OpenAI is reportedly accelerating work on a new large language model codenamed “Garlic”, an initiative sparked by growing competition from AI rivals like Gemini 3 and Opus 4.5. According to industry insiders, the company’s CEO Sam Altman, has declared an internal “code red,” pushing teams to strengthen ChatGPT’s reasoning and coding abilities.

What is Garlic? Why does it matter?

Garlic is said to be designed as a major leap, not just a routine model update. Early internal tests suggest it performs impressively on reasoning and coding benchmarks, even outpacing newer models from competitors.

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This project builds on lessons from an earlier internal experiment, nicknamed “Shallotpeat.” Garlic reportedly refines that work with improved pretraining strategies and a more efficient architecture. One of its most notable advancements is its ability to deliver large-model performance while using fewer computational resources — a shift OpenAI researchers describe as achieving “big model quality without big model cost.”

That efficiency could open the door to broader deployment, making high-performance AI more accessible for enterprises, developers, and specialised use cases.

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What does this mean for OpenAI — and for users?

1. A strategic comeback effort: Garlic appears central to OpenAI’s push to regain momentum in the increasingly competitive AI landscape.

2. More capability at lower cost: If the model lives up to expectations, users could see stronger reasoning, cleaner coding output, and faster performance — without the heavy compute demands typical of next-gen models.

3. Faster release cycles: Reports suggest Garlic may arrive as early as 2026, potentially as GPT-5.2 or GPT-5.5, signaling a quicker cadence for model updates.

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What we still don’t know

OpenAI hasn’t confirmed Garlic’s existence or details, so everything remains unofficial. There’s no clarity yet on trade-offs, real-world performance, pricing, or public availability. For now, Garlic stands as a promising glimpse into OpenAI’s next chapter — one that could redefine efficiency and capability in AI systems.