Structured output (JSON mode)

There are two ways to get machine-readable JSON: the OpenAI response_format field, or a tool whose parameters are your schema. Which one works depends on your key's group.

When response_format is dropped there is no error. The request succeeds, but the model is not constrained, so you may get prose or JSON in a Markdown fence. Always validate the output.

#Which kind of group do I have?

Send any request to /v1/messages with your key. A 404 that says the platform only supports /v1/chat/completions means a Chat Completions only group, where response_format passes through. Any other answer means it may be dropped, so use the tool schema option.

#Option 1: response_format

On Chat Completions only groups, use json_schema if the model supports it, or json_object. Also ask for JSON in the prompt; some models require it.

import json, os
from openai import OpenAI

client = OpenAI(base_url="https://bigmodel.io/v1", api_key=os.environ["BIGMODEL_API_KEY"])

completion = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[
        {"role": "system", "content": "Reply with a JSON object with keys name and year."},
        {"role": "user", "content": "Which language did Guido van Rossum create?"},
    ],
    response_format={"type": "json_object"},
)
data = json.loads(completion.choices[0].message.content)
print(data["name"], data["year"])

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://bigmodel.io/v1", apiKey: process.env.BIGMODEL_API_KEY });

const completion = await client.chat.completions.create({
  model: "deepseek-v4-flash",
  messages: [
    { role: "system", content: "Reply with a JSON object with keys name and year." },
    { role: "user", content: "Which language did Guido van Rossum create?" },
  ],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(completion.choices[0].message.content));

curl https://bigmodel.io/v1/chat/completions \
  -H "Authorization: Bearer $BIGMODEL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "system", "content": "Reply with a JSON object with keys name and year."},
      {"role": "user", "content": "Which language did Guido van Rossum create?"}
    ],
    "response_format": {"type": "json_object"}
  }'

Support differs per model: some accept json_object but not json_schema, and strict schema enforcement is not universal. Check the model vendor's documentation for the model you choose.

#Option 2: a tool schema (works on every group)

tools and tool_choice survive every conversion, so a forced tool call is the portable way to get schema-shaped JSON.

import json, os
from openai import OpenAI

client = OpenAI(base_url="https://bigmodel.io/v1", api_key=os.environ["BIGMODEL_API_KEY"])

schema = {
    "type": "object",
    "properties": {"name": {"type": "string"}, "year": {"type": "integer"}},
    "required": ["name", "year"],
}
completion = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Which language did Guido van Rossum create?"}],
    tools=[{"type": "function", "function": {"name": "answer", "description": "Return the answer", "parameters": schema}}],
    tool_choice={"type": "function", "function": {"name": "answer"}},
)
data = json.loads(completion.choices[0].message.tool_calls[0].function.arguments)
print(data)

const schema = {
  type: "object",
  properties: { name: { type: "string" }, year: { type: "integer" } },
  required: ["name", "year"],
};
const completion = await client.chat.completions.create({
  model: "glm-5.3",
  messages: [{ role: "user", content: "Which language did Guido van Rossum create?" }],
  tools: [{ type: "function", function: { name: "answer", description: "Return the answer", parameters: schema } }],
  tool_choice: { type: "function", function: { name: "answer" } },
});
console.log(JSON.parse(completion.choices[0].message.tool_calls[0].function.arguments));

If a model does not accept a forced function, use "tool_choice": "required" or "auto" with a clear instruction.

#Validate the result

Neither option guarantees valid data from every model. Parse and validate, and retry once on failure:

from pydantic import BaseModel, ValidationError

class Answer(BaseModel):
    name: str
    year: int

try:
    answer = Answer.model_validate_json(raw_json)
except ValidationError as err:
    # send err back to the model and ask it to fix the JSON, or retry
    raise

#Verify the setup

Run the cURL example. choices[0].message.content should be a JSON object such as {"name": "Python", "year": 1991}, with no surrounding text. If you get prose, response_format was dropped or is not supported by the model; switch to the tool schema.

#Troubleshooting

The reply is prose, or JSON inside a Markdown code fence. response_format was dropped on your group or ignored by the model. Use the tool schema option, or strip the fence before parsing.

400 from the provider about response_format. The model does not support the format you sent, often json_schema. Use json_object, or the tool schema option.

The JSON is cut off. The reply hit max_tokens; finish_reason is length. Raise max_tokens or ask for a smaller object.

seed, n or logprobs seem to have no effect. On converted groups these fields are dropped along with response_format. Only Chat Completions only groups pass them through, and then only if the model supports them.

503 api_error "No available accounts". The model is not served to your key's group. See Model IDs.

#Related

• Tool calling for the full tool loop.
• Chat Completions API reference.
• Errors.