ChatGPT-4.5 vs Grok 3: Coding comparison

So, Grok 3 and ChatGPT-4.5 are finally here. Two of the most advanced AI models, both claiming to be smarter, faster, and better than ever. But which one actually delivers? Grok 3, released on February 17, 2025, is xAI’s latest model. It’s built to be more conversational, sharper at coding, and deeply integrated with X (formerly Twitter). It promises real-time insights and a more interactive experience. ChatGPT-4.5, launched on February 27, 2025, takes reasoning, problem-solving, and memory to the next level. OpenAI calls it their most capable model yet, designed to handle technical and creative tasks with greater accuracy. Numbers and benchmarks only tell part of the story. What really matters is how well these AIs perform in real-world scenarios. Can they solve tough coding challenges? Do they actually understand complex problems, or just generate convincing answers? To find out, I am putting both models to the test. Let’s see which one truly stands out. Table of Contents Grok 3 System Card ChatGPT-4.5 System Card Coding Challenge Physics-Based Square in a Spinning Circle Dynamic 3D Shifting Panels Animation in JavaScript Modern Photo Editor App in Python Procedural City Skyline Generator Final Verdict TL;DR If you just want the results, Grok 3 outperforms ChatGPT-4.5 in real-world coding tasks. It delivers cleaner code, better physics simulations, and a more polished UI. ChatGPT-4.5? Not even close. It struggled with execution, gave blank screens, and felt half-baked. And yeah, that checks out. Grok 3 is built for coding and problem-solving, while ChatGPT-4.5 is still in beta and clearly needs more work. Grok 3 System Card Grok 3 was released by Elon Musk’s xAI. It is designed for advanced coding tasks and logical reasoning. It focuses on code generation and debugging. It improves multi-step problem-solving and delivers faster context-aware responses. It comes in four modes, each with a unique purpose. Grok 3 Mini is for quick answers. Grok 3 Think Mode boosts logical reasoning. Grok 3 Big Brain Mode handles complex coding. Grok 3 DeepSearch digs deep into data. To access Grok 3 modes, users need an X Premium+ subscription, priced at $40 per month. Those who want more power can opt for SuperGrok at $30 per month, unlocking extra features. One of its most debated features is the Unhinged Voice Mode. This gives Grok 3 a bold, sarcastic personality. Some users love the humor. Others think it makes AI unpredictable. The debate is ongoing: Should AI be fun, or should it just stick to facts? Grok 3 performs well in benchmarks. It scored 75 in Science and leads in technical problem-solving. It got 57 in Coding and proved its programming skills. In Math, it scored 52 and beat some models but still fell behind the best. Grok 3 Mini scored lower but held its ground against Gemini-2 Pro and DeepSeek-V3. With fast responses, strong coding skills, and a personality that sparks debate, Grok 3 brings something different to the AI race. The real test is how well it handles actual coding challenges. That is where it must prove itself. ChatGPT-4.5 System Card ChatGPT-4.5 was released by OpenAI on February 27, 2025. It is designed for advanced reasoning, coding, and problem-solving. It improves memory and provides more accurate responses. It handles complex technical tasks with better efficiency. At launch, ChatGPT-4.5 was only available for Pro users. Sam Altman later announced that it would be available for Plus users as well. ChatGPT Plus costs $20 per month and provides access to the latest model with faster responses. ChatGPT Pro costs $200 per month and offers higher usage limits with priority access. The biggest upgrade in ChatGPT-4.5 is its ability to handle multi-step reasoning and logic-based problems. It is better at coding and debugging. It also improves data analysis and creative writing. It generates more natural and context-aware responses. ChatGPT-4.5 is a powerful AI for developers, researchers, and businesses. Its value depends on how much reasoning, coding, and deep problem-solving matter to the user. ChatGPT vs Grok ChatGPT-4.5 is built on GPT-4 and GPT-4o. It has better reasoning and coding skills. OpenAI has improved its AI models over time. Grok models are newer but improving fast. The chart shows Grok 0 launched in 2023. It was followed by Grok 1, Grok 1.5, and Grok 2. Grok 3 has made big improvements. GPT-4o still has the highest MLU score. ChatGPT-4.5 is expected to be even better. Grok 3 is getting stronger but still needs time to catch up. ChatGPT-4.5 is ahead for now. If Grok keeps growing this fast, it might close the gap soon. Coding Challenge AI models are getting better at coding, but how well do they handle real-world programming challenges? To find out, I tested Grok 3 and ChatGPT-4.5 with three complex coding tasks. Each challenge requires logic, creativity, and technical skills.

