Why Does gpt-4-vision-preview Return Truncated Output Despite Being Within Token Limits?

Below is some guidance regarding this behavior:

Understanding the Issue

• The fact that you see the finish_details field set to "max_tokens" means that during generation the model hit what it considers to be its token limit. This is why the output gets cut off at a short length.
• Even if your request appears to be well within the documented token limits, there can be subtleties in how token usage is calculated. Every element in the conversation (including roles, system messages, or extra formatting) contributes to overall token consumption.

Possible Explanations

• Internal Token Accounting: There may be additional internal tokens (beyond what you see in your request details) that push the generation over an internal threshold.
• Preview-Specific Behavior: Since you’re using the gpt-4-vision-preview, it’s possible that the preview release has more conservative limits or different handling of tokens during generation. This might cause early termination even when it appears you are within limits.
• Hidden Formatting or Management Overhead: The model may add behind-the-scenes tokens for formatting or context management which contribute to the overall token count, leading to an earlier-than-expected cut-off.

Next Steps

• Review Token Usage: Double-check that there isn’t any hidden or additional message content (e.g., system instructions or extra tokens added by the preview interface) that could be adding unexpected token overhead.
• Monitor Announcements: Since preview features can have evolving behavior, it might be useful to follow official updates or community discussions (like on the OpenAI forums) to see if other developers are experiencing similar issues.
• Contact Support: If the behavior persists and you believe it’s not due to any accidental overage in your request design, consider reaching out to OpenAI support or posting on the OpenAI community forums with specific details of your case.

This behavior isn’t uncommon when working with early or preview versions—the internal mechanisms can sometimes differ from the strict token limits seen in documentation for the stable models.

If you have any more specific technical details or logs you’d like help reviewing, feel free to share them (while respecting any confidentiality guidelines).