Quickstart

import requests
import json

BASE_URL = "https://nano-gpt.com/api/v1"
API_KEY = "YOUR_API_KEY"  # Replace with your API key

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
    "Accept": "text/event-stream"  # Required for SSE streaming
}

def stream_chat_completion(messages, model="minimax/minimax-m2.7"):
    """
    Send a streaming chat completion request using the OpenAI-compatible endpoint.
    """
    data = {
        "model": model,
        "messages": messages,
        "stream": True  # Enable streaming
    }

    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=data,
        stream=True
    )

    if response.status_code != 200:
        raise Exception(f"Error: {response.status_code}")

    for line in response.iter_lines():
        if line:
            line = line.decode('utf-8')
            if line.startswith('data: '):
                line = line[6:]
            if line == '[DONE]':
                break
            try:
                chunk = json.loads(line)
                if chunk['choices'][0]['delta'].get('content'):
                    yield chunk['choices'][0]['delta']['content']
            except json.JSONDecodeError:
                continue

# Example usage
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Please explain the concept of artificial intelligence."}
]

try:
    print("Assistant's Response:")
    for content_chunk in stream_chat_completion(messages):
        print(content_chunk, end='', flush=True)
    print("")
except Exception as e:
    print(f"Error: {str(e)}")

{ "model": "zai-org/glm-5:fast", "messages": [{ "role": "user", "content": "Hello" }] }
{ "model": "zai-org/glm-5:cheap", "messages": [{ "role": "user", "content": "Hello" }] }
{ "model": "moonshotai/kimi-k2.6:thinking:caching", "messages": [{ "role": "user", "content": "Hello" }] }

curl https://nano-gpt.com/v1/images/generations \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "hidream",
    "prompt": "A serene landscape at sunset",
    "n": 1,
    "size": "1024x1024"
  }'

import base64
import requests

API_KEY = "YOUR_API_KEY"

def generate_image(prompt, model="hidream", size="1024x1024"):
    response = requests.post(
        "https://nano-gpt.com/v1/images/generations",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "model": model,
            "prompt": prompt,
            "n": 1,
            "size": size,
            "response_format": "b64_json"
        }
    )
    response.raise_for_status()
    return response.json()

# Example usage
prompt = "A serene landscape with mountains and a lake at sunset, digital art style"
result = generate_image(prompt)

image_bytes = base64.b64decode(result["data"][0]["b64_json"])
with open("generated_image.png", "wb") as f:
    f.write(image_bytes)

print("Image generated successfully!")
print("Image saved as 'generated_image.png'")

RUN_ID=$(curl -s -X POST https://nano-gpt.com/api/generate-video \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "veo2-video",
    "prompt": "A cinematic shot of a mountain lake at sunrise"
  }' | jq -r '.runId')

curl "https://nano-gpt.com/api/video/status?requestId=$RUN_ID" \
  -H "Authorization: Bearer YOUR_API_KEY"