GPT-5 vs. Claude Opus 4.1: The Ultimate Agentic Coding Showdown

In the current landscape, agentic coding is taking center stage. This article presents a head-to-head competition between GPT-5 and Claude Opus 4.1 to determine which model excels at agentic coding tasks.

Initial Task: A Simple Website Update

To begin, I tasked GPT-5 with a real-world scenario: making several updates to a website. The first step was to get the local development server running.

GPT-5 was instructed simply to "start the website on a server." The model correctly identified the necessary command and initiated a local static server. A notable improvement over previous models is its communication; it clearly stated its action: "starting a local static server from your project directory," before executing the command.

With the website running, the next task was to update some content. The goal was to change the "best max model" from its previous value to "GPT-5 thinking" and update the reasoning behind this choice. The prompt specified that the model should reference its superior performance on the Frontier Math benchmark.

The model's approach was impressive. It first announced its plan: "I'll scan the site files to find where the best max model and any video link are defined then make the required content edits." This level of transparency, explaining its intended actions before execution, is a significant step forward for agentic models, preventing the kind of unpredictable behavior that could be catastrophic in some situations.

After scanning the files, it confirmed the specific change: "I'm going to update the math model card to GPT-5 thinking." Once the edits were made, it automatically opened the relevant "model recommendations" page in the browser for verification. The changes were perfect. The model was updated, and the reasoning text was exactly as requested.

Challenge 1: Fixing a Visual Bug

Next, a more complex, visual bug was introduced. An ad block was obstructing and deforming the layout on the homepage, making it look ugly. A screenshot of the issue was provided to GPT-5.

Leveraging its multimodal capabilities, GPT-5 analyzed the image to understand the layout problem. After processing, it applied a fix. A quick refresh of the production website confirmed the result: the ad was no longer obstructing the view, and the layout was clean. This small, real-life use case demonstrated a high level of practical utility.

The Main Event: Building a Production-Ready Website

With the initial tests complete, it was time for a more substantial challenge: building a multi-page, production-ready website for an educational consultancy firm from scratch. Both models were given access to a codebase containing all necessary assets, including JSON files for course details, FAQs, and testimonials, as well as a folder of images.

Round 1: Claude Opus 4.1's Attempt

Opus 4.1 quickly analyzed the file structure and generated a comprehensive to-do list. It planned to build a homepage, courses pages (both a general page and individual course pages), an about us page, a contact page, and more. It even included implementing animations and interactions.

The final website, branded "Edu Consult Pro," had a polished UI with sections for "10,000 students placed" and "500+ partner colleges." However, its core functionality was flawed. The main "Explore Courses" page was broken; clicking on categories like "Medical" or the "View All" button did nothing. The courses were only visible further down the homepage, and even then, clicking them didn't lead anywhere.

Other pages were more successful. The testimonials page worked well, with an option to filter reviews by star rating. The FAQ page also functioned, featuring a search bar that correctly filtered questions, though its CSS was not properly implemented. The contact page was complete with a form and an embedded map. Overall, it was a visually appealing but partially non-functional website.

Round 2: GPT-5's Turn

GPT-5 also began by creating a to-do list, using more technical language like "architect the site map" and "implement global assets for the header and footer." Its plan was similar to Opus's, focusing on creating the essential pages.

The resulting website had a different aesthetic. The headline, "From stream to seat, your trusted guide to the right college and course," felt more natural than the AI-generated feel of Opus's copy. Most importantly, the website worked. Clicking on "Medical Courses" correctly displayed a list of medical courses, each with its duration and category. This held true for all other course categories. The site also included a functional search bar on the courses page, allowing users to type "MBBS" or "Nursing" and see relevant results instantly.

The site wasn't without flaws. The font on the FAQ page was far too small. The contact form was simpler than Opus's, lacking the option to select a stream or course of interest.

Verdict: Functionality Over Flair

While Opus's design had some nice touches, its failure to deliver a working courses page was a critical flaw. GPT-5, despite some minor aesthetic issues, produced a fully functional website. Given the vast price difference—the Opus generation cost nearly $5.00, while GPT-5's cost just $0.57—GPT-5 is the clear winner. The affordability allows for multiple iterations to fix minor issues, a luxury not possible with Opus's pricing.

The Creative Challenge: Building the Best Chess Game

For the next test, both models were given a completely empty codebase and a single directive: build the absolute best chess game possible, using any framework or language.

Opus planned to use React and TypeScript, with SVG graphics for the board and a Minimax algorithm for the AI opponent. GPT-5 opted for a single-file web app using chess.js, chessboard.js, and Stockfish for the engine. Its to-do list was more detailed, including play modes (human vs. engine, human vs. human), an evaluation bar, and an optional analysis mode.

GPT-5 finished first but produced a non-working result. The UI elements for selecting game modes were present, but the chessboard itself never appeared.

Opus struggled initially, producing a blank page. However, it was able to view the browser output, diagnose the problem, and iteratively debug its own code. Slowly, a chessboard appeared. It then proceeded to test the game by selecting a pawn and making a move. The final product was a basic but working chess game. Although it lacked visual feedback for checkmate, it was a functional implementation.

In this round, Opus's ability to see the screen and self-correct gave it a decisive edge, making it the winner.

The Debugging Gauntlet

A key skill for any developer is debugging. To test this, the models were presented with a benchmark testing system containing several intentionally implanted bugs.

The bugs were: 1. In open_router_api.py: A function transposed any square matrix it received, causing correct grid-based test answers to be marked as incorrect. 2. In server.py: After seven successful tests, the logic inverted, marking correct answers as wrong and wrong answers as correct. 3. In common.js: A subtle bug that changed the display colors.

GPT-5 identified the first two critical bugs in a single pass. It correctly pointed out the stateful inversion in server.py and the matrix transposition in open_router_api.py.

Opus, on the other hand, only found the bug in server.py initially. It required a follow-up prompt—"please check if there is any other bug"—before it investigated open_router_api.py and found the second issue. GPT-5's superior performance was likely due to its tendency to scan a wider range of files before drawing conclusions.

Final Round: Complex Application Repair

In the final round, the models were tasked with fixing a complex chess benchmarking application riddled with multiple real-world errors. The primary issues were a WebSocket connection failure that prevented games from starting, an illegal move counter that incremented on legal moves, and a bug that showed the wrong player's turn to the backend AI.

Both models performed exceptionally well. They were each able to identify and fix all the critical bugs. After their edits, the application ran smoothly, the game started correctly, the illegal move counter functioned as intended, and the backend models received the correct information. It was a tie.

Final Verdict: The Price of Power

The final tally gives GPT-5 a slight edge, winning two rounds, tying one, and losing one, compared to Opus's one win, one tie, and two losses.

Both models are incredibly capable in agentic applications. The competition was neck-and-neck in several areas. However, the deciding factor is price. GPT-5 consistently delivered comparable or better results at a fraction of the cost—sometimes up to eight times cheaper. This makes it a much more practical tool for real-world development, where multiple iterations and back-and-forth adjustments are the norm.

While Opus may have an edge in pure front-end design aesthetics, GPT-5's powerful agentic capabilities, combined with its affordability, make it the overall winner in this showdown.