A practical guide to OpenAI JSON Mode in 2025

If you've ever tried to get a large language model (LLM) to output data in a specific format, you know it can be a bit of a struggle. One minute it gives you exactly what you asked for, the next it’s a jumble of text that breaks your code. For any business trying to build reliable applications, that kind of unpredictability just doesn’t work.

You need clean, structured data to do things like trigger a workflow, update a support ticket, or look up an order. Without it, you're stuck writing messy code to parse unpredictable strings, and that's a headache nobody wants.

OpenAI introduced JSON Mode to tackle this problem, offering a way to make sure the model's output is always a syntactically valid JSON object. It’s a helpful feature, but it’s not quite the magic wand you might be hoping for.

This guide will walk you through what OpenAI JSON Mode actually is, how to use it, where it tends to fall short, and why you might need something a bit more powerful to build production-ready AI systems.

What is OpenAI JSON Mode?

OpenAI JSON Mode is a feature in their API that forces a model to generate only strings that can be parsed into a valid JSON object. In other words, it guarantees syntactical correctness. When you flip this switch, you can be confident the model's response won't have common formatting mistakes like a missing comma, a stray bracket, or an unclosed quote.

But here’s the most important thing to get your head around: JSON Mode does not guarantee schema adherence. Even if you explicitly ask for specific fields in your prompt, the model isn't forced to follow that structure. It might leave out keys you marked as required, invent new ones you never asked for, or return a string where you expected a number. As one user on the OpenAI community forums discovered, even when properties are marked as "required," they can still be missing from the final response.

Think of it as a helpful first step. It cleans up the format but doesn't enforce the content, leaving a significant reliability gap for developers.

How to implement OpenAI JSON Mode

Getting started with OpenAI JSON Mode is pretty simple, but you have to follow a couple of specific rules to get it working correctly.

Key requirements for using OpenAI JSON Mode

There are two absolute must-haves to enable JSON Mode:

1. Set the response_format parameter: You have to include the object "response_format={ "type": "json_object" }" in your API call. This is the main switch that turns the feature on.

2. Instruct the model in your prompt: Your prompt, usually in the system message, has to include the string "JSON" somewhere. According to OpenAI's own documentation, this is a safeguard. Without it, a model that isn't sure what to do might just generate an endless stream of whitespace until it hits its token limit, leaving you with a long, empty, and expensive API call.

Supported models for OpenAI JSON Mode and a code example

JSON Mode works with most of the newer models, including "gpt-4o", "gpt-4-turbo", and "gpt-3.5-turbo".

Here’s a quick Python example of what a basic API call looks like. Let's say you want to pull a user's name and email from a support message:

from openai import OpenAI  

client = OpenAI(api_key="YOUR_API_KEY")  

response = client.chat.completions.create(  

  model="gpt-4o",  

  response_format={ "type": "json_object" },  

  messages=[  

    {"role": "system", "content": "You are a helpful assistant designed to extract user information and output it in JSON format."},  

    {"role": "user", "content": "Hi, my name is Jane Doe and my email is jane.doe@example.com. I'm having trouble with my recent order."}  

  ]  

)  

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

This call will most likely return a clean JSON object like this:

{  

  "name": "Jane Doe",  

  "email": "jane.doe@example.com"  

}  

Where relying on OpenAI JSON Mode can get tricky

While JSON Mode is definitely better than parsing plain text, building a production-grade application on it alone is a risky move. The feature has a few big challenges that can lead to fragile systems and a lot of extra work for your developers.

Challenge 1: Lack of schema adherence

This is the real catch. Since the model doesn't have to follow your requested schema, it can return JSON that’s technically valid but completely useless for your application. Required fields might be missing, the model could hallucinate extra fields you didn't ask for, or it might return a value with the wrong data type, like "price": "fifty-nine ninety-nine" instead of "59.99".

This forces your developers to write a ton of defensive code. They have to build a validation layer to check every response against the schema you actually need. If the validation fails, they have to set up retry logic, which means more API calls, higher costs, and a slower experience for your users.

Challenge 2: Brittle prompts and complex error handling

Let's be honest, the requirement to include the word "JSON" in your prompt feels a bit like a clunky workaround, especially when you're trying to craft a detailed system message. It's just one more thing to remember and can sometimes get in the way of the model's main task.

