OpenAI on Thursday announced significant price cuts for two of its newest artificial intelligence models, GPT-5.6 Terra and GPT-5.6 Luna, just weeks after they were released to the public.
The move comes as the company faces growing pressure to appeal to cost-conscious customers, particularly businesses that have been reluctant to adopt high-priced AI models without a clear return on investment.
OpenAI is also seeking to strengthen its position against intensifying competition from Chinese AI startups as well as technology giants Google and Microsoft, which have been promoting more affordable AI offerings.
The GPT-5.6 family consists of three models: Sol, the flagship and most capable version; Terra, the mid-range model; and Luna, the fastest model designed for speed and efficiency.
OpenAI introduced three models in its GPT-5.6 family: Sol, its most powerful model; Terra, a mid-tier option; and Luna, its fastest model designed for speed and efficiency.
On Thursday, the company announced a 20 per cent price reduction for Terra, lowering its cost to $2 per million input tokens and $12 per million output tokens.
It also slashed Luna’s pricing by 80 per cent, reducing it to 20 cents per million input tokens and $1.20 per million output tokens. Pricing for Sol, the flagship model, remains unchanged.
“Our strategy remains focused on advancing both capability and efficiency so each generation of intelligence can accomplish more work at a lower cost,” OpenAI said in a release.
OpenAI sparked the generative AI boom with the launch of ChatGPT in 2022, prompting companies across the United States to rapidly adopt the technology and encourage employees to integrate AI into their daily work.
That wave gave rise to what became known as “tokenmaxxing”—a trend in which organisations urged workers to maximise their use of AI tools with little concern for computing costs, prioritising adoption and experimentation over efficiency.
OpenAI sparked the generative AI boom with the launch of ChatGPT in 2022, prompting companies across the United States to rapidly adopt the technology and encourage employees to integrate AI into their daily work.
That wave gave rise to what became known as “tokenmaxxing”—a trend in which organisations urged workers to maximise their use of AI tools with little concern for computing costs, prioritising adoption and experimentation over efficiency.
