How Do GPT-3.5 and GPT-4 API Response Times Compare on OpenAI and Azure?

Below is a summary discussion on API latency considerations based on your experiments and related OpenAI API documentation:

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Latency Depends on Output Token Count

As you noted, the overall response time is heavily influenced by the number of output tokens generated. This aligns with the guidance for optimizing latencies with the API. Even small improvements, like reducing field name lengths in JSON outputs, can yield notable performance gains—especially when using models like GPT-4 where each token may add roughly 196 ms of latency.

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Model-Specific Observations

• OpenAI gpt-3.5-turbo (73 ms/token):
  The measurements suggest that the OpenAI version of GPT-3.5-turbo processes tokens relatively slower than the Azure variant.

• Azure gpt-3.5-turbo (34 ms/token):
  With a considerably lower token processing time, Azure’s offering could be preferable when low latency is crucial.

• OpenAI gpt-4 (196 ms/token):
  As expected, GPT-4 shows higher latency per token. This means that when using GPT-4, minimizing output tokens (and possibly structuring responses to reduce verbosity) can be particularly beneficial.

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Tradeoffs and Optimization Strategies

1. Request Splitting vs. Single Request:
   One approach mentioned in the technical documentation involves splitting the request into two sequential calls to optimize token processing speed, even if it increases the total number of requests.

2. Output Token Minimization:
   Experimenting with ways to reduce the raw number of output tokens—for example, by using efficient JSON field names or eliminating unnecessary text—can lead to improved response times, a benefit that is amplified with GPT-4.

3. Use Case Considerations:
   The choice often depends on the specific application. For interactive scenarios or customer service use cases, ensuring the right balance between output quality and response speed is key. Sometimes a model with a higher token cost (like GPT-4) may be justified if its higher quality output offsets the latency.

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Practical Example

If running a request with Azure using gpt-3.5-turbo and expecting around 600 output tokens, the approximate latency would be:

PLAINTEXTCopied
34 ms/token x 600 tokens = 20,400 ms or roughly 20.4 seconds.

This simple calculation can help you adjust either the model choice or the formatting of the response to meet your application's latency requirements.

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By taking these measurements into account and leveraging the optimization strategies provided in the API documentation, developers can tailor their integrations to better handle API slowness issues. Let me know if you need further details or assistance on any specific aspect of the API performance!