API Documentation

Authorization: Bearer sk-your-api-key

curl https://token.openclaw-token.shop/v1/chat/completions \
  -H "Authorization: Bearer sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from openai import OpenAI

client = OpenAI(api_key="sk-your-key", base_url="https://token.openclaw-token.shop/v1")

import OpenAI from "openai";
const client = new OpenAI({ apiKey: "sk-your-key", baseURL: "https://token.openclaw-token.shop/v1" });

response = client.chat.completions.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a Python quicksort"}
    ]
)
print(response.choices[0].message.content)

from anthropic import Anthropic

anthropic_client = Anthropic(api_key="sk-your-key", base_url="https://token.openclaw-token.shop")
message = anthropic_client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a Python quicksort"}]
)
print(message.content[0].text)

const res = await client.chat.completions.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Write a JS quicksort" }],
});
console.log(res.choices[0].message.content);

response = client.chat.completions.create(
    model="gpt5.4-plus",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)
print(response.choices[0].message.content)

const res = await client.chat.completions.create({
  model: "gpt5.4-plus",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Explain quantum computing" }],
});
console.log(res.choices[0].message.content);

response = client.chat.completions.create(
    model="gemini-3-flash-preview",
    messages=[{"role": "user", "content": "Hello, Gemini!"}]
)
print(response.choices[0].message.content)

![image](https://i.ibb.co/xxxxx/image.jpg)

import re
from openai import OpenAI

client = OpenAI(api_key="sk-your-key", base_url="https://token.openclaw-token.shop/v1")

response = client.chat.completions.create(
    model="gemini-3.1-flash-image",
    max_tokens=2000,
    messages=[{"role": "user", "content": "Draw a cute orange cat at sunset"}]
)

content = response.choices[0].message.content
# Extract image URL from markdown
url = re.search(r'!\[image\]\((\S+)\)', content)
if url:
    print(f"Image URL: {url.group(1)}")

# 2K resolution with 16:9 aspect ratio
response = client.chat.completions.create(
    model="gemini-3.1-flash-image",
    max_tokens=2000,
    messages=[{"role": "user", "content": "A mountain landscape at sunset"}],
    extra_body={"google": {"image_config": {
        "image_size": "2K",
        "aspect_ratio": "16:9"
    }}}
)

# 4K resolution (takes 60-200s, large images)
response = client.chat.completions.create(
    model="gemini-3.1-flash-image",
    max_tokens=2000,
    messages=[{"role": "user", "content": "A detailed city skyline"}],
    extra_body={"google": {"image_config": {
        "image_size": "4K",
        "aspect_ratio": "16:9"
    }}}
)

import base64

# Read local image as base64
with open("input.png", "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="gemini-3.1-flash-image",
    max_tokens=2000,
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Change the background to a beach scene"},
            {"type": "image_url", "image_url": {
                "url": f"data:image/png;base64,{img_b64}"
            }}
        ]
    }]
)

# Turn 1: Generate initial image
messages = [{"role": "user", "content": "Draw a simple red house"}]
r1 = client.chat.completions.create(
    model="gemini-3.1-flash-image", max_tokens=2000, messages=messages
)

# Turn 2: Edit the result
messages.append({"role": "assistant", "content": r1.choices[0].message.content})
messages.append({"role": "user", "content": "Add a blue sky and green grass"})
r2 = client.chat.completions.create(
    model="gemini-3.1-flash-image", max_tokens=2000, messages=messages
)

# Turn 3: Further refinement
messages.append({"role": "assistant", "content": r2.choices[0].message.content})
messages.append({"role": "user", "content": "Add a sun and some clouds"})
r3 = client.chat.completions.create(
    model="gemini-3.1-flash-image", max_tokens=2000, messages=messages
)

response = client.chat.completions.create(
    model="gemini-3.1-flash-image",
    max_tokens=2000,
    temperature=1.5,     # higher = more creative
    top_p=0.9,
    messages=[
        {"role": "system", "content": "You are a cartoon artist. Always draw in cute kawaii style."},
        {"role": "user", "content": "Draw a dog playing in a park"}
    ]
)

// Generate
const res = await client.chat.completions.create({
  model: "gemini-3.1-flash-image",
  max_tokens: 2000,
  messages: [{ role: "user", content: "Draw a sunset landscape" }],
});
const content = res.choices[0].message.content;
const urlMatch = content.match(/!\[image\]\((\S+)\)/);
if (urlMatch) console.log("Image:", urlMatch[1]);