Beyond that, you have to handle all the other edge cases yourself. What happens if the response gets cut off because it hit the "max_tokens" limit? You're left with an incomplete, un-parseable JSON string. Or what if the model refuses to answer a prompt due to safety filters? The refusal message it sends back won't be in your desired JSON format, which could break your application if you haven't built specific logic to catch it. These issues make it clear that a simple API call is a long way from a robust system.

Challenge 3: OpenAI JSON Mode is just one piece of the puzzle

Getting a clean JSON output is only the beginning. Once you have the data, you still need to build the entire workflow around it. You need code to triage the support ticket, call another API to look up order details, update a database, or escalate to a human agent. The JSON is just the trigger; the real work comes after.

This is where the difference between a raw API and a full workflow platform becomes obvious. While the API gives you a tool, platforms like eesel AI provide the entire customizable workflow engine, letting you define not just the data format but the actions that follow, all without writing a single line of code.

From OpenAI JSON Mode to production-ready AI: Better alternatives

The issues with JSON Mode are real, but thankfully, there are better ways to build reliable AI-powered applications. Whether you’re a developer who wants more control or a business that just wants things to work, there's a solution that fits.

An upgrade to OpenAI JSON Mode: Structured outputs

OpenAI clearly recognized the limitations of JSON Mode and released a much more powerful feature called Structured Outputs. This is their recommended solution for anyone who needs reliable, schema-compliant data.

With Structured Outputs, you provide a formal JSON Schema as part of your API request. This forces the model to generate a response that is not only valid JSON but also strictly follows the structure you defined. It’s a huge step up for reliability.

Here’s a quick comparison:

Beyond OpenAI JSON Mode: Why tools like eesel AI are the final step

Even with Structured Outputs, you're still left with a good amount of developer work. You have to write and maintain JSON schemas, manage API calls, and build all the surrounding application logic and error handling. For many businesses, that’s a heavy lift that distracts from their main focus.

This is where a platform like eesel AI comes in, taking care of all that complexity for you. Instead of wrestling with APIs, you get a complete, end-to-end solution designed for business results.

• Go live in minutes: Forget about coding API calls and defining schemas. With eesel AI, you can connect your helpdesk (like Zendesk or Freshdesk) with a single click, and our AI agents are ready to start learning from your data right away.

• Total control: Our no-code prompt editor and workflow engine give you fine-grained control over the AI's persona, its knowledge sources, and the exact actions it can take. This goes far beyond what a simple JSON schema can do, allowing you to build complex, multi-step automations without a developer.

• Test with confidence: Building with raw APIs means you can only test your system in a live environment, which can be risky. eesel AI’s simulation mode lets you test your AI on thousands of your past tickets, giving you a precise forecast of its performance and automation rate before it ever interacts with a real customer.

Understanding OpenAI API pricing

When you use OpenAI's API directly, you pay for what you use based on "tokens," which are basically pieces of words. Every request has input tokens (your prompt) and output tokens (the model's response), and the cost per token varies depending on which model you use.

Here's a simplified look at the pricing for a popular model from the OpenAI pricing page:

This per-token model can lead to some unpredictable bills. If your JSON Mode calls fail and require retries, your costs go up. If you have a busy month with a high volume of support tickets, your bill could be much higher than you expected.

In contrast, platforms like eesel AI offer transparent, predictable pricing without per-resolution fees, so your costs don't penalize you for providing great support.

Choosing the right tool for the job

So, what's the final word on OpenAI JSON Mode? It's a handy feature for developers who need to make sure an API response is syntactically correct JSON. It's a useful tool for quick prototypes or simple internal tasks. However, its lack of schema enforcement makes it too unreliable for most production business workflows.

For developers building custom solutions from the ground up, OpenAI's Structured Outputs is a far better choice, offering the reliability needed for serious applications.

But for businesses that want to use AI to automate complex workflows like customer support, a dedicated, self-serve platform is the most efficient and reliable path. It handles the complexity so you can focus on the results.

Go beyond OpenAI JSON Mode: Automate your support workflows without the guesswork

Building reliable AI workflows with raw APIs is complex, time-consuming, and risky. eesel AI gives you a powerful, no-code platform to deploy AI agents that integrate seamlessly with your existing tools. Simulate on past data, customize every action, and go live in minutes, not months.

Start your free trial today or Book a demo to see it in action.