Mar 13, 2025 - 15:41

ChatGPT-4.5 vs Grok 3: Coding comparison

So, Grok 3 and ChatGPT-4.5 are finally here. Two of the most advanced AI models, both claiming to be smarter, faster, and better than ever. But which one actually delivers?

Grok 3, released on February 17, 2025, is xAI’s latest model. It’s built to be more conversational, sharper at coding, and deeply integrated with X (formerly Twitter). It promises real-time insights and a more interactive experience.

ChatGPT-4.5, launched on February 27, 2025, takes reasoning, problem-solving, and memory to the next level. OpenAI calls it their most capable model yet, designed to handle technical and creative tasks with greater accuracy.

Numbers and benchmarks only tell part of the story. What really matters is how well these AIs perform in real-world scenarios. Can they solve tough coding challenges? Do they actually understand complex problems, or just generate convincing answers?

To find out, I am putting both models to the test. Let’s see which one truly stands out.

Grok 3 System Card
ChatGPT-4.5 System Card
Coding Challenge
1. Physics-Based Square in a Spinning Circle
2. Dynamic 3D Shifting Panels Animation in JavaScript
3. Modern Photo Editor App in Python
4. Procedural City Skyline Generator
Final Verdict

TL;DR

If you just want the results, Grok 3 outperforms ChatGPT-4.5 in real-world coding tasks. It delivers cleaner code, better physics simulations, and a more polished UI. ChatGPT-4.5? Not even close. It struggled with execution, gave blank screens, and felt half-baked.

And yeah, that checks out. Grok 3 is built for coding and problem-solving, while ChatGPT-4.5 is still in beta and clearly needs more work.

Grok 3 System Card

Grok 3 was released by Elon Musk’s xAI. It is designed for advanced coding tasks and logical reasoning. It focuses on code generation and debugging. It improves multi-step problem-solving and delivers faster context-aware responses.

It comes in four modes, each with a unique purpose. Grok 3 Mini is for quick answers. Grok 3 Think Mode boosts logical reasoning. Grok 3 Big Brain Mode handles complex coding. Grok 3 DeepSearch digs deep into data.

To access Grok 3 modes, users need an X Premium+ subscription, priced at $40 per month. Those who want more power can opt for SuperGrok at $30 per month, unlocking extra features.

One of its most debated features is the Unhinged Voice Mode. This gives Grok 3 a bold, sarcastic personality. Some users love the humor. Others think it makes AI unpredictable. The debate is ongoing: Should AI be fun, or should it just stick to facts?

Grok 3 performs well in benchmarks. It scored 75 in Science and leads in technical problem-solving. It got 57 in Coding and proved its programming skills.

In Math, it scored 52 and beat some models but still fell behind the best. Grok 3 Mini scored lower but held its ground against Gemini-2 Pro and DeepSeek-V3.

With fast responses, strong coding skills, and a personality that sparks debate, Grok 3 brings something different to the AI race. The real test is how well it handles actual coding challenges. That is where it must prove itself.

ChatGPT-4.5 System Card

ChatGPT-4.5 was released by OpenAI on February 27, 2025. It is designed for advanced reasoning, coding, and problem-solving. It improves memory and provides more accurate responses. It handles complex technical tasks with better efficiency.

At launch, ChatGPT-4.5 was only available for Pro users. Sam Altman later announced that it would be available for Plus users as well. ChatGPT Plus costs $20 per month and provides access to the latest model with faster responses. ChatGPT Pro costs $200 per month and offers higher usage limits with priority access.

