Unexpected Repeated Message Generation in GPT-3.5-Turbo-16k Causes Excessive Token Usage

Below is an overview addressing your observations and questions. Note that this response is based solely on publicly available documentation and our technical guidelines.

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What’s Happening with GPT-3.5-turbo vs. GPT-4?

• Differences in Model Behavior:
  The two models may behave differently regarding token limits and how they handle long-running runs. GPT-3.5-turbo can have stricter constraints on message length or automatic truncation strategies that may lead to the repeated generation of output or partial replies.

• Token Management and Truncation:
  In scenarios where the conversation or message exceeds the maximum context length, the API automatically truncates the input. When using auto truncation and response formatting, the GPT-3.5-turbo model might attempt several completions to “finish” the response. This leads to multiple messages under the same run, which in turn multiplies token usage. GPT-4 appears more resilient to this behavior, which is why you noticed no similar issues there.

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Why Is the Model Creating Multiple Messages for One Run?

• Repeated Attempts in Response to Excess Tokens or Timeouts:
  If the request is nearing the token limit or faces a timeout, the model may generate multiple messages to try to complete the output. Some of these attempts may be repeated within the same run (showing the same run_id) because the API implementation continues sending output until halted by an explicit cancellation or rate limit error.

• Auto-Response Format Settings:
  The current setup (using response_format set to “auto”) might be allowing the model to output continuously until it reaches a hard limit. This can result in more than one message when a single “finish” isn’t clearly defined. Our documentation recommends when using JSON mode or a fixed response format, you must explicitly instruct the model to adhere to that format to avoid such behavior.

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Is It Normal for a Run to Create Several Messages?

• Expected vs. Observed Behavior:
  Under normal circumstances—especially in a controlled session where you expect one response message per run—this is not the intended behavior. You should see only one response message per run unless the conversation is long enough to split the output or if a retry happens due to a transient error.

• Unusual Token Consumption:
  The average of 7.2 messages per run suggests that multiple regeneration attempts or repeated outputs are being recorded. This is not typical for a properly configured call and might indicate unintended behavior when using GPT-3.5-turbo under your specific high-throughput scenario.

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How Can This Be Prevented?

• Tighten Response Formatting Instructions:
  Explicitly instruct the assistant (via a system or user message) to produce only one final output in the desired format (e.g., JSON mode). For example:

  MARKDOWNCopied
  SYSTEM: Please respond with a single JSON object containing the analysis result. Do not produce any stream of additional messages.

• Monitor and Sync Runs:
  Consider waiting for a run to complete (or at least successfully start) before initiating another to avoid overlapping requests that might interfere with one another.

• Review Token Limits & Timeout Settings:
  Ensure that your input plus expected output fall well within the token limits. If automatic truncation is being triggered, reduce input size or adjust the settings (like max_completion_tokens) accordingly.

• Rate Limit Considerations:
  The “rate_limit_exceeded” errors indicate that some runs might be getting retried automatically. Implement back-off or rate-limit handling in your client to prevent repeated attempts within a short window.

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About Costs, Failed Runs, and Refunds

• Cost Implications:
  Token usage is calculated based on consumption regardless of completion status. If a run produces tokens—even if the run later fails or gets cancelled—those tokens are counted.

• Refunds and Billing Disputes:
  For billing inquiries or refund requests, including situations involving failed runs or unexpected token usage, you should reach out directly to OpenAI support through your account dashboard. Provide as much detail as possible (logs, run identifiers, etc.) so we can investigate the issue further.

• Beta Software Considerations:
  While some APIs may be labeled “beta” and subject to iterative improvements, unexpected behavior like multiple repeated messages is not considered standard operation. Your feedback and detailed logs are valuable; contacting OpenAI support is the recommended channel to ensure the issue is escalated and resolved.

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Summary

• Issue Cause: Likely a combination of token management, truncation behavior, and auto-response format settings specific to the GPT-3.5-turbo model.
• Mitigation Steps: Adjust your response instructions, manage rate limits, and review token settings to prevent repeated message generation.
• Billing Concerns: Reach out to OpenAI support regarding refunds or credits for unexpectedly high token usage.

For further technical support or to provide your detailed logs for investigation, please contact the OpenAI support team via the developer dashboard or the designated support channel. They can help clarify and troubleshoot any inconsistencies related to the Assistant API behavior.

I hope this helps clarify the situation and guides you toward a resolution.