API Documentation — ModelBridge

📖 API Documentation

ModelBridge provides a fully OpenAI-compatible API. If you've used the OpenAI SDK before, you already know how to use us — just change the base URL.

Base URL

https://aibridge-api.com/v1

Authentication

All requests require an API key in the Authorization header:

Authorization: Bearer mb-xxxxxxxxxxxxx

🔗 Available Endpoints

Method	Endpoint	Description
GET	`/v1/models`	List available models
POST	`/v1/chat/completions`	Chat completion (supports streaming)
POST	`/v1/embeddings`	Generate text embeddings
GET	`/health`	Service health check

🤖 Supported Models

Model ID	Type	Context Window	Best For
`deepseek-chat`	Chat	64K	General purpose conversations, coding
`deepseek-reasoner`	Reasoning	64K	Complex reasoning, math, logic
`qwen-max`	Chat	32K	Multilingual tasks, long context
`qwen-plus`	Chat	131K	Cost-effective general usage

💬 Chat Completions

The primary endpoint for generating text responses. Supports both regular and streaming (SSE) modes.

Request

Python
cURL
Node.js

from openai import OpenAI

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

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

print(response.choices[0].message.content)
    

curl https://aibridge-api.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "deepseek-chat",
    "messages": [
      {"role": "system", "content": "You are helpful."},
      {"role": "user", "content": "Hello!"}
    ]
  }'
    

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.API_KEY,
  baseURL: 'https://aibridge-api.com/v1',
});

const response = await client.chat.completions.create({
  model: 'deepseek-chat',
  messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0].message.content);
    

Streaming Response

Set stream: true to receive tokens as they are generated:

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Tell me a story."}],
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
    

⚠️ Error Codes

Status Code	Error	Cause	Solution
401	Unauthorized	Invalid or missing API key	Check your Authorization header
404	Not Found	Invalid endpoint or model	Verify the URL and model name
429	Rate Limited	Too many requests	Reduce frequency; upgrade plan if needed
500	Internal Error	Upstream service issue	Retry after a few seconds
502	Bad Gateway	Upstream unavailable	Upstream AI provider is down
504	Gateway Timeout	Request took too long	Try with shorter prompts