Pricing
- Input Tokens: $0.088 /M tokens
- Output Tokens: $0.385 /M tokens
- Cache Read: $0.011 /M tokens
Input Modalities
Output Modalities
- Text
Context length
- 262K tokens
Max output
- 131K tokens
Try this model
Python
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="deepinfra-gemma-4-26b-a4b-it",
messages=[
{
"role": "user",
"content": "Hello, how are you?"
}
],
max_tokens=1024,
stream=False,
)
print(response.choices[0].message.content)Gemini 3.8 Flash-Lite TTS (gemini-3.8-flash-lite-tts) is Google's fast and affordable flagship text-to-speech model, optimized for high throughput, low latency, and cost-effective conversational speech.
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.
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.
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.
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.
Input price: $0.25/M, Text output: $1.5/M, Image output: $30/M
Google's newest, most compact, and most cost-effective image generation and editing model, designed for large-scale use.
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