> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Structured Outputs

> Get JSON responses from models. Supports json_object and json_schema modes.

## JSON mode

Force the model to return valid JSON by setting `response_format`:

```python theme={null}
from openai import OpenAI

client = OpenAI(
    base_url="https://kymaapi.com/v1",
    api_key="ky-your-api-key"
)

response = client.chat.completions.create(
    model="qwen-3-32b",
    messages=[{"role": "user", "content": "List 3 programming languages with name and year. Return JSON."}],
    response_format={"type": "json_object"}
)

import json
data = json.loads(response.choices[0].message.content)
print(data)
# [{"name": "Python", "year": 1991}, {"name": "JavaScript", "year": 1995}, ...]
```

## JSON Schema (strict mode)

Define the exact shape of the response:

```python theme={null}
response = client.chat.completions.create(
    model="qwen-3.6-plus",
    messages=[{"role": "user", "content": "List 2 colors"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "colors",
            "schema": {
                "type": "object",
                "properties": {
                    "colors": {
                        "type": "array",
                        "items": {"type": "string"}
                    }
                },
                "required": ["colors"]
            }
        }
    }
)

data = json.loads(response.choices[0].message.content)
print(data)
# {"colors": ["red", "blue"]}
```

The response will always match your schema. No parsing errors.

## Which models support structured outputs?

| Model            | json\_object | json\_schema |
| ---------------- | :----------: | :----------: |
| Qwen 3 32B       |       ✅      |       ✅      |
| Qwen 3 Coder     |       ✅      |       ✅      |
| Qwen 3.6 Plus    |       ✅      |       ✅      |
| Llama 3.3 70B    |       ✅      |       ✅      |
| Kimi K2.5        |       ✅      |       ✅      |
| GPT-OSS 120B     |       ✅      |       ✅      |
| Gemini 2.5 Flash |       ✅      |       ✅      |
| Gemini 3 Flash   |       ✅      |       ✅      |
| Gemma 4 31B      |       ✅      |  ⚠️ Limited  |

<Tip>
  If a model doesn't support `json_schema`, use `json_object` mode and describe the format in your prompt. It works with all models.
</Tip>

## With Pydantic (Python)

```python theme={null}
from pydantic import BaseModel
from openai import OpenAI

class MovieReview(BaseModel):
    title: str
    rating: float
    summary: str

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-your-api-key")

response = client.chat.completions.create(
    model="qwen-3-32b",
    messages=[{"role": "user", "content": "Review the movie Inception"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "movie_review",
            "schema": MovieReview.model_json_schema()
        }
    }
)

review = MovieReview.model_validate_json(response.choices[0].message.content)
print(f"{review.title}: {review.rating}/10")
```

## With TypeScript/Zod

```typescript theme={null}
import OpenAI from 'openai';
import { z } from 'zod';
import { zodToJsonSchema } from 'zod-to-json-schema';

const ColorSchema = z.object({
  colors: z.array(z.string())
});

const client = new OpenAI({
  baseURL: 'https://kymaapi.com/v1',
  apiKey: 'ky-your-api-key'
});

const response = await client.chat.completions.create({
  model: 'qwen-3.6-plus',
  messages: [{ role: 'user', content: 'List 3 colors' }],
  response_format: {
    type: 'json_schema',
    json_schema: {
      name: 'colors',
      schema: zodToJsonSchema(ColorSchema)
    }
  }
});

const data = ColorSchema.parse(JSON.parse(response.choices[0].message.content));
```