Pricing
- Input Tokens: $15 /M tokens
- Output Tokens: $60 /M tokens
- Cache Read: $7.5 /M tokens
Input Modalities
- Text
- Vision
Output Modalities
- Text
Capabilities
- Thinking
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="o1-preview",
messages=[
{
"role": "user",
"content": "Hello, how are you?"
}
],
max_tokens=1024,
stream=False,
)
print(response.choices[0].message.content)GPT-6 Astra is OpenAI's newest and most intelligent model, with industry-leading performance in computer operations, web browsing, software engineering, scientific research, and professional work. It excels at executing multi-step workflows across code, browsers, and various professional software. Astra can achieve better results with significantly fewer output tokens, making its estimated API cost per task lower.
GPT-5.6 Sol (limited-time 50% off) is OpenAI’s frontier reasoning model for complex coding, professional knowledge work, deep research, and long-running agents. It supports a roughly 1.05M-token context window, image understanding, and extensive tool use. Compared with Terra and Luna, Sol prioritizes capability and reliability on demanding tasks.
GPT-5.6 Luna is designed for cost-sensitive, high-volume workloads. It roughly corresponds to the nano model tier used in earlier GPT-5 families.
GPT‑5.6 Sol sets a new standard for both intelligence and efficiency, achieving state-of-the-art results across coding, knowledge work, cybersecurity, and science while outperforming previous and competing frontier models with fewer tokens and at lower estimated cost.
GPT-5.6 Terra is designed for workloads that balance intelligence and cost. It roughly corresponds to the mini model tier used in earlier GPT-5 families.
Developed by OpenAI, gpt-oss-20b-free is an open-weight 21B parameter model released under the Apache 2.0 license. This model utilizes a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass. It supports an expansive context window of up to 131,072 tokens, making it well-suited for long-context tasks.
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