Last week, OpenAI released GPT‑5.4 in ChatGPT (as GPT‑5.4 Thinking), their API, and their proprietary coding IDE: Codex. It’s reportedly their most capable and efficient frontier model for professional work, bringing together the best of OpenAI’s recent advances in reasoning, coding, and agentic workflows into a single frontier model. GPT-5.4 incorporates the industry-leading coding capabilities of GPT‑5.3‑Codex while “improving how the model works across tools, software environments, and professional tasks involving spreadsheets, presentations, and documents” (Source).
In this article, we will look at how to access and make the best use of GPT-5.4 with the help of the DigitalOcean Platform, ChatGPT, and Codex. Readers can expect to learn just how potent this model is, and how to best implement the model into their AI workflows. Follow along for more information on the model itself, and a full demo on how to use it with DigitalOcean AI Platform serverless inference to make a toy game in Python.
GPT-5.4 is another incremental step forward for Large Language Modeling. As we can see from the image above, GPT-5.4 outperforms its predecessors across nearly all types of tests. We should particularly call out the sharp improvements shown in computer use and tool use capabilities. In addition to the plethora of improvements it has made across all benchmarks over its predecessors, there have also been numerous more subtle improvements we would like to call attention to.
In Codex, AI Platform serverless inference, and the API, GPT‑5.4 is the first general-purpose model they have released with native, state-of-the-art computer-use capabilities. In practice, this means that GPT-5.4 enables agents to operate computers and carry out complex workflows across applications. It supports up to 1 million tokens of context, allowing agents to plan, execute, and verify tasks across long horizons. With this huge amount of detail & its own in-built capabilities, the model can issue instructions to applications like Playwright to direct computer use in experimentation.
GPT-5.4 also enhances how models operate within large ecosystems of tools and integrations by introducing tool search, enabling agents to locate and use the right tools more efficiently without sacrificing capability. In addition, GPT-5.4 is OpenAI’s most token-efficient reasoning model to date, requiring far fewer tokens than GPT-5.2 to solve complex problems: resulting in lower usage costs and faster performance.
In ChatGPT, GPT-5.4 Thinking can now present a preview of its reasoning plan before completing a response, allowing you to guide or adjust its direction while it works and reach an outcome that better matches your needs without extra back-and-forth. It also strengthens deep web research—especially for highly specific questions—while maintaining context more effectively during longer reasoning tasks. Together, these enhancements deliver faster, more relevant, and higher-quality results.
Together with advances in general reasoning, coding, and professional knowledge work, GPT‑5.4 enables more reliable agents, faster developer workflows, and higher-quality outputs across ChatGPT, the API, and Codex.
Getting started with GPT-5.4 is simple, with 4 major ways to quickly access the model on your machine or the web: ChatGPT, Codex, the OpenAI API, and with AI Platform serverless inference. These are all unique tools with their own advantages and disadvantages:
We can see an example of using the AI Platform serverless inference methodology above to fix a small Python code snippet. This ran incredibly quickly to effectively generate the solution. Using the Serverless Inference methodology wherever possible, as it gives the same versatility of the API method with integrations from DigitalOcean to enhance our experience.
For this tutorial, we are going to use Codex, which is optimized for aiding developers, with GPT-5.4 to help us create a completely new, toy video game that we can run on our local machine.
To get started, download and launch Codex. Then, log in following the instructions. Finally, open the project/directory you want to work in with Codex. Once those steps are completed, we are ready to get started with prompting the model to create our project.
To create a novel game with GPT-5.4, we first need an exciting concept to guide our plan. One game we have always loved playing is Badminton & there is really no mainstream video game for the sport, so we are going to make our game around badminton! Let’s craft a prompt around creating a game for Badminton, set Codex to plan mode, and enter in our instructions.
An example prompt could be something like: “In this repo, we are going to create a new playable game for Mac OS computers. The game is going to be badminton, and this game is going to be like pong but with 3-dimensions of movement. Help me make a plan for the game.”
As we can see from the image above, this will generate a long plan for GPT-5.4 to follow in later steps. This organizational instruction stage is key to getting the most out of our outputs from the model. We can also use this intermittent stage to make any adjustments we need to the plan. For example, if we would like the game to be viewed from the front or the side. We can manually adjust the instructions for the next step to clarify this important view.
Once that is generated, we can begin generating the application itself. This took 18 minutes for us to generate a fully functional, Godot powered application that can be run on Mac computers. Here is a video of us struggling to compete against the AI controlling the opponent.
As we can see, GPT-5.4 completed a fully functional game experience for us that is controlled by keyboard controls. All we needed to do was set up the initial instruction, and wait for the model to run!
The game is completely open-source, and can be downloaded at the main Badminton repo on Github. You will need at least Godot 4.2 to play the game, and it was designed for Mac Silicon computers.
GPT-5.4 represents a meaningful leap forward in making advanced AI genuinely practical for professional and developer workflows. By combining high-powered reasoning, powerful coding capabilities, native computer-use skills, and improved tool integration, it moves beyond simple prompt-response interactions and into the realm of reliable autonomous assistance. Whether accessed through ChatGPT for productivity, Codex for development, the API for custom pipelines, or DigitalOcean’s AI Platform serverless inference for scalable deployment, GPT-5.4 provides flexible paths for integrating frontier intelligence into real-world projects. As models continue to evolve toward longer context, better planning, and tighter tool integration, GPT-5.4 stands as one of the clearest signals yet that highly capable, agent-driven AI systems are becoming practical tools for everyday builders.
This work is licensed under a Creative Commons Attribution-NonCommercial- ShareAlike 4.0 International License.
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。