What are structured outputs in the OpenAI API and how do you use them?

Structured outputs guarantee that a model's response strictly conforms to a developer-defined JSON schema. This eliminates the need for output parsing heuristics and makes AI outputs reliably machine-readable.

from openai import OpenAI
from pydantic import BaseModel

client = OpenAI()

# Method 1: Pydantic model (Python SDK - simplest approach)
class CodeReview(BaseModel):
    issues: list[str]
    severity: str  # "low" | "medium" | "high"
    suggested_fix: str
    confidence_score: float

# Responses API with structured output
response = client.responses.create(
    model="gpt-5.5",
    input="Review this Python function for bugs: def add(a, b): return a - b",
    text={
        "format": {
            "type": "json_schema",
            "json_schema": {
                "name": "code_review",
                "schema": CodeReview.model_json_schema(),
                "strict": True
            }
        }
    }
)
review = CodeReview.model_validate_json(response.output_text)
print(f"Severity: {review.severity}")
print(f"Issues: {review.issues}")

# Chat Completions with parse() helper (beta):
completion = client.beta.chat.completions.parse(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "Extract: Alice is 30, is an engineer."}],
    response_format=CodeReview,
)
result = completion.choices[0].message.parsed

Key differences between APIs: in the Responses API, use text.format with type: json_schema. In Chat Completions, use response_format with type: json_schema. Both support strict: true which enforces the schema constraint at the grammar level, eliminating the possibility of schema violations.

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