July 30 (Reuters) - OpenAI slashed prices of its low- and

mid-tier AI models on Thursday, a move that may intensify

competition in the industry as U.S. companies battle cheaper

Chinese rivals for customers increasingly wary of the

technology's ballooning costs.

The ChatGPT maker lowered the cost of its smaller GPT-5.6

Luna model by 80% and its mid-tier Terra by 20%, while leaving

the price of its biggest and flagship Sol model unchanged.

The cuts show that rising cost scrutiny by businesses facing

hefty AI bills is forcing American labs to rethink pricing. Many

tech CEOs have also said in recent months that cheaper AI

options are key to the technology's widespread adoption.

OpenAI's new pricing also turns up the heat on Anthropic,

whose Claude models dominate enterprise and developer use but

sit at the costlier end of the market. Both companies have been

under pressure from open-source Chinese rivals such as Z.ai's

GLM-5.2 that nearly match their performance at a lower cost.

Analysts have said that cutting prices could boost usage of

OpenAI's and Anthropic's technology, but strain their finances

ahead of highly anticipated initial public offerings.

While Thursday's cuts affect only OpenAI's smaller and

mid-tier models, the company said it would still benefit

businesses broadly as those models can now do work that recently

required a top-tier system at far lower cost.

The new pricing means businesses using OpenAI's technology

will have to pay less for every million "tokens", or the units

used to measure AI usage, they run through the models.

Sending text to Luna drops to 20 cents per million tokens

from $1 and Terra's to $2 from $2.50, while generating responses

falls to $1.20 and $12 from $6 and $15, respectively.

Anthropic's mid-tier Claude Sonnet 4.6 model, meanwhile,

costs $3 per million input tokens and $15 per million output

tokens, above the rates for Terra.

OpenAI said the lower prices were partly enabled by

efficiency gains from GPT-5.6, including the model's ability to

improve code and optimize performance during internal

development.

Overall, prices of tokens have been falling in the past

year, but the cost of completing a task is rising as ​AI firms

shift from flat subscriptions to usage-based pricing.

That is leaving companies with unpredictable and often

higher bills as usage per task becomes harder to estimate.