// Edit with image input
import fs from "fs";
const imgB64 = fs.readFileSync("input.png").toString("base64");
const edited = await client.chat.completions.create({
  model: "gemini-3.1-flash-image",
  max_tokens: 2000,
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Make this image more colorful" },
      { type: "image_url", image_url: { url: `data:image/png;base64,${imgB64}` } },
    ],
  }],
});

from openai import OpenAI

client = OpenAI(api_key="sk-your-key", base_url="https://token.openclaw-token.shop/v1")

response = client.images.generate(
    model="gpt-image-1.5-vvip",
    prompt="A cute orange cat wearing sunglasses at the beach",
    size="1024x1024",
    quality="high",
    n=1
)
print(response.data[0].url)

response = client.images.generate(
    model="gpt-image-1.5-vvip",
    prompt="A product photo of a sneaker, isolated, studio lighting",
    size="1024x1024",
    quality="high",
    background="transparent",
    moderation="low",
    output_format="png",
    output_compression=30,
    response_format="b64_json",
    n=1
)

import base64
image_data = base64.b64decode(response.data[0].b64_json)
with open("output.png", "wb") as f:
    f.write(image_data)

# gpt-image-1/1.5 supports n=1~10, gpt-image-2 only n=1
response = client.images.generate(
    model="gpt-image-1.5-vvip",
    prompt="A colorful butterfly",
    size="1024x1024",
    quality="medium",
    n=4
)
for i, img in enumerate(response.data):
    print(f"Image {i+1}: {img.url}")

# Basic edit
response = client.images.edit(
    model="gpt-image-1.5-vvip",
    image=open("input.png", "rb"),
    prompt="Add red sunglasses to the cat",
    n=1
)
print(response.data[0].url)

# Edit with mask (region-specific inpainting)
response = client.images.edit(
    model="gpt-image-1.5-vvip",
    image=open("input.png", "rb"),
    mask=open("mask.png", "rb"),  # alpha channel marks edit area
    prompt="Place a golden crown here",
    n=1
)

// Generate
const image = await client.images.generate({
  model: "gpt-image-1.5-vvip", prompt: "A cute cat",
  size: "1024x1024", quality: "high", n: 1,
});
console.log(image.data[0].url);

// Edit
import fs from "fs";
const edited = await client.images.edit({
  model: "gpt-image-1.5-vvip",
  image: fs.createReadStream("input.png"),
  prompt: "Add sunglasses", n: 1,
});

response = client.images.generate(
    model="wan2.6-t2i",
    prompt="一只戴墨镜的橘猫在海边冲浪，超写实摄影",
    size="1280*1280",
    n=1,
)
print(response.data[0].url)  # URL valid 24h

# Full parameters: negative prompt + seed + no auto-enrich + no watermark
response = client.images.generate(
    model="wan2.6-t2i",
    prompt="一只柴犬在草地上奔跑，摄影大片风格",
    negative_prompt="模糊, 低质量, 变形, 多余的腿",
    size="1472*1104",   # 4:3 landscape
    n=2,
    seed=42,
    prompt_extend=False,  # use prompt as-is
    watermark=False,
)
for i, img in enumerate(response.data):
    print(f"Image {i+1}: {img.url}")

# Generate 4 images at once (default n=4)
response = client.images.generate(
    model="wan2.6-t2i",
    prompt="一朵盛开的红色玫瑰，微距摄影",
    size="1280*1280",
    n=4,
)
import urllib.request
for i, img in enumerate(response.data):
    urllib.request.urlretrieve(img.url, f"output_{i+1}.png")
    print(f"Saved output_{i+1}.png")

const image = await client.images.generate({
  model: "wan2.6-t2i",
  prompt: "一只橘猫在海边，超写实",
  negative_prompt: "模糊, 低质量",
  size: "1280*1280",
  n: 1,
  seed: 42,
  prompt_extend: true,
  watermark: false,
});
console.log(image.data[0].url);

# Wan 2.7 uses chat/completions, returns image URLs in content
response = client.chat.completions.create(
    model="wan2.7-image",
    messages=[{"role": "user", "content": [{"type": "text", "text": "A cute shiba inu"}]}]
)
for choice in response.choices:
    for item in choice.message.content:
        if item.get("image"):
            print(item["image"])

response = client.images.generate(
    model="doubao-seedream-5-0-260128",
    prompt="A cute shiba inu, photorealistic",
    size="2K"
)
print(response.data[0].url)

import requests, time

BASE = "https://token.openclaw-token.shop"
HEADERS = {"Authorization": "Bearer sk-your-key", "Content-Type": "application/json"}

