Tokens & Context Windows
Understanding AI's memory limits and how to work within them
What is a token?
AI models don't process text character by character or word by word — they use tokens. A token is roughly ¾ of a word in English.
"Hello" = 1 token. "Hello, world!" = 3 tokens. "I am using AItheGuru.in" = 6 tokens.
Why does this matter? Models are priced per token (input + output), and they have a maximum number of tokens they can handle in one conversation.
Context windows explained
The context window is how much text a model can "see" at once — like working memory. Everything in the conversation (your messages + the AI's responses) counts against this limit.
GPT-4o: 128K tokens ≈ 96,000 words ≈ a 300-page book Claude 3.5: 200K tokens ≈ 150,000 words ≈ a 500-page book Gemini 1.5 Pro: 1M tokens ≈ 750,000 words ≈ 4 novels
Once you hit the limit, the model starts "forgetting" older parts of the conversation.
Practical implications
For long documents: Claude or Gemini with large context windows handle full books or codebases. GPT-4o handles most business documents comfortably.
For cost: Longer contexts cost more. If you're building an app with an LLM backend, manage context size carefully.
For conversations: Long chats degrade in quality as older context is compressed or dropped. For complex projects, start fresh conversations for distinct tasks.
Token counting tip: Use platform.openai.com/tokenizer to see exactly how many tokens your text uses.