Structured Output

Get reliable, schema-conformant JSON from Parasail models using guided_json and response_format.

Overview

Parasail's OpenAI-compatible endpoint can return structured, machine-parseable output so you don't have to scrape JSON out of free-form text. There are two distinct ways to do this:

• Prompt-only JSON—you ask the model for JSON in the prompt. Simple, but the model can still drift (extra prose, missing keys, invalid JSON).

• Server-enforced schemas—you pass a schema with the request and the server constrains decoding so the output is guaranteed to match. On Parasail this is done with guided_json or response_format (JSON Schema).

Use server-enforced schemas whenever you need to parse the result programmatically.

Prompt-only JSON vs. server-enforced schemas

Approach	How	Guarantee
Prompt-only JSON	Describe the desired JSON in the prompt	None—best-effort
guided_json	Pass a JSON Schema in extra_body	Output is constrained to the schema
response_format (JSON Schema)	Pass response_format={"type": "json_schema", ...}	Output is constrained to the schema

Both server-enforced approaches use the same Parasail base URL:

https://api.parasail.io/v1

Supported models

The following models support guided/structured decoding as of June 2026. Capabilities change over time—use the models endpoint to check model availability, then validate structured-output support with a small test request.

Treat this table as a starting point for testing. Before relying on a constraint in production, send a small request with the same model and constraint type you plan to use.

Model	Guided JSON / JSON Schema	Regex	Choice
parasail-llama-33-70b-fp8	Y	Y	Y
parasail-llama-4-scout-instruct	Y	Y	Y
parasail-llama-4-maverick-instruct-fp8	Y	Y	Y
parasail-qwen3-30b-a3b	Y		Y
parasail-qwen3-235b-a22b	Y		Y
parasail-qwen3-32b	Y		Y
parasail-gemma3-27b-it	Y	Y	Y
parasail-mistral-devstral-small	Y	Y	Y

Server-enforced JSON with guided_json

Pass a JSON Schema via the OpenAI SDK's extra_body parameter. The server uses guided decoding to ensure the response matches your schema.

import json
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>",
)

schema = {
    "type": "object",
    "properties": {
        "location": {"type": "string"},
        "date": {"type": "string", "format": "date"},
    },
    "required": ["location", "date"],
    "additionalProperties": False,
}

completion = client.chat.completions.create(
    model="parasail-qwen3-32b",
    messages=[
        {"role": "user", "content": "What's the weather like in Manhattan Beach on 2025-06-03?"}
    ],
    extra_body={"guided_json": schema},
)

# The content is already constrained to match the schema
data = json.loads(completion.choices[0].message.content)
print(json.dumps(data, indent=2))

Expected output:

{
  "location": "Manhattan Beach",
  "date": "2025-06-03"
}

Server-enforced JSON with response_format

Parasail also accepts the OpenAI response_format field with type: "json_schema"—the same shape OpenAI uses. Combine strict: true with additionalProperties: false for the tightest output.

import json
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>",
)

schema = {
    "type": "object",
    "properties": {
        "capital": {"type": "string"},
        "country": {"type": "string"},
    },
    "required": ["capital", "country"],
    "additionalProperties": False,
}

resp = client.chat.completions.create(
    model="parasail-qwen3-32b",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    response_format={
        "type": "json_schema",
        "json_schema": {"name": "capital_lookup", "strict": True, "schema": schema},
    },
)

data = json.loads(resp.choices[0].message.content)
print(json.dumps(data, indent=2))

Expected output:

{
  "capital": "Paris",
  "country": "France"
}

Prompt-only JSON

If you don't need a hard guarantee, you can simply ask the model for JSON. This works with any model but is best-effort—always validate and json.loads() the result before relying on it.

import json
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>",
)

resp = client.chat.completions.create(
    model="parasail-qwen3-32b",
    messages=[
        {
            "role": "user",
            "content": (
                "Return ONLY a JSON object with keys 'location' and 'date' "
                "for the weather in Manhattan Beach on 2025-06-03. No prose."
            ),
        }
    ],
)

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

Other guided decoding constraints (regex and choice)

For models listed with regex or choice support in the table above, guided decoding can also constrain output to a regular expression or a fixed set of choices via extra_body:

# Constrain to an ISO date
extra_body = {"guided_regex": r"\d{4}-\d{2}-\d{2}"}

# Constrain to one of a fixed set of values
extra_body = {"guided_choice": ["red", "green", "blue"]}

cURL example

curl https://api.parasail.io/v1/chat/completions \
  -H "Authorization: Bearer $PARASAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "parasail-qwen3-32b",
        "messages": [{"role": "user", "content": "What is the capital of France?"}],
        "guided_json": {
          "type": "object",
          "properties": {
            "capital": {"type": "string"},
            "country": {"type": "string"}
          },
          "required": ["capital", "country"],
          "additionalProperties": false
        }
      }'

Troubleshooting

Symptom	Likely cause	Fix
Model ignores the constraint	Using prompt-only JSON	Switch to guided_json or response_format for server-enforced schemas.
Valid JSON but unexpected extra keys	additionalProperties not set	Add "additionalProperties": false to your schema.
Need to stream large JSON	Output too large for one chunk	Set stream=True; constraints still apply. Concatenate delta.content and parse at the end.

Next steps

• Chat completions guide

• Tool/function calling guide

• Chat Completions API reference

• Models endpoint