Pricing
- Input Tokens: $0.2 /M tokens
- Output Tokens: $0.2 /M tokens
- Cache Read: $0 /M tokens
Input Modalities
Try this model
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AIHUBMIX_API_KEY"],
base_url="https://aihubmix.com/v1",
)
response = client.chat.completions.create(
model="gemma-3n-e4b-it",
messages=[
{
"role": "user",
"content": "Hello, how are you?"
}
],
max_tokens=1024,
stream=False,
)
print(response.choices[0].message.content)Frequently asked questions
What is Gemma 3n E4B It?
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More models from Google
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Gemini 3.8 Flash-Lite TTS (gemini-3.8-flash-lite-tts) is Google's fast, cost-efficient workhorse text-to-speech model, built to replace gemini-3.1-flash-tts-preview for high-throughput production workloads.
Gemini 3.8 Flash TTS (gemini-3.8-flash-tts) is Google's 3.8 Flash text-to-speech audio model, designed to deliver studio-grade sound fidelity, expressive performance, and long-form stability.
- Input: $ 0.75 /M
- Output: $ 3.75 /M
- Web Search: $0.014/request
- Cache Storage: $0.5/h/M tokens
- Input Audio: $1/M tokens
- Input Video: $1/M tokens
Gemini 3.8 Flash is Google's most intelligent Flash-series model, designed for long-running software engineering tasks, autonomous agents, and complex enterprise workflows, while retaining the Flash series' fast responsiveness and cost-effectiveness.
- Input: $ 0.75 /M
- Output: $ 3.75 /M
- Web Search: $0.014/request
- Cache Storage: $1/h/M tokens
- Input Audio: $1/M tokens
- Input Video: $1/M tokens
Gemini 3.7 Flash is Google’s natively multimodal reasoning model for coding, agents, web development, and knowledge work. It supports a 1M-token context window and adjustable thinking levels. Compared with Gemini 3.6 Flash, it improves coding, tool use, multi-step planning, and instruction following.
- Input: $ 1.5 /M
- Output: $ 7.5 /M
- Web Search: $0.014/request
- Cache Storage: $1/h/M tokens
- Input Audio: $1/M tokens
- Input Video: $1/M tokens
Gemini 3.6 Flash provides sustained frontier-level intelligence optimized for real-world tasks at a higher speed and lower cost. Designed for the agentic era, it excels at code generation, agentic execution, and spatial reasoning. This model is particularly effective for rapid agentic loops involving complex coding cycles and iterations.
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