The biggest upgrade in ChatGPT-4.5 is its ability to handle multi-step reasoning and logic-based problems. It is better at coding and debugging. It also improves data analysis and creative writing. It generates more natural and context-aware responses.

ChatGPT-4.5 is a powerful AI for developers, researchers, and businesses. Its value depends on how much reasoning, coding, and deep problem-solving matter to the user.

ChatGPT vs Grok

ChatGPT-4.5 is built on GPT-4 and GPT-4o. It has better reasoning and coding skills. OpenAI has improved its AI models over time.

Grok models are newer but improving fast. The chart shows Grok 0 launched in 2023. It was followed by Grok 1, Grok 1.5, and Grok 2. Grok 3 has made big improvements.

GPT-4o still has the highest MLU score. ChatGPT-4.5 is expected to be even better. Grok 3 is getting stronger but still needs time to catch up. ChatGPT-4.5 is ahead for now. If Grok keeps growing this fast, it might close the gap soon.

Coding Challenge

AI models are getting better at coding, but how well do they handle real-world programming challenges? To find out, I tested Grok 3 and ChatGPT-4.5 with three complex coding tasks. Each challenge requires logic, creativity, and technical skills.

1. Physics-Based Square in a Spinning Circle

Let’s see how well the AI models handle real-world physics. The task was to create a 2D simulation where a square moves inside a spinning circular container. The square needed to follow gravity, bounce off walls, and react to the spinning motion.

prompt:

Create a 2D physics simulation in Pygame where a square moves inside a spinning circular container. The square must follow gravity, collisions, and friction, reacting naturally by tumbling, sliding, and bouncing.

Grok 3 Response:

import pygame
import math
import numpy as np

# Initialize Pygame
pygame.init()

# Screen settings
WIDTH = 800
HEIGHT = 800
screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Spinning Container Physics Simulation")
clock = pygame.time.Clock()

# Colors
WHITE = (255, 255, 255)
RED = (255, 0, 0)
BLUE = (0, 0, 255)

# Physics constants
GRAVITY = 0.5
FRICTION = 0.98
RESTITUTION = 0.7  # Bounce factor

class Square:
    def __init__(self, x, y, size):
        self.pos = np.array([float(x), float(y)])
        self.vel = np.array([0.0, 0.0])
        self.angle = 0.0
        self.angular_vel = 0.0
        self.size = size
        self.mass = 1.0
        self.moment_of_inertia = (self.mass * size * size) / 6.0

    def get_corners(self):
        # Calculate the four corners of the square based on position and angle
        half_size = self.size / 2
        corners = [
            np.array([-half_size, -half_size]),
            np.array([half_size, -half_size]),
            np.array([half_size, half_size]),
            np.array([-half_size, half_size])
        ]
        # Rotate and translate corners
        rotated_corners = []
        for corner in corners:
            rotated_x = corner[0] * math.cos(self.angle) - corner[1] * math.sin(self.angle)
            rotated_y = corner[0] * math.sin(self.angle) + corner[1] * math.cos(self.angle)
            rotated_corners.append(self.pos + np.array([rotated_x, rotated_y]))
        return rotated_corners

    def draw(self, surface, offset_angle=0):
        corners = self.get_corners()
        # Apply container rotation offset
        rotated_corners = []
        for corner in corners:
            centered = corner - np.array([WIDTH/2, HEIGHT/2])
            rot_x = centered[0] * math.cos(offset_angle) - centered[1] * math.sin(offset_angle)
            rot_y = centered[0] * math.sin(offset_angle) + centered[1] * math.cos(offset_angle)
            rotated_corners.append((rot_x + WIDTH/2, rot_y + HEIGHT/2))
        pygame.draw.polygon(surface, RED, rotated_corners)

class Container:
    def __init__(self, radius):
        self.radius = radius
        self.angle = 0.0
        self.angular_vel = 0.05  # Rotation speed

