Developer Documentation

API Reference

Access OpenAI and Google Gemini models through one gateway, using the SDKs you already know. Change two lines — the base URL and your key — and you're live.

Quickstart

Install the OpenAI SDK, point it at the AICreditMart endpoint, and use your API key.

python
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)
That's it. The API is OpenAI-compatible — existing OpenAI SDK code works by changing only base_url and api_key.

Authentication

Pass your API key as the api_key in the SDK client. It's sent as a standard Bearer token and tracks your usage and billing.

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

Keep your key private. If a key is exposed, contact us to rotate it.

Base URLs

Two endpoints, depending on which SDK you use:

OpenAI SDKhttps://api.aicreditmart.com/v1/
Gemini SDKhttps://api.aicreditmart.com/genai

Use the OpenAI base URL for all OpenAI models. Use the Gemini base URL with Google's google-genai SDK for Gemini models.

Rate Limits

Rate limits are generous and set per API key. Most workloads run without hitting them. If you expect high sustained throughput or need a dedicated limit, contact us and we'll raise it for your key.

Regions

Models run primarily on US-based infrastructure (Azure US, Google US, and others). If you have data-residency requirements, EU region routing is available on request — contact us to enable it for your key.

Tracking Usage

Every account has a dedicated dashboard with real-time usage and cost tracking, broken down by model, tokens, and spend — so you always know exactly what you're using.

Sign in to view your usage at app.aicreditmart.com.

OpenAI Models

Send the Model ID exactly as shown.

Text & reasoning

Model IDNotes
gpt-5.4Affordable model for coding and professional work
gpt-5.4-miniStrongest mini for coding and computer use
gpt-5.4-nanoCheapest 5.4-class model for high-volume tasks
gpt-5.2Previous frontier model, configurable reasoning
gpt-5.1Strong coding and agentic tasks, configurable reasoning
gpt-5Reasoning model for coding and agentic tasks
gpt-5-miniNear-frontier, low-latency, high-volume workloads
gpt-5-nanoFastest, most cost-efficient GPT-5
gpt-4.1Smartest non-reasoning model
gpt-4.1-miniSmaller, faster GPT-4.1
gpt-4.1-nanoFastest, most cost-efficient GPT-4.1
gpt-4oFast, intelligent, flexible multimodal model
gpt-4o-miniFast, affordable model for focused tasks

Image

Model IDNotes
gpt-image-2State-of-the-art image generation
gpt-image-1.5Previous-generation image model
gpt-image-1-miniCost-efficient image generation

Video

Model IDNotes
sora-2Flagship video generation with synced audio

Embeddings

Model IDNotes
text-embedding-3-largeMost capable embedding model
text-embedding-3-smallSmall, fast embedding model

Example — Text (gpt-5.4)

python
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}")

Example — Image (gpt-image-2)

python
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")

Example — Video (sora-2)

python
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")

Example — Embeddings (text-embedding-3-small)

python
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)}")

Google Gemini Models

Use Google's google-genai SDK pointed at the Gemini base URL. Send model names with the models/ prefix.

Text

Model IDNotes
gemini-3.5-flashMost intelligent flash for agentic and coding
gemini-3.1-pro-previewAdvanced reasoning and agentic coding (preview)
gemini-3.1-flash-liteFrontier-class performance at low cost
gemini-3-flash-previewFrontier-class performance, low cost (preview)
gemini-2.5-proAdvanced reasoning and coding for complex tasks
gemini-2.5-flashBest price-performance for high-volume reasoning
gemini-2.5-flash-liteFastest, most budget-friendly 2.5 model

Image

Model IDNotes
gemini-3-pro-imageStudio-quality 4K image generation and editing
gemini-3.1-flash-imageHigh-efficiency image generation, optimized for speed
gemini-2.5-flash-imageNative image generation for fast creative workflows

Example — Text (gemini-3.5-flash)

python
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)

Example — Image (gemini-3.1-flash-image)

python
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")
Higher resolution. Gemini image models scale by resolution. Add image_config=types.ImageConfig(image_size="4K") inside the config for up to 4K (on gemini-3.1-flash-image and gemini-3-pro-image).
Need a key, higher limits, or a model that isn't listed? Contact your AICreditMart account manager.