Pricing
- Input Tokens: $0.120 /M tokens
- Output Tokens: $0.480 /M tokens
Input Modalities
- Text
Output Modalities
- Text
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="aihub-Phi-4-mini-instruct",
messages=[
{
"role": "user",
"content": "Hello, how are you?"
}
],
max_tokens=1024,
stream=False,
)
print(response.choices[0].message.content)Frequently asked questions
What is aihub-Phi-4-mini-instruct?
What is the context length of aihub-Phi-4-mini-instruct?
How much does aihub-Phi-4-mini-instruct cost?
What modalities does aihub-Phi-4-mini-instruct support?
How do I call aihub-Phi-4-mini-instruct via API?
Who created aihub-Phi-4-mini-instruct?
Compare aihub-Phi-4-mini-instruct
More models from Microsoft
Token-based pricing: Text input $5 / 1M tokens | Image input $8 / 1M tokens | Image output $47 / 1M tokens
MAI-Image-2.5 is Microsoft's flagship AI image generation and editing model. With top-tier realism, precise text rendering, and powerful image editing capabilities, it debuted among the top three in AI image generation rankings. It is primarily targeted at commercial design, product photography, and professional creative work.
Token-based pricing: Text input $5 / 1M tokens | Image input $8 / 1M tokens | Image output $47 / 1M tokens
MAI-Image-2.5 is Microsoft's flagship AI image generation and editing model. With top-tier realism, precise text rendering, and powerful image editing capabilities, it debuted among the top three in AI image generation rankings. It is primarily targeted at commercial design, product photography, and professional creative work.
Token-based pricing: Text input $1.75 / 1M tokens | Image input $1.75 / 1M tokens | Image output $33 / 1M tokens
MAI-Image-2.5 is Microsoft's flagship AI image generation and editing model. With top-tier realism, precise text rendering, and powerful image editing capabilities, it debuted among the top three in AI image generation rankings. It is primarily targeted at commercial design, product photography, and professional creative work.
Phi-4-mini-reasoning is a lightweight open model designed for advanced mathematical reasoning and logic-intensive problem-solving. It is particularly well-suited for tasks such as formal proofs, symbolic computation, and solving multi-step word problems. With its efficient architecture, the model balances high-quality reasoning performance with cost-effective deployment, making it ideal for educational applications, embedded tutoring, and lightweight edge or mobile systems. Phi-4-mini-reasoning supports a 128K token context length, enabling it to process and reason over long mathematical problems and proofs. Built on synthetic and high-quality math datasets, the model leverages advanced fine-tuning techniques such as supervised fine-tuning and preference modeling to enhance reasoning capabilities. Its training incorporates safety and alignment protocols, ensuring robust and reliable performance across supported use cases.
Microsoft's latest model
Phi-4 is a state-of-the-art open model based on a combination of synthetic datasets, curated public domain website data, and acquired academic books and QA datasets. The approach aims to ensure that small, efficient models are trained using data focused on high quality and advanced reasoning.
AIHubMix© 2023 - 2026 AIHubMix, LLC