# 1. Submit
r = requests.post(f"{BASE}/v1/video/generations", headers=HEADERS, json={
    "model": "wan2.6-t2v",
    "prompt": "An orange cat running on a beach, slow motion",
    "negative_prompt": "blurry, low quality",
    "resolution": "720P",
    "duration": 5,
    "seed": 42,
})
task_id = r.json()["id"]

# 2. Poll (every 10s, may take 1~3 min)
while True:
    data = requests.get(f"{BASE}/v1/videos/{task_id}", headers=HEADERS).json()
    if data["status"] == "completed":
        print(data["metadata"]["url"])  # URL valid 24h
        break
    elif data["status"] == "failed":
        print("Error:", data.get("error", {}).get("message"))
        break
    time.sleep(10)

# Silent (default, cheaper)
r = requests.post(f"{BASE}/v1/video/generations", headers=HEADERS, json={
    "model": "wan2.6-i2v-flash",
    "prompt": "The cat starts running",
    "input_reference": "https://your-image-url.png",
    "resolution": "720P",
    "duration": 5,
})
# Poll same as above...

# AI-generated audio (2× silent price)
r = requests.post(f"{BASE}/v1/video/generations", headers=HEADERS, json={
    "model": "wan2.6-i2v-flash",
    "prompt": "A guitarist performing on stage",
    "input_reference": img_url,
    "resolution": "1080P",
    "duration": 5,
    "audio": True,
})

# Your own background audio (wav/mp3, 3~30s, ≤15MB)
r = requests.post(f"{BASE}/v1/video/generations", headers=HEADERS, json={
    "model": "wan2.6-i2v-flash",
    "prompt": "Musician performing",
    "input_reference": img_url,
    "resolution": "720P",
    "duration": 5,
    "audio": True,
    "audio_url": "https://your-audio.mp3",
})

# Standard quality model, multi-camera, all options
r = requests.post(f"{BASE}/v1/video/generations", headers=HEADERS, json={
    "model": "wan2.6-i2v",
    "prompt": "Cat exploring a garden, multiple angles",
    "negative_prompt": "blurry, shaky",
    "input_reference": img_url,
    "resolution": "1080P",
    "duration": 10,
    "shot_type": "multi",
    "prompt_extend": True,
    "watermark": False,
    "seed": 42,
})

const BASE = "https://token.openclaw-token.shop";
const headers = { "Authorization": "Bearer sk-your-key", "Content-Type": "application/json" };

// Submit
const res = await fetch(`${BASE}/v1/video/generations`, {
  method: "POST", headers,
  body: JSON.stringify({
    model: "wan2.6-i2v-flash", prompt: "Cat running",
    input_reference: "https://your-image.png",
    resolution: "720P", duration: 5, audio: true,
  }),
});
const { id: taskId } = await res.json();

// Poll
while (true) {
  const d = await (await fetch(`${BASE}/v1/videos/${taskId}`, { headers })).json();
  if (d.status === "completed") { console.log(d.metadata.url); break; }
  if (d.status === "failed") { console.log(d.error); break; }
  await new Promise(r => setTimeout(r, 10000));
}

export ANTHROPIC_BASE_URL="https://token.openclaw-token.shop"
export ANTHROPIC_API_KEY="sk-your-key"

claude                          # defaults to claude-sonnet-4-6
claude --model claude-opus-4-7  # specify model

pip install openai
from openai import OpenAI
client = OpenAI(api_key="sk-your-key", base_url="https://token.openclaw-token.shop/v1")

npm install openai
import OpenAI from "openai";
const client = new OpenAI({ apiKey: "sk-your-key", baseURL: "https://token.openclaw-token.shop/v1" });

pip install anthropic
from anthropic import Anthropic
client = Anthropic(api_key="sk-your-key", base_url="https://token.openclaw-token.shop")

# Run GPT Image 1/1.5 full test (11 cases)
NEWAPI_BASE_URL="https://your-api-gateway.example.com/v1" \
NEWAPI_API_KEY="sk-your-key" \
python3 bin/demo_gpt_image.py

# Specify model
python3 bin/demo_gpt_image.py --model gpt-image-1-vvip

# Run GPT Image 2 full test
NEWAPI_BASE_URL="https://token.openclaw-token.shop/v1" \
NEWAPI_API_KEY="sk-your-key" \
python3 bin/demo_gpt_image_2.py

# Run Wan 2.6 full test (all models)
NEWAPI_BASE_URL="https://token.openclaw-token.shop/v1" \
NEWAPI_API_KEY="sk-your-key" \
python3 bin/demo_wan26.py

# Wan 2.6 - only text-to-image
python3 bin/demo_wan26.py --only-t2i

# Wan 2.6 - only video
python3 bin/demo_wan26.py --skip-t2i