GPT-5.1 Codex-Max vs Gemini 3 Pro: hands-on coding comparison

Comprehensive Review: Comparing GPT-5.1 Codex-Max and Gemini 3 Pro in Coding Tasks

In the rapidly evolving landscape of AI-powered coding assistants, understanding the strengths and limitations of emerging models is essential for developers seeking to optimize their workflows. Recently, I conducted an in-depth, hands-on comparison between two prominent AI coding models—GPT-5.1 Codex-Max and Gemini 3 Pro—focusing on their performance across a variety of programming challenges. Below, I share my findings and insights from this evaluation.

Methodology

To ensure a fair and thorough assessment, I subjected both models to the same set of three coding tasks:

1. Developing a simple Ping Pong game
2. Implementing logic for a Hexagon-based game with robust state management
3. Reproducing a complete Next.js user interface based solely on an image

This approach allowed me to evaluate each model’s capabilities in code generation, multimodal understanding, reasoning, and debugging.

Performance Highlights of Gemini 3 Pro

Multimodal Strengths:
Gemini 3 Pro demonstrated exceptional multimodal coding prowess. When provided with a UI screenshot, it generated a Next.js layout that closely resembled the original design, accurately capturing spacing, structural components, and styling nuances. Its ability to interpret visual input and convert it into functional code was notably impressive.

Refined Logic Handling:
The Hexagon game logic created by Gemini was more precise and required fewer corrective edits. Its reasoning chain appeared more stable, handling edge cases effectively and producing cleaner, production-ready code out of the box.

Performance of GPT-5.1 Codex-Max

Speed and Reasoning Clarity:
Codex-Max exhibited remarkable speed and a clear, step-by-step approach to problem-solving. It maintained consistency over extended prompts, provided transparent explanations of its methods, and handled debugging tasks with sustained contextual awareness.

Superior Output on Specific Tasks:
Interestingly, GPT-5.1 outshined Gemini in certain areas. For the Ping Pong game, it generated a more polished, visually appealing implementation that yielded smoother gameplay. Its refactoring suggestions for the Hexagon logic were logical and the initial attempt was almost accurate, requiring minimal adjustments.

Limitations in Multimodal Tasks:
While capable of recreating UIs from images, GPT-5.1’s output lacked the final polish and often required additional prompt iterations to achieve visually precise results.

Overall Assessment

Both models undoubtedly serve as powerful coding assistants, each with distinct advantages