000 words)
- Claude 3.5 Sonnet: 200,000 tokens (~150,000 words)
- Gemini 1.5 Pro: 2,000,000 tokens (~1.5 million words)
- Perplexity Pro: Varies by underlying model
Why Token Limits Matter
Token limits directly impact:
- Source Selection: How many documents can be included in RAG retrieval
- Attention Quality: Longer contexts dilute attention weights
- Response Time: Larger contexts require more inference compute
- Citation Behavior: Models may prioritize early vs. late context differently
Token Limit Implications for AEO
Understanding token constraints helps you optimize:
- Content Length: Concise, high-value content fits more sources in context
- Information Hierarchy: Place key facts early (before context gets truncated)
- Source Authority: High-authority sources more likely to be included despite limits
- Conversational Search: Multi-turn queries consume token budget quickly
The Token Economy
As models with larger token limits emerge:
- More full research becomes possible
- Long-form content may gain competitive advantage
- Computation costs increase (affecting platform economics)
- Grounding strategies must adapt to larger contexts
Token limits are evolving rapidly, but understanding their current constraints remains critical for effective AEO strategy.
Related Terms
Model Temperature
Model temperature is the dial that controls how random an AI model's output is. Low for predictable, high for creative.
What Is a Context Window?
The context window is the maximum number of tokens an AI model can read and reason over in a single request.
Large Language Model (LLM)
A large language model is an AI trained on huge amounts of text to predict the next token, which is enough to make it read, write, and reason in plain language.
Token
A token is the smallest piece of text an AI model reads at a time. Sometimes a word, often a fragment of one.
