I’m building a large language model translation program and need to make many concurrent calls to the Gemini model in a short period. Initially, I used the OpenAI-compatible mode, but frequently received numerous 429 errors (“Too Many Requests”). After switching to Gemini’s native API, the 429 errors became much less frequent. I’m unsure if this is due to my coding skills or some issue with the OpenAI-compatible API. Could you help me analyze this?
Using the compatible mode makes switching models easier, but the native mode seems to offer more parameters.
The behavior you’re observing is expected and actually highlights a good technical insight. The native Gemini API generally provides better concurrency handling and rate limiting compared to the OpenAI-compatible mode, which is primarily designed for compatibility rather than optimal performance.
Here’s a quick comparison:
# Native Gemini API - Better concurrency handling
import google.generativeai as genai
genai.configure(api_key='YOUR_API_KEY')
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(text)
# OpenAI-compatible mode - More portable but potentially limited
from openai import OpenAI
client = OpenAI(base_url="https://gemini.example.com/v1", api_key="YOUR_API_KEY")
response = client.chat.completions.create(model="gemini-pro", messages=[{"role": "user", "content": text}])
For high-concurrency applications, sticking with the native API is the better choice, even though it means less portability across different LLM providers.