Why Does gpt-4-0125-preview Generate Tokens Slower Than gpt-4-0613?

Below is some context and potential reasons for the performance difference you’re observing:

Possible Factors Affecting Token Speed

• Server and Infrastructure Variations:
  While both models share many similarities in their interface, they may be running on different server configurations or be optimized differently behind the scenes. Differences in hardware utilization or current server load could lead to variations in token generation speed.

• Model Architecture and Optimization Trade-Offs:
  The preview turbo model is designed with different optimization goals in mind (such as improved instruction following in certain scenarios). These adjustments may lead to subtle performance trade-offs. In other words, while the preview turbo might excel in some tasks, other aspects—like raw token generation speed—could be impacted.

• Processing in JSON Mode:
  Querying in JSON mode, which is often used in tandem with function calling, can introduce additional overhead. If one mode incurs more processing compared to another, that might contribute to the token rate differences you’re seeing.

What to Consider Moving Forward

1. Load Variability:
   It might help to run your tests over an extended period or at different times to see if the speeds remain consistent. Variability in server load or ongoing updates can sometimes affect performance in small-scale testing.

2. Model-Specific Optimizations:
   The documentation indicates that each model variant is designed with certain capabilities and trade-offs in mind. If your application primarily benefits from faster token generation, it might be helpful to benchmark the models under your typical workload conditions.

3. Feedback on Preview Models:
   Since the turbo model you’re using is in preview, your experience and performance metrics are valuable. Sharing your findings through the appropriate feedback channels could help improve future iterations.

Final Thoughts

Ultimately, while informal benchmarks can provide insights, the differences in observed speeds can be attributed to several factors—from server infrastructure differences to the additional overhead of JSON processing. If the performance impact is significant for your application, it may be worthwhile to experiment with different configurations or reach out to OpenAI support for more detailed insights.

Let me know if you have any follow-up questions or need more information.