Gemini 1.5 vs Pro Comparison

Other differences:

Price: Gemini 1.5 Pro is more expensive than Gemini 1.5.

Usage:
Gemini 1.5: Suitable for relatively small-scale tasks such as personal research, projects, etc.
Gemini 1.5 Pro: Suitable for large-scale data processing, complex tasks, corporate use, etc.

Tips for choosing:

Amount of data to be processed and complexity of tasks:
Small data & simple tasks: Gemini 1.5
Large data & complex tasks: Gemini 1.5 Pro
Budget: Gemini 1.5 Pro is more expensive than Gemini 1.5.
Purpose of use: Individual vs. corporate, etc.

There are two previous versions,

Gemini 1.5 (https://deepmind.google/technologies/gemini/)
Released on May 14, 2024
Provides code explanation, automatic unit test generation, and code conversion features with a 1 million-word window applied.
Improved model size and performance that can handle over 10 million tokens of text.
Gemini 1.0 (https://technologymagazine.com/articles/google-unveils-gemini-its-largest-and-most-capable-ai-model)
Released on February 7, 2024
Released 3 models (Ultra, Pro, Nano)
Differentiated model size and features

Gemini 1.5 vs Gemini 1.5 Pro Comparison Examples

1. Code Analysis and Explanation

Gemini 1.5:

def add_numbers(a, b):
  """Adds two numbers."""

• Provides only simple comments and lacks in-depth analysis of code structure or meaning.

Gemini 1.5 Pro:

def add_numbers(a: int, b: int) -> int:
  """Adds two integers and returns the result.

  Args:
    a: The first integer.
    b: The second integer.

  Returns:
    The sum of the two numbers.
  """

• Provides detailed comments on the code, clearly explaining the function's input, output, and functionality.
• Accurately understands the structure and meaning of the code, providing more efficient analysis.

2. Automatic Unit Test Generation

Gemini 1.5:

Users must write unit tests themselves.

Gemini 1.5 Pro:

import unittest

class TestAddNumbers(unittest.TestCase):

  def test_add_positive_numbers(self):
    self.assertEqual(add_numbers(1, 2), 3)

  def test_add_negative_numbers(self):
    self.assertEqual(add_numbers(-1, -2), -3)

  def test_add_zero(self):
    self.assertEqual(add_numbers(0, 0), 0)

if __name__ == "__main__":

• Automatically generates unit tests for the code.
• Verifies the functionality of the code through test cases, increasing development speed.

3. Code Conversion

Gemini 1.5:

Does not provide code conversion functionality.

Gemini 1.5 Pro:

# Python code
def add_numbers(a, b):
  return a + b

# Conversion to Java code
public class AddNumbers {
  public static int add(int a, int b) {
    return a + b;
  }

• Converts code between various programming languages to enhance code compatibility.

4. Amount of text processed

Gemini 1.5:

Can handle over 10 million tokens of text.

Gemini 1.5 Pro:

Can handle over 32 million tokens of text.

• Processes larger amounts of information, providing more accurate and reliable results.

5. Other

• Gemini 1.5 Pro has a larger model size and better performance than Gemini 1.5.
• Gemini 1.5 Pro provides more features, especially suitable for large-scale data processing and complex tasks.

Conclusion

Gemini 1.5 Pro is a more powerful and versatile AI model than Gemini 1.5. It can be used in various tasks such as code analysis, automatic unit test generation, code conversion, and is especially suitable for large-scale data processing and complex tasks.