OpenAI Cuts Luna Model Prices by 80% to Boost Enterprise Adoption

OpenAI is expanding its enterprise footprint by reducing prices on its budget-tier Luna model by 80%. This strategic shift has already resulted in a tenfold increase in usage. The move signals a transition toward outcome-based pricing models intended to compete with open-source alternatives and capture a larger share of corporate AI spending.

"If you’re deploying Luna and compare that to (Z.ai’s) GLM 5.3, for example, on a cloud layer, we are cheaper," said Sarah Friar, OpenAI CFO, in a statement regarding the company's pricing strategy.

Scaling Enterprise Revenue

The pricing adjustments are part of an effort to balance the company's revenue streams. According to a recent report, OpenAI saw its enterprise revenue grow 32% between June and July. The company is on track to reach parity between its enterprise and consumer revenue segments by the end of the calendar year.

Shifting to Outcome-Based Pricing

OpenAI is testing outcome-based pricing models for its corporate clients. This approach departs from industry-standard usage-based fees, which charge companies based on the number of tokens processed or computational cycles consumed.

Outcome-based pricing ties costs to the business value or results generated by the AI, such as the number of customer tickets resolved or lines of code successfully refactored. By adopting this model, OpenAI aims to provide predictable budgeting for enterprise departments wary of the fluctuating costs associated with scaling large language models in production environments.

Competitive Positioning Against Open-Source

This pricing strategy targets the rise of open-source models like Z.ai’s GLM 5.3. While open-source models allow enterprises to host software on their own infrastructure, the total cost of ownership involves expenses related to cloud compute, maintenance, and security patching. OpenAI’s strategy positions its managed services as a cost-effective alternative that removes the operational overhead of managing local model deployments.

As the company moves toward parity in revenue segments, the outcome-based model will serve as the mechanism for securing long-term enterprise contracts, shielding corporate customers from the technical complexity of self-hosted open-source alternatives.