Does ChatGPT-4 Offer Extended Token Limits Compared to ChatGPT-3.5?

Below is a summary addressing your concerns:

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ChatGPT-4 Context Window vs. ChatGPT-3.5

• ChatGPT-4 Capabilities:
  The ChatGPT-4 models now support an 8K token limit in the standard version (with some versions supporting even larger windows, such as 32K). In contrast, ChatGPT-3.5 is capped around 4K tokens.

• Implications:
  A larger context window means you can supply longer inputs and expect longer outputs. However, even with 8K tokens available, both your input and the output share that token space.

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Why Your Outputs May Be Abruptly Ending

• Output Limits vs. Token Management:
  When you see completions stop at around 900–1,000 tokens, it is often because the overall token usage (input + output) is approaching the model’s configured or inherent output limits. The models reserve part of the context window for internal “reasoning tokens,” which aren’t always visible in your messages but count toward the total.

• Token Truncation:
  Especially in situations with complex prompts or multi-turn conversations, the assistant may truncate part of the context to stay within limits. This can make it seem like the output ends abruptly. In some API configurations, you get to set parameters like max_completion_tokens, which affect the length of the generated output.

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Tips for More Consistent Completions

• nailing down prompt structure:
  There isn’t a one-size-fits-all “perfect prompt,” but here are some best practices:

  • Be clear and explicit: Clearly define boundaries for code blocks (e.g., using markdown code fences) and specify what you expect from the output.
  • Manage conversation context: If the conversation gets too long, consider summarizing or resetting the context to reduce noise.
  • Set parameters (in API usage): Use parameters like max_prompt_tokens and max_completion_tokens to control how much of the token window is allocated to each.

• Handling Abrupt Completions:
  When you encounter incomplete outputs, consider:

  • Reducing unnecessary context: Keep only the most relevant parts of previous messages.
  • Breaking down complex prompts: Divide your task into smaller, manageable steps so that each run stays comfortably within the token limits.
  • Explicit instructions: Remind the model to “continue with the previous output” if it stops short. For example, you can use prompts like, “Continue from where you left off,” to signal that the answer was truncated and should be resumed.

• Generating Code:
  Since you use the assistant for code generation:

  • Clearly delimit code sections.
  • Ask the assistant to produce the code in a specific format (e.g., “please output just the code block” or “wrap the code in markdown markers”).
  • Consider requesting incremental outputs if the code is extensive.

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Final Thoughts

Although larger context windows in ChatGPT-4 can aid in more detailed responses, both ChatGPT-4 and ChatGPT-3.5 have inherent limits imposed by the token window. The balance between input, output, and internal reasoning tokens often means you need to carefully manage the context to avoid abrupt truncations.

There isn’t a universal “perfect prompt” to work around these limitations—all gains tend to come from iterative testing and adjusting based on your specific use case. Tweaking your parameters for context management and clarifying your intent in the prompt can lead to a more consistent output, especially when generating complex code.

Happy coding, and I hope these insights help you get better results from your interactions with the models!