Benchmark: bignumber.js vs. big.js vs. decimal.js (I)asd

HTML Preparation code:

<!--script src="https://raw.githubusercontent.com/iriscouch/bigdecimal.js/v0.6.1/lib/bigdecimal.js"></script-->
<script src="https://cdnjs.cloudflare.com/ajax/libs/bignumber.js/9.0.1/bignumber.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/big.js/6.0.3/big.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/decimal.js/10.2.1/decimal.min.js"></script>
<!--script src="https://raw.githubusercontent.com/infusion/Fraction.js/v4.0.12/fraction.min.js"></script-->

Tests:

decimal.js
new Decimal(1804) .mul(new Decimal(10).pow(6 - 18)) .log(1.0001) .round() .neg() .toNumber();
Native
Math.log(1804 * 10 ** (6 - 18)) / Math.log(1.0001)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
decimal.js
Native

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the explanation of what is being tested in this benchmark.

**Benchmark Definition**

The benchmark compares three different libraries: `bignumber.js`, `big.js`, and `decimal.js`. These libraries are designed to handle large numbers and mathematical operations that would be slow or unreliable with standard JavaScript arithmetic.

**Options Compared**

The benchmark tests the performance of each library for a specific mathematical operation:

1. **`decimal.js`**: This library performs the calculation using decimal arithmetic, which is more precise than standard JavaScript floating-point arithmetic.
2. **Native (Math)**: This option uses the built-in `Math` functions in JavaScript, such as `Math.log`, to perform the calculation.
3. **`bignumber.js`**, **`big.js`**: These libraries also perform decimal arithmetic, but with different optimizations and features.

**Pros and Cons**

* **`decimal.js`**:
	+ Pros: More precise calculations, easy to use, and well-maintained.
	+ Cons: May be slower than other options due to the overhead of decimal arithmetic.
* **Native (Math)**:
	+ Pros: Fastest option, widely supported, and well-integrated with JavaScript.
	+ Cons: May produce imprecise results due to floating-point arithmetic limitations.
* **`bignumber.js`**, **`big.js`**:
	+ Pros: Fast performance, easy to use, and feature-rich (e.g., support for arbitrary-precision arithmetic).
	+ Cons: May be more complex to use than `decimal.js`, and some features may not be as well-maintained.

**Library Descriptions**

* **`decimal.js`**: A library that provides decimal arithmetic for JavaScript. It is designed to be easy to use and provides a simple API for performing calculations.
* **`bignumber.js`**: A library that provides arbitrary-precision arithmetic for JavaScript. It is designed to be fast and feature-rich, but may have a steeper learning curve.
* **`big.js`**: A library that provides arbitrary-precision arithmetic for JavaScript. It is designed to be fast and easy to use, with a focus on simplicity.

**Special JS Features or Syntax**

None mentioned in this benchmark.

**Other Alternatives**

If you need high-performance arithmetic libraries, you may also consider:

* **`Fraction.js`**: A library that provides rational arithmetic (fractions) for JavaScript.
* **`Arbitrary-precision libraries like libgmpjs`**: These libraries provide arbitrary-precision arithmetic and are often used in scientific computing applications.

Keep in mind that the choice of library depends on your specific use case, performance requirements, and ease of use preferences.

Related benchmarks:

bignumber.js vs. big.js vs. decimal.js (I)asd (version: 0)

- https://github.com/MikeMcl/bignumber.js/tree/v9.0.0 - https://github.com/MikeMcl/big.js/tree/v5.2.2 - https://github.com/MikeMcl/decimal.js/tree/v10.2.0

Comparing performance of: decimal.js vs Native

Created: 2 years ago by: Guest

Jump to the latest result

decimal.js

Native

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):