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GPT-5.3-Codex combines coding excellence with advanced reasoning, outpacing its predecessor by 25% in execution speed and introducing agentic capabilities for seamless task management and interactive control.
OpenAI has just unveiled GPT-5.3-Codex, a groundbreaking model that significantly expands Codex's capabilities across various professional tasks on a computer. This new iteration not only builds upon the coding prowess of GPT-5.2-Codex but also integrates advanced reasoning and knowledge from GPT-5.2, making it 25% faster in execution. The result is an agentic model that can handle long-running tasks involving research, tool use, and complex execution, all while maintaining context and allowing for interactive steering.
GPT-5.3-Codex sets new industry standards on several key benchmarks:
One of the most impressive demonstrations of GPT-5.3-Codex's capabilities is in web development. The model can autonomously build highly functional and complex games and applications over several days. To showcase this, OpenAI tasked GPT-5.3-Codex with creating two games:

Using preselected prompts like "fix the bug" or "improve the game," GPT-5.3-Codex iterated on these projects over millions of tokens, refining the games to a high standard. You can play and explore these games to see the model's capabilities in action.
For developers and professionals, GPT-5.3-Codex represents a significant leap forward in AI-assisted work. It can handle tasks that were previously too complex or time-consuming for automated systems, such as:
GPT-5.3-Codex is a testament to OpenAI's commitment to pushing the boundaries of what AI can do in professional settings. By combining advanced coding skills with robust reasoning capabilities, this model sets a new standard for agentic assistants in software development and beyond. Whether you're a developer looking to accelerate your projects or a professional seeking an intelligent assistant, GPT-5.3-Codex is a powerful tool worth exploring.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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6 February 2026
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