Chat Completions API

Inference Space provides the OpenAI-compatible chat completion endpoint POST /v1/chat/completions across the LLM model families in the catalog, including Claude, GPT, Gemini, DeepSeek, Qwen, and Kimi. Existing OpenAI-compatible clients can connect by changing only the Base, Key, and model; no application-code rewrite is required.

The private deployment API is identical; only the Base domain needs to change → Enterprise private deployment.

Overview

• Endpoint: POST https://ai.inf.space/v1/chat/completions
• Base: https://ai.inf.space/v1 (the browser console shares the ai.inf.space host; use the /v1 path for API requests)
• Protocol: OpenAI Chat Completions compatible, with request and response fields matching OpenAI
• Multiple models: Send only model with the same Key and endpoint; the gateway selects a service from the organization's routing policy

Authentication

Authenticate with the HTTP header Authorization: Bearer <gk_...>. API Keys start with gk_ and are created in the console. The request body is application/json.

Authorization: Bearer gk_xxxxxxxxxxxxxxxx
Content-Type: application/json

In production, keep the Key on the server and do not expose it to browsers or clients.

Automatic routing

Clients send only model and must not select a provider through a URL query parameter, the X-Provider header, or a request-body field. The gateway selects an available service from the API Key organization's routing policy and performs failover when a candidate is unavailable.

Gemini models

Call Gemini by using the corresponding model. Model IDs match Google's official IDs, and the gateway selects the service from organization policy:

Basic request

curl "https://ai.inf.space/v1/chat/completions" \
  -H "Authorization: Bearer $TOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-x",
    "messages": [
      { "role": "system", "content": "You are a concise assistant." },
      { "role": "user", "content": "Introduce yourself in three sentences." }
    ]
  }'

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://ai.inf.space/v1",
    api_key=os.environ["TOS_API_KEY"],
)

resp = client.chat.completions.create(
    model="claude-sonnet-4-x",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Introduce yourself in three sentences."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://ai.inf.space/v1",
  apiKey: process.env.TOS_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "claude-sonnet-4-x",
  messages: [
    { role: "system", content: "You are a concise assistant." },
    { role: "user", content: "Introduce yourself in three sentences." },
  ],
});
console.log(resp.choices[0].message.content);

Parameters

The standard OpenAI fields are supported:

Streaming

With stream: true, the response is returned as Server-Sent Events (SSE) chunks. Each chunk is the OpenAI-standard chat.completion.chunk; incremental text is in choices[0].delta.content, and the stream ends with data: [DONE].

curl "https://ai.inf.space/v1/chat/completions" \
  -H "Authorization: Bearer $TOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-x",
    "stream": true,
    "messages": [
      { "role": "user", "content": "Write a short poem about clouds." }
    ]
  }'

The final usage chunk looks like this:

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion.chunk",
  "choices": [],
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 38,
    "total_tokens": 62
  }
}

Tool calls

Declare callable functions with tools. When needed, the model returns tool_calls. After the application executes a tool, add its result as a role: "tool" message in the next messages turn so the model can produce the final answer.

curl "https://ai.inf.space/v1/chat/completions" \
  -H "Authorization: Bearer $TOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-x",
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
            "description": "Get the weather for a specified city",
          "parameters": {
            "type": "object",
            "properties": {
              "city": { "type": "string", "description": "City name" }
            },
            "required": ["city"]
          }
        }
      }
    ],
    "tool_choice": "auto",
    "messages": [
      { "role": "user", "content": "What is the weather like in Hefei today?" }
    ]
  }'

Responses and usage

The non-streaming response uses the standard OpenAI structure:

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "model": "claude-sonnet-4-x",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "Hello, I am a concise assistant." },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 38,
    "total_tokens": 62
  }
}

Billing is based on input and output tokens in usage, in units of 1M tokens. Exact prices, available models, and discounts depend on console and organization pricing; organization-specific prices and contract discounts take precedence.

Related pages

• Messages API (native Anthropic)
• Responses API (OpenAI-compatible)
• Authentication