    def update(self):
        self.angle += self.angular_vel

    def draw(self, surface):
        # Draw container outline
        pygame.draw.circle(surface, BLUE, (WIDTH//2, HEIGHT//2), self.radius, 2)
        # Draw a reference line to show rotation
        end_x = WIDTH/2 + self.radius * math.cos(self.angle)
        end_y = HEIGHT/2 + self.radius * math.sin(self.angle)
        pygame.draw.line(surface, BLUE, (WIDTH/2, HEIGHT/2), (end_x, end_y), 2)

def handle_collision(square, container):
    corners = square.get_corners()
    center = np.array([WIDTH/2, HEIGHT/2])

    for corner in corners:
        # Distance from container center to corner
        dist_vec = corner - center
        dist = np.linalg.norm(dist_vec)

        if dist >= container.radius:
            # Collision detected
            normal = dist_vec / dist  # Normal vector pointing outward
            penetration = dist - container.radius

            # Move square back to prevent penetration
            square.pos -= normal * penetration

            # Reflect velocity
            relative_vel = square.vel + square.angular_vel * np.array([-normal[1], normal[0]]) * square.size/2
            vel_along_normal = np.dot(relative_vel, normal)

            if vel_along_normal < 0:  # Moving into wall
                impulse = -(1 + RESTITUTION) * vel_along_normal / (1/square.mass + (square.size/2)**2/square.moment_of_inertia)

                # Apply linear impulse
                square.vel += impulse * normal / square.mass

                # Apply angular impulse
                r = corner - square.pos
                cross = r[0] * normal[1] - r[1] * normal[0]
                square.angular_vel += impulse * cross / square.moment_of_inertia

def main():
    container = Container(300)
    square = Square(WIDTH/2, HEIGHT/2 - 200, 50)

    running = True
    while running:
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                running = False

        # Update container
        container.update()

        # Apply gravity (adjusted for container rotation)
        gravity_dir = np.array([0, GRAVITY])
        rotated_gravity = np.array([
            gravity_dir[0] * math.cos(container.angle) - gravity_dir[1] * math.sin(container.angle),
            gravity_dir[0] * math.sin(container.angle) + gravity_dir[1] * math.cos(container.angle)
        ])
        square.vel += rotated_gravity

        # Update square physics
        square.pos += square.vel
        square.angle += square.angular_vel

        # Apply friction
        square.vel *= FRICTION
        square.angular_vel *= FRICTION

        # Handle collision
        handle_collision(square, container)

        # Draw everything
        screen.fill(WHITE)
        container.draw(screen)
        square.draw(screen, container.angle)
        pygame.display.flip()

        clock.tick(60)

    pygame.quit()

if __name__ == "__main__":
    main()

Here's the output of the program:

ChatGPT 4.5 Response

import pygame
import pymunk
import pymunk.pygame_util
import math

# Initialization
pygame.init()
WIDTH, HEIGHT = 800, 800
window = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Ball in a Spinning Circle - Physics Simulation")

fps = 60
clock = pygame.time.Clock()

# Physics Setup
space = pymunk.Space()
space.gravity = (0, 980)  # downward gravity (pixels per second squared)

draw_options = pymunk.pygame_util.DrawOptions(window)

# Container Parameters
circle_radius = 300
circle_thickness = 10

# Create the spinning container as a kinematic body (manually controlled rotation)
container_body = pymunk.Body(body_type=pymunk.Body.KINEMATIC)
container_body.position = WIDTH / 2, HEIGHT / 2

# Important fix: Add container_body to the physics space
space.add(container_body)

# Define the circle shape by multiple small segments (better collision detection)
segments = 60
angles_step = 2 * math.pi / segments

for i in range(segments):
    angle_start = angles_step * i
    angle_end = angles_step * (i + 1)
    x1 = circle_radius * math.cos(angle_start)
    y1 = circle_radius * math.sin(angle_start)
    x2 = circle_radius * math.cos(angle_end)
    y2 = circle_radius * math.sin(angle_end)
    segment_shape = pymunk.Segment(container_body, (x1, y1), (x2, y2), circle_thickness)
    segment_shape.elasticity = 0.8
    segment_shape.friction = 0.8
    space.add(segment_shape)

