@rich dagger i am using gpt 4o and it has high latency of 2s and i am giving 1000 tokens of system prompt can you tell me why
#gpt 4o high latency
lethal cloud
rich daggerBOT
Hi @lethal cloud. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
The high latency you're experiencing with GPT-4o (about 2 seconds) is primarily due to two factors:
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Model Size and Complexity: GPT-4o is a large, highly capable model. Larger models inherently process tokens more slowly than smaller ones, especially when compared to models like GPT-4o-mini or GPT-3.5. The model's complexity means it takes longer to generate each token, and this effect is amplified with longer prompts and outputs Production best practices – Improving latencies.
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Number of Tokens in the Prompt: While reducing input tokens does help, the impact is relatively minor unless you're working with very large prompts. Cutting 50% of your prompt may only yield a 1–5% latency improvement. For a 1000-token system prompt, the main latency still comes from the model's token generation step, not from processing the prompt itself Latency optimization – Use fewer input tokens.
Key points from OpenAI documentation:
- The bulk of latency typically arises from the token generation step, not from processing the prompt tokens.
- Generating tokens is much slower than reading the prompt; longer generations accumulate more latency.
- Using a smaller model (like GPT-4o-mini) or reducing the number of output tokens will have a much greater impact on latency than reducing the prompt size alone Production best practices – Common factors affecting latency.
What you can do:
- If possible, use a smaller model for tasks that don't require GPT-4o's full capabilities.
- Limit the number of output tokens requested.
- Use streaming to show tokens as they are generated, which can improve perceived latency.
- Only optimize prompt length if your context is extremely large (several thousand tokens).
In summary, your 2s latency is expected for a large model like GPT-4o with a 1000-token prompt. The main bottleneck is the model's token generation speed, not the prompt size. For significant latency improvements, consider model selection and output length adjustments Latency optimization.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
lethal cloud
@rich dagger i am using gpt 4o for voice agent so latecy is so metter in this case. and 1000 token prompt is normal. i am from india and the people from US they had told me that they have 600ms latency on same condition so could you tell me how can i achive that latency like them
@rich dagger i am using gpt 4o for voice agent so latecy is so metter in this case. and 1000 token prompt is normal. i am from india and the people from US they had told me that they have 600ms latency on same condition so could you tell me how can i achive that latency like them
rich daggerBOT
Let me check the knowledge base for you again. One moment! :books:
Your observation is correct: for voice agents, latency is critical, and a 1000-token system prompt is common. If users in the US are seeing ~600ms latency with the same setup, but you are experiencing ~2s latency from India, the main difference is likely network latency due to geographic distance.
Key points from OpenAI documentation:
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Server Location: OpenAI’s servers are currently located in the US. If your infrastructure (client or backend) is far from the US, network round-trip time will add significant latency. The documentation specifically notes:
"Our servers are currently located in the US. ... you could consider locating the relevant parts of your infrastructure in the US to minimize the roundtrip time between your servers and the OpenAI servers."
Production best practices – Common factors affecting latency -
Model and Token Factors: The model and number of tokens generated are the main contributors to processing latency, but network latency can be a major factor when operating from outside the US.
How to reduce your latency:
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Move your infrastructure closer to the US:
Host your application backend (the part that calls the OpenAI API) on a US-based server or cloud region. This will minimize the network round-trip time to OpenAI’s servers. -
Optimize for streaming:
Use streaming responses (stream: true) so you can start processing audio as soon as the first tokens are available, improving perceived latency for voice agents Production best practices – Common factors affecting latency.
- Prompt and output optimization:
While a 1000-token prompt is normal, ensure you are not generating more output tokens than necessary, as generation time is the main source of model-side latency.
Summary:
The main reason for your higher latency is likely the network distance between India and the US-based OpenAI servers. To achieve latency similar to US users, run your API-calling backend in the US. This is a standard approach for latency-sensitive applications using OpenAI models from outside the US.