The OpenAI Python SDK is mostly a base_url and an api_key. If an API speaks the same shapes — /images/generations, /chat/completions, /videos — the SDK you already have installed works against it.

LivePair exposes exactly that surface: an OpenAI-compatible endpoint covering 60+ image and video generation models (Seedream, Seedance, Wan, Qwen Image, Ideogram, Minimax) plus a set of chat models. One base_url change, and client.images.generate() produces media from models that don't exist on OpenAI.

Disclosure: I work on LivePair. Everything below is runnable against the live API — the catalog, prices, and shapes are public at GET /v1/agent/models.

Setup 

pip install openai requests

Grab a key: sign up at livepairai.com → Settings → API keys → lp_…. Billing is a prepaid credit balance — each call debits the per-model price, no subscription.

from openai import OpenAI client = OpenAI( base_url="https://livepairai.com/v1/agent", api_key="lp_...", # sent as a Bearer token, so the stock SDK works )

Images — client.images.generate just works 

img = client.images.generate( model="seedream-5.0", prompt="a lighthouse on a cliff at dusk, cinematic lighting", size="1024x1024", ) print(img.data[0].url)

That's the unmodified OpenAI call. The catalog decides what model can be — seedream-5.0, qwen-image-3, ideogram-3-turbo, and so on. size accepts the standard 1024x1024 / 1792x1024 / 1024x1792 values and maps onto each model's aspect-ratio ladder.

Chat — client.chat.completions.create 

r = client.chat.completions.create( model="deepseek/deepseek-v4.1-flash", messages=[{"role": "user", "content": "Explain the CAP theorem in two sentences."}], max_tokens=256, ) print(r.choices[0].message.content)

Video — Sora-shaped, poll-then-download

The video endpoint mirrors OpenAI's Videos API shape: POST /videos returns a job immediately, GET /videos/{id} polls status, GET /videos/{id}/content streams the mp4. Newer SDK releases have a client.videos namespace; here's the plain-HTTP version so the contract is visible:

import time, requests H = {"Authorization": "Bearer lp_...", "Content-Type": "application/json"} BASE = "https://livepairai.com/v1/agent" job = requests.post(f"{BASE}/videos", headers=H, json={ "model": "wan-3.0-t2v", "prompt": "drone shot over a foggy forest at dawn, slow push-in", "size": "1280x720", # 16:9; 720x1280 → 9:16  "seconds": "5", }).json() vid = job["id"] while True: v = requests.get(f"{BASE}/videos/{vid}", headers=H).json() if v["status"] in ("completed", "failed"): break time.sleep(5) if v["status"] == "completed": mp4 = requests.get(f"{BASE}/videos/{vid}/content", headers=H) # 302 → media URL  open("out.mp4", "wb").write(mp4.content)

Prices are data, not a pricing page

GET /v1/agent/models returns every model with a priceUsd field — the exact per-call debit before you make it:

models = requests.get(f"{BASE}/models").json() for m in models["models"]: print(m["id"], m["mode"], m["priceUsd"])

GET /v1/agent/balance shows your prepaid balance and recent ledger entries.

Why bother

The useful part isn't another API key — it's that image gen, video gen, and chat land on the same OpenAI surface you already integrate, priced per call against a balance you can watch. For pipelines that need several model families (compare outputs, fallback chains, cost routing), switching models is a string, not a new provider account.

There's also a no-account path if you're wiring autonomous agents: the same endpoints answer 402 Payment Required and settle per call in USDC via x402, so a client that can sign a payment header can call without any signup at all.

Gotchas

  • n > 1 isn't supported on image generation — loop instead.
  • Video is async by design; budget ~1–3 min per clip depending on model.
  • A generation that fails provider-side refunds to the balance automatically.
  • Results aren't published anywhere and are deleted after 48h — save the bytes.

Docs: livepairai.com/docs, machine-readable spec at /v1/agent/openapi.json, MCP server for agent clients under /mcp.