API Reference

from openai import OpenAI

client = OpenAI(
    base_url="https://api.aicreditmart.com/v1/",
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

api_key = "sk-bf-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

import anthropic

client = anthropic.Anthropic(
    base_url="https://api.aicreditmart.com/anthropic",
    api_key="YOUR_API_KEY",   # your sk-bf virtual key, not a real Anthropic key
)

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1000,
    messages=[
        {"role": "user", "content": "Tell me about Stockholm in one sentence."}
    ],
)
print(message.content[0].text)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.aicreditmart.com/v1/",
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "How far is New York from London?"}],
)
print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")

from openai import OpenAI
import base64

client = OpenAI(
    base_url="https://api.aicreditmart.com/v1/",
    api_key="YOUR_API_KEY",
)

response = client.images.generate(
    model="gpt-image-2",
    prompt="A majestic horse standing in a field",
    n=1,
    size="1024x1024",
    quality="medium",
    output_format="png",
)

if response.data[0].b64_json:
    img = base64.b64decode(response.data[0].b64_json)
    with open("horse.png", "wb") as f:
        f.write(img)
    print("Saved horse.png")

import time
from openai import OpenAI

client = OpenAI(
    base_url="https://api.aicreditmart.com/v1/",
    api_key="YOUR_API_KEY",
)

# 1. Create the video
video = client.videos.create(
    model="sora-2",
    prompt="A cat playing piano in a jazz bar",
    size="1280x720",
    seconds="4",
)

# 2. Poll until complete
while video.status in ["queued", "in_progress"]:
    time.sleep(5)
    video = client.videos.retrieve(video.id)
    print(f"Status: {video.status}")

# 3. Download
if video.status == "completed":
    content = client.videos.download_content(video.id, variant="video")
    content.write_to_file("output.mp4")
    print("Saved output.mp4")

from openai import OpenAI

client = OpenAI(
    base_url="https://api.aicreditmart.com/v1/",
    api_key="YOUR_API_KEY",
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The quick brown fox jumps over the lazy dog.",
)
print(f"Dimensions: {len(response.data[0].embedding)}")

from google import genai
from google.genai import types

client = genai.Client(
    api_key="YOUR_API_KEY",
    http_options=types.HttpOptions(base_url="https://api.aicreditmart.com/genai"),
)

response = client.models.generate_content(
    model="models/gemini-3.5-flash",
    contents="Tell me about London in one sentence.",
)
print(response.text)

from google import genai
from google.genai import types

client = genai.Client(
    api_key="YOUR_API_KEY",
    http_options=types.HttpOptions(base_url="https://api.aicreditmart.com/genai"),
)

MODEL = "gemini-3.1-flash-image"
r = client.models.generate_content(
    model=f"models/{MODEL}",
    contents="A photorealistic golden retriever wearing a red cape, studio lighting",
    config=types.GenerateContentConfig(response_modalities=["IMAGE"]),
)

for part in r.candidates[0].content.parts:
    if part.inline_data and part.inline_data.data:
        with open(f"{MODEL}.png", "wb") as f:
            f.write(part.inline_data.data)
        print(f"Saved {MODEL}.png")