OpenAI Codex differs from GPT-3 in its specialized training and focus on code generation and software engineering tasks. While GPT-3 is a general-purpose language model trained on diverse text from the internet, Codex was specifically trained on massive amounts of code from public repositories. The original Codex model was built on the GPT-3 architecture but fine-tuned with 159 gigabytes of Python code from over 54 million GitHub repositories, along with code in many other programming languages. This specialized training allows Codex to understand programming syntax, patterns, and best practices in ways that general GPT-3 cannot match.
The current Codex (2025) is built on the codex-1 model, which uses OpenAI’s o3 reasoning architecture rather than GPT-3. This represents a fundamental advancement in both the underlying technology and capabilities. While GPT-3 can generate some code when prompted, it lacks the deep understanding of programming concepts, debugging abilities, and software engineering workflows that Codex possesses. Codex understands not just how to write code, but also how to structure projects, run tests, handle dependencies, and follow development best practices. It can interpret complex technical requirements and translate them into working software solutions.
The practical differences become clear in how each model handles coding tasks. GPT-3 might generate basic code snippets when asked, but Codex can understand entire codebases, maintain context across multiple files, and perform sophisticated software engineering tasks. Codex can also execute code, run tests, and iterate on solutions until they work correctly, whereas GPT-3 simply generates text without the ability to verify or execute the code it produces. This makes Codex a true development tool rather than just a text generator that can occasionally write code.