Why structured outputs?

Model update, November 2, 2026. Mistral Large 3 (mistral-large-2512) and Mistral Medium 3.5 were deprecated on October 31, 2026, and will be retired on April 30, 2027. Their designated successor, Mistral Large 4 (mistral-large-4), has been in public preview since October 6. The examples in this course use the alias mistral-large-latest, which still points to Mistral Large 3 according to its official specification (verified November 3, 2026). Similarly, mistral-medium-latest points to Mistral Medium 3.5. These aliases will remain valid until April 30, 2027. For new projects, test mistral-large-4, their designated successor.

The problem with free-form outputs

When you place a language model in the middle of an automated pipeline, you quickly run into a problem that rarely shows up on day one: its free-text response is unpredictable. The same piece of information can be phrased in dozens of different ways, and each variation forces your downstream code to parse it differently. What works during a demo breaks in production, often silently.

Take a mundane task. You ask Mistral to extract a product name and price from a commercial description. Without constraints, the model might reply:

The product is called "Widget Pro" and costs €49.99.

Or, for the same input just a few hours later:

Name: Widget Pro
Price: 49.99 EUR

Both answers are correct for a human reader. For your code, they have almost nothing in common: the decimal separator changed from a comma to a period, the currency symbol became an ISO code, and the sentence structure vanished. Each variation requires different parsing logic. In production, this leads to silent errors, broken pipelines, and hours of debugging.

JSON: the universal format for pipelines

JSON solves this problem by enforcing a machine-readable structure. The same extraction becomes unambiguous:

{
  "name": "Widget Pro",
  "price": 49.99,
  "currency": "EUR"
}

The code that consumes this response becomes simple and reliable again. Instead of writing a fragile regular expression, you write two stable lines:

import json

data = json.loads(response)
product_name = data["name"]
price = data["price"]

No more risky regexes, no more heuristic parsing. JSON is the de facto standard for system-to-system communication, making it the natural choice for LLM outputs in professional contexts.

Where the need arises

Structured outputs become essential as soon as the model’s response feeds something other than a screen. Information extraction is the most direct example: pulling entities—names, dates, amounts—from unstructured documents is only useful if the result drops directly into a system. Classification follows the same logic: categorizing support tickets, emails, or customer reviews requires the label and its confidence score to arrive in predictable fields, not buried in a sentence.

Data transformation takes this further by converting free text into structured records destined for a database. Agents and call chains depend on this just as much: an agent can only orchestrate a workflow if it returns a structured action, with the tool name and its parameters in stable keys. Finally, an LLM sometimes acts as an intermediary layer in front of a REST API that expects a specific payload—in this case, the format isn’t a convenience; it’s a contract.

The cost of malformed outputs

In production, reliability isn’t optional, and a malformed output costs you in three ways. Syntactically invalid JSON crashes your application during parsing, which is the most visible scenario but paradoxically the least dangerous. Missing fields silently corrupt your data: the record passes, but it’s incomplete, and the issue doesn’t surface until weeks later. Mismatched types—a string "42" where you expected the number 42—cause unexpected behavior in downstream calculations without ever raising an exception.

Mistral’s structured outputs address these three challenges with two complementary mechanisms. JSON Mode guarantees valid JSON output without enforcing a schema: you enable it with a parameter and guide the format via the prompt. Custom Structured Outputs go further by enforcing a precise schema, described using Pydantic in Python or Zod in TypeScript.

This course walks you through both approaches. You’ll learn how to choose the right one for your use case, write the corresponding code in both languages, and handle error cases with patterns that hold up in production.

Key takeaways

• Free-text outputs are unpredictable and unsuited for automated pipelines
• JSON is the standard format for machine-to-machine communication
• Mistral offers two mechanisms for structured outputs: JSON Mode and Custom Structured Outputs
• Output reliability is critical in production—structured outputs are an investment, not a luxury