OpenAI is secretly fast-tracking 'Garlic' to fix ChatGPT's biggest flaws: What we know
Briefly

OpenAI is secretly fast-tracking 'Garlic' to fix ChatGPT's biggest flaws: What we know
"Following Google's release of Gemini 3, which quickly rose to the top of the LMArena AI leaderboard, OpenAI CEO Sam Altman informed employees that he was declaring a "code red." The aim was to further improve ChatGPT to better compete, according to a report by The Information. Now, a follow-up report from the publication reveals that the company is developing a new model in response, codenamed Garlic."
"(Disclosure: Ziff Davis, ZDNET's parent company, filed an April 2025 lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.) Also: Is DeepSeek's new model the latest blow to proprietary AI? OpenAI's Chief Research Officer Mark Chen informed colleagues that Garlic has performed well in company evaluations compared to Gemini 3 and Anthropic's Opus 4.5 in tasks involving coding and reason, according to the report."
"Chen also added that when developing Garlic, OpenAI addressed issues with pretraining, the initial phase of training in which the model begins learning from a massive dataset. The company focused the model on broader connections before training it for more specific tasks. These changes in pretraining enable OpenAI to infuse a smaller model with the same amount of knowledge previously reserved for larger models, according to Chen's remarks cited in the report."
Google released Gemini 3, which quickly rose to the top of the LMArena AI leaderboard and set a new standard in reasoning. OpenAI declared a "code red" to further improve ChatGPT and is developing a new model codenamed Garlic. Garlic reportedly performed well in internal evaluations against Gemini 3 and Anthropic's Opus 4.5 on coding and reasoning tasks. OpenAI adjusted pretraining to emphasize broader connections before task-specific training, enabling a smaller Garlic model to absorb knowledge typically found in larger models. Smaller models reduce deployment cost and complexity for developers.
Read at ZDNET
Unable to calculate read time
[
|
]