# Create the ball inside the container
ball_radius = 20
ball_mass = 2
ball_inertia = pymunk.moment_for_circle(ball_mass, 0, ball_radius)
ball_body = pymunk.Body(ball_mass, ball_inertia)
ball_body.position = WIDTH / 2, HEIGHT / 2
ball_shape = pymunk.Circle(ball_body, ball_radius)
ball_shape.elasticity = 0.8
ball_shape.friction = 0.6
space.add(ball_body, ball_shape)

# Simulation parameters
rotation_speed = 0.5  # radians per second

# Main loop
running = True
angle = 0

while running:
    dt = clock.tick(fps) / 1000.0  # time step in seconds

    # Check for quit
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            running = False

    # Spin the container continuously
    angle += rotation_speed * dt
    container_body.angle = angle

    # Update physics simulation
    space.step(dt)

    # Drawing
    window.fill((255, 255, 255))
    space.debug_draw(draw_options)  # easy built-in visual debugging

    pygame.display.flip()

pygame.quit()

Here's the output of the program:

Summary

Grok 3 gave it a shot. The square bounced, spun, and reacted, even if it wasn’t perfect. ChatGPT-4.5 just dropped the square in the center like physics didn’t even matter, which was honestly disappointing.

2. Dynamic 3D Shifting Panels Animation in JavaScript

Let’s try a simple space animation and see if the AI models can handle it.

This isn’t a complex challenge, just drifting planets, soft lighting, and twinkling stars. I expected both models to get it right without issues. Grok 3 seems promising, but after ChatGPT-4.5’s last performance, I’m not too confident. Let’s see what happens.

Prompt:

Create a 3D animation of planets drifting in space using Three.js. The scene should feature a few planets of different sizes and colors, slowly moving in a natural, floating motion. Each planet should rotate gently, with soft lighting that highlights their surfaces. A glowing central star should cast light, creating subtle shadows and reflections. The background must include twinkling stars that fade in and out at different speeds, adding depth. The camera should pan slowly, providing a dynamic but smooth view of the planets without rapid movements.

Grok 3 Response




    </span>Planet Animation<span class="nt">

Here's the output of the program:

ChatGPT 4.5 Response:


 lang="en">

     charset="UTF-8">
    </span>3D Planet Scene in Three.js<span class="nt">

Here's the output of the program:

Summary

Grok 3 did exactly what I expected. The planets moved smoothly, the lighting worked well, and the animation felt complete. Everything came together without any issues.

ChatGPT-4.5 was a frustrating experience. No matter how many times I tried, it just gave me a blank screen. No movement. No animation. Nothing.

3. Modern Photo Editor App in Python

This time, I want to see if the AI models can build a fully functional photo editor with a clean and intuitive UI for easy image editing.

Prompt:

Create a Python-based photo editor using Tkinter and OpenCV that allows users to upload, edit, and save images. It should support free cropping, zooming, rotation, resizing, brightness/contrast adjustments, and filters with real-time previews. The UI must be modern and user-friendly, ensuring smooth editing and high-quality image exports.

Grok 3 Response

import tkinter as tk
from tkinter import filedialog, messagebox, ttk
import cv2
import numpy as np
from PIL import Image, ImageTk
import os 

class PhotoEditor:
    def __init__(self, root):
        self.root = root
        self.root.title("Advanced Photo Editor")
        self.root.geometry("1200x800")
        self.root.configure(bg="#2d2d2d")  # Dark theme

        # Initialize variables
        self.original_image = None
        self.current_image = None
        self.preview_image = None
        self.crop_start = None
        self.crop_rect = None
        self.is_cropping = False
        self.zoom_level = 1.0
        self.canvas_width = 900
        self.canvas_height = 700

        # Create GUI
        self.create_interface()

    def create_interface(self):
        # Main frame
        main_frame = ttk.Frame(self.root, padding="10")
        main_frame.pack(fill=tk.BOTH, expand=True)

        # Left control panel
        self.control_panel = ttk.Frame(main_frame, width=250, style="Dark.TFrame")
        self.control_panel.pack(side=tk.LEFT, fill=tk.Y, padx=5)

        # Upload button
        ttk.Button(self.control_panel, text="


                                            
                            
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