---
title: Working with Decimal Types in Instructor
description: Learn how to use Python Decimal types for precise financial calculations and numeric data extraction with Instructor.
---

## See Also

- [Types](../concepts/types.md) - Working with different data types
- [Fields](../concepts/fields.md) - Customizing field validation
- [Field Validation](../learning/patterns/field_validation.md) - Field-level validation patterns
- [Validation](../concepts/validation.md) - Core validation concepts

# Using Decimals

Extract precise decimal values for financial calculations using Python's `Decimal` type.

```python
from decimal import Decimal
from pydantic import BaseModel, field_validator
import instructor


class Receipt(BaseModel):
    item: str
    price: Decimal

    @field_validator('price', mode='before')
    @classmethod
    def parse_price(cls, v):
        if isinstance(v, str):
            return Decimal(v)
        return v


client = instructor.from_provider("openai/gpt-4.1-mini")

receipt = client.create(
    messages=[{"role": "user", "content": "Coffee costs $4.99"}],
    response_model=Receipt,
)

print(f"Item: {receipt.item}")
print(f"Price: {receipt.price}")  # Decimal('4.99')
print(f"Type: {type(receipt.price)}")  # <class 'decimal.Decimal'>
```

The `field_validator` ensures string values from LLM responses are properly converted to Decimal objects for precise financial calculations.