Why Is the Assistants API Slower Than Standard GPT-4 Calls for Real-Time Chatbots?

I'm noticing that using the assistant API for chat interactions results in delays of 4–8 seconds for a short prompt and response, which contrasts sharply with the 1–2 seconds typical of standard GPT-4 calls.

This latency makes the API seem impractical for real-time chatbots or production use cases. I'm trying to understand whether this is due to inherent overhead (such as additional steps like file loading and stateful orchestration) or if it’s just because the assistant API is still new.

Below is an example of my code with timing logs:

PYTHONCopied
import time 
from openai import OpenAI

def assistant_response( 
        input_message: str, 
        assistant_id: str, 
        thread_id: str, 
        player_profile_path: str 
):

    # 1. Load assistant.
    print("2. Load assistant.")
    s = time.time()
    assistant = client.beta.assistants.retrieve(assistant_id=assistant_id)
    e = time.time()
    print(e - s)

    if len(assistant.file_ids) < 1:
        s = time.time()
        # 2. Createn an open AI file object 
        print("1. Createn an open AI file object ")
        file = client.files.create(
            file=open(player_profile_path, "rb"),
            purpose='assistants'
        )
        e = time.time()
        print(e - s)

    # 3. Load conversation thread based on player ID. 
    print("3. Load conversation thread based on player ID.")
    s = time.time()
    thread = client.beta.threads.retrieve(thread_id=thread_id)
    e = time.time()
    print(e - s)

    # 4. Add new message to thread. 
    print("4. Add new message to thread. ")
    s = time.time()
    message = client.beta.threads.messages.create(
        thread_id=thread.id,
        role="user",
        content=input_message
        # file_ids=assistant.file_ids
    )
    e = time.time()
    print(e - s)

    print("4. Waiting for run to finish. ")
    s = time.time()
    run = openai.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
    )

    while run.status != "completed":
        run = openai.beta.threads.runs.retrieve(
            thread_id=thread.id,
            run_id=run.id
        )
        print(run.status)

    messages = openai.beta.threads.messages.list(
    thread_id=thread.id
    )
    e = time.time()
    print(e - s)

    return messages.data[0].content[0].text.value

The output log shows:

BASHCopied
2. Load assistant. 
0.18107199668884277 
3. Load conversation thread based on player ID. 
0.1474926471710205 
4. Add new message to thread. 
0.2909998893737793 
4. Waiting for run to finish. 
queued 
queued
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
in_progress 
7.238811016082764 

Total elapsed time: 7.8624725341796875

I’m looking for insights on whether the delay is inherently due to the API’s multi-step, stateful processing or if there might be ways to optimize it. Any thoughts on how to achieve lower latency for production use cases?