OpenAI to Simplify Model Names with the Launch of GPT-5

OpenAI is set to gradually retire its current naming convention for foundational models, moving away from the “GPT” numerical identifiers to a cohesive brand with the upcoming GPT-5 launch.

This transition, revealed during a recent AMA session on Reddit with key members from the Codex and research teams, signifies OpenAI’s aim to streamline product interactions and minimize confusion regarding different model capabilities and how they are utilized.

Currently, Codex, the company’s AI coding assistant, operates through two main deployment options: the ChatGPT interface and the Codex command line interface. Models such as codex-1 and codex-mini serve as the foundation for these tools.

As stated by Jerry Tworek, OpenAI’s VP of Research, the goal of GPT-5 is to merge these variations, providing users with direct access to functionalities without the need for switching between different model versions or interfaces. Tworek mentioned,

“GPT-5 is our next foundational model that is designed to enhance everything our current models can do while reducing the need for switching models.”

New tools from OpenAI for coding, memory, and operations

This announcement aligns with a larger integration of OpenAI’s tools, including Codex, Operator, memory systems, and advanced research capabilities into an all-encompassing agentic framework. This new architecture will enable models to write code, execute it, and validate it within cloud-based environments.

Researchers at OpenAI have highlighted that using numeric suffixes to differentiate models does not accurately capture how users engage with functionalities, particularly as ChatGPT agents perform complex coding tasks simultaneously.

Elimination of numeric suffixes occurs against the backdrop of OpenAI’s growing interest in agent behavior, prioritizing operational effectiveness over static model identification. Instead of labeling releases with versions like GPT-4 or GPT-4o-mini, the system will increasingly focus on functionality, such as Codex for developer engagements or Operator for local system tasks.

Andrey Mishchenko pointed out that this change also proves practical: codex-1 has been tailored for the ChatGPT environment, making it less suitable for general API needs at this stage, although efforts are underway to standardize agents for broader API use.

While GPT-4o was released with a few limited versions, internal assessments indicate that the next iteration will emphasize versatility and sustainability rather than just small numerical enhancements. Various researchers have acknowledged that Codex’s performance in real-world applications has met or surpassed expectations on benchmarks like SWE-bench, even though updates such as codex-1-pro are still pending release.

The convergence of underlying models aims to tackle the fragmentation seen in interfaces aimed at developers, which has led to uncertainty regarding the most suitable version for different applications.

This simplification occurs as OpenAI broadens its approach to integration across various development settings. Anticipated future support includes Git platforms beyond GitHub Cloud, as well as compatibility with project management and communication tools.

Hanson Wang from the Codex team confirmed that deployment through CI pipelines and local systems is already possible via the CLI. Codex agents now function in isolated containers with defined operational periods, allowing them to execute tasks for up to an hour per session, as noted by Joshua Ma.

Expansion of OpenAI models

OpenAI’s language models have traditionally been categorized by size or their stage of development, such as GPT-3, GPT-3.5, GPT-4, and GPT-4o. Nevertheless, GPT-4.1 and GPT-4.5 are, in some respects, ahead of but also somewhat behind the newest model, GPT-4o, causing some confusion.

As the core models increasingly handle tasks directly—such as reading code repositories, conducting tests, and formatting commits—the significance of versioning has decreased in favor of access based on capabilities. This evolution aligns with internal usage trends where developers prioritize task delegation over specific model version choices.

In response to a question about the potential merging of Codex and Operator to manage tasks such as frontend validation and backend operations, Tworek responded,

“We already have a product that can perform actions on your computer—it’s called Operator… eventually, we want these tools to feel unified.”

The Codex project was described as a response to the internal challenge of underusing OpenAI’s models in day-to-day development, a sentiment shared by multiple team members during the discussion.

This decision to phase out model versioning reflects a broader move towards a modular framework in OpenAI’s deployment strategy. Teams and enterprise users will maintain stringent data controls, with Codex content excluded from model training. Pro and Plus users will have distinct opt-in options. As Codex agents expand beyond the ChatGPT interface, OpenAI is developing new usage tiers and a more adaptable pricing structure that may include usage-based plans beyond standard API integrations.

OpenAI has not provided a specific timeline for the launch of GPT-5 or the complete phase-out of existing model names, though updates to internal messaging and interface designs are expected alongside the release. For the time being, users engaging with Codex through ChatGPT or CLI can anticipate improvements in performance as model functionalities evolve under the new unified identity of GPT-5.