Structured Output

Structured Output lets models return JSON content that can be parsed by programs, making it easier to validate, parse, and automate downstream processing.

Use Cases

Extract structured fields from news articles, such as title, time, and link.
Perform sentiment analysis on product reviews, including polarity, intensity, and keywords.
Generate recommendation lists from transaction or browsing history, such as products, reasons, prices, and promotional information.

Supported Models

Most online language models support JSON mode; VL models currently do not.
Model capabilities are continuously updated. For the latest support information, refer to the model detail page in Models.

Output Format

The platform supports controlling model output format through response_format. This includes:

JSON mode (json_object): Use json_object when you only need the model to return valid JSON.
Strict schema (json_schema): Use json_schema when the output must follow a fixed structure.

JSON Mode

JSON mode lets the model return a JSON string instead of free-form text.

response_format = {"type": "json_object"}

When using JSON mode, we still recommend specifying the required fields and format in your prompt.

Strict Schema

If you need the model to output data in a specific structure, you can pass in a JSON Schema. The model is then constrained to the field names, types, and required fields you define, reducing post-processing costs.

response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "city",
        "schema": {
            "type": "object",
            "properties": {
                "capital": {"type": "string"},
            },
            "required": ["capital"],
        },
    },
}

Best Practices

Define output constraints clearly: In your prompt, state that the model should output JSON only and should not include explanatory text.
Strengthen schema constraints: When using json_schema, clearly define field names, types, required fields, optional fields, examples, and “no extra fields” when applicable.
Reduce randomness: Use a lower temperature, such as 0.2-0.5, to reduce randomness and drift.
Prefer non-streaming responses: Prefer stream=False. If you use streaming, wait until all chunks are received before calling json.loads.
Control output length: Set a reasonable max_completion_tokens value to avoid truncating the JSON object.
Handle parsing failures: If parsing fails, retry with stricter constraints and record the raw output for troubleshooting.
Validate external JSON: We recommend handling edge cases where the model returns incomplete or invalid JSON on the application side. Validate the JSON returned by the model before using it, and avoid concatenating untrusted content directly into SQL queries or code paths.

Examples

The examples read the API key from an environment variable to avoid writing secrets into code.

export API_KEY="YOUR_API_KEY"

JSON Mode

import os
import json
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["API_KEY"],
    base_url="https://api.scitix.ai/model-api",
)

resp = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M2.7",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant designed to output JSON.",
        },
        {
            "role": "user",
            "content": "What is the capital of France? Please respond as {\"capital\": ...}",
        },
    ],
    response_format={"type": "json_object"},
    temperature=0.3,
    max_completion_tokens=256,
    stream=False,
)

raw = resp.choices[0].message.content
print("RAW:", raw)

try:
    data = json.loads(raw)
    print("PARSED:", data)
except json.JSONDecodeError:
    print("JSON parsing failed; retry with lower temperature or higher max_completion_tokens")

Sample output:

{"capital": "Paris"}

Strict Schema

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["API_KEY"],
    base_url="https://api.scitix.ai/model-api",
)

response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "city",
        "schema": {
            "type": "object",
            "properties": {
                "capital": {"type": "string"},
            },
            "required": ["capital"],
        },
    },
}

resp = client.chat.completions.create(
    model="Qwen/Qwen3-32B",
    messages=[{"role": "user", "content": "Capital of France?"}],
    response_format=response_format,
)

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

Get Started

Models

Usage

API Guides

Tool Integrations

Structured Output

Use Cases

Supported Models

Output Format

JSON Mode

Strict Schema

Best Practices

Examples

JSON Mode

Strict Schema

​Use Cases

​Supported Models

​Output Format

​JSON Mode

​Strict Schema

​Best Practices

​Examples

​JSON Mode

​Strict Schema

Use Cases

Supported Models

Output Format

JSON Mode

Strict Schema

Best Practices

Examples

JSON Mode

Strict Schema