First API Call

ScitiX provides a unified Model API platform for developers and enterprises. It is compatible with OpenAI-style interfaces and supports large language models, vision/multimodal models, code generation models, and reasoning models, all available with pay-as-you-go billing. You can create an API key and call the model through REST or the OpenAI-compatible SDK.

1. Create an API key

Go to the API Keys page and click Create API Key.
Give the key a recognizable name, then copy it and store it in an environment variable.
export API_KEY="YOUR_API_KEY"

Each key shows its status, last-used time, and monthly usage in the list. For quotas, rate limits, or pausing a key, see the API Keys guide.

2. Call a model

Call via REST

Use standard HTTP requests. The following example sends a streaming Chat Completions request:

curl -N https://api.scitix.ai/model-api/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-32B",
    "stream": true,
    "max_completion_tokens": 1024,
    "messages": [
      {"role": "user", "content": "What new opportunities might reasoning models bring to the market?"}
    ]
  }'

The example above prints the raw streaming response. Because the API uses Server-Sent Events (SSE), you will see multiple data: {...} JSON lines. To display only the concatenated text content, you can pipe the response through jq:

This script requires jq to be installed on your machine, for example with brew install jq on macOS or apt-get install jq on Debian/Ubuntu.

curl -N https://api.scitix.ai/model-api/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-32B",
    "stream": true,
    "max_completion_tokens": 1024,
    "messages": [{"role": "user", "content": "What new opportunities might reasoning models bring to the market?"}]
  }' 2>/dev/null | while IFS= read -r line; do
  if [[ "$line" == data:* ]]; then
    json="${line#data: }"
    if [[ "$json" != "[DONE]" ]]; then
      echo -n "$(echo "$json" | jq -r '.choices[0].delta.content // empty')"
    fi
  fi
done
echo

Set stream to false if you prefer to receive the full result in a single response. For other tasks, such as text-to-image, refer to the documentation or the model detail page for the corresponding APIs and parameters.

Call via the OpenAI-compatible Python SDK

The platform is compatible with the official OpenAI Python SDK. Install Python 3.7.1+ and run:

pip install --upgrade openai

Example with streaming output and reasoning support:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("API_KEY") or "YOUR_API_KEY",
    base_url="https://api.scitix.ai/model-api"
)

response = client.chat.completions.create(
    model="Qwen/Qwen3-32B",
    messages=[
        {"role": "user", "content": "What new opportunities might reasoning models bring to the market?"}
    ],
    max_completion_tokens=1024,
    stream=True
)

for chunk in response:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta
    if getattr(delta, "content", None):
        print(delta.content, end="", flush=True)
    if getattr(delta, "reasoning", None):
        print(delta.reasoning, end="", flush=True)

max_completion_tokens controls the generation budget for the request. For reasoning models, internal reasoning also consumes this budget. For more information about token usage and billing, see Token Usage and Billing.

If you do not need streaming, remove stream=True and read response.choices[0].message.content. Choose the appropriate model and parameters from the Models page based on your scenario.

3. Monitor usage

On the Home page, you can view usage data, including token consumption, spending, Top Models, Top API Keys, and member usage, to quickly understand the main sources of traffic. Member usage is visible only to administrators. To inspect request details and metrics data, see View Usage and View Metrics.

Get Started

Models

Usage

API Guides

Tool Integrations

First API Call

1. Create an API key

2. Call a model

Call via REST

Call via the OpenAI-compatible Python SDK

3. Monitor usage

​1. Create an API key

​2. Call a model

​Call via REST

​Call via the OpenAI-compatible Python SDK

​3. Monitor usage

1. Create an API key

2. Call a model

Call via REST

Call via the OpenAI-compatible Python SDK

3. Monitor usage