OpenAI has lowered API prices for GPT-5.6 Luna and Terra while unveiling a faster processing option for GPT-5.6 Sol. The updates aim to make the company’s AI models more cost-efficient for developers and business users.
GPT-5.6 Luna price falls by 80%
OpenAI has cut the cost of GPT-5.6 Luna by 80%. The model now costs $0.20 per million input tokens and $1.20 per million output tokens.
Previously, Luna cost $1 per million input tokens and $6 per million output tokens. The new GPT-5.6 pricing makes the model a more affordable choice for large-scale tasks.
Terra also receives a lower API price
GPT-5.6 Terra has also become cheaper to use. Input tokens now cost $2 per million, down from $2.50, while output tokens have dropped from $15 to $12 per million.
OpenAI said the reductions follow efficiency improvements across its newest models. These changes could lower costs for teams that rely on AI for regular coding, research and automation work.
Codex and ChatGPT Work allowances change
The updated GPT-5.6 pricing also affects usage allowances in Codex and ChatGPT Work. Tasks that use Luna or Terra will deduct less from a customer’s available quota.
As a result, users may be able to complete more tasks without increasing their plan or usage limits.
OpenAI is also moving Auto-review in the ChatGPT app and Codex CLI from GPT-5.4 to GPT-5.6 Luna. The company expects the change to cut Auto-review costs by roughly ten times.
GPT-5.6 Sol Fast mode offers quicker responses
OpenAI has introduced a Fast mode for GPT-5.6 Sol API customers. It can process requests up to 2.5 times faster than standard mode without reducing the model’s intelligence.
The option costs twice the standard Sol API rate and targets time-sensitive coding, research and agentic workloads. For less urgent tasks, OpenAI says standard processing should remain sufficient.
The company said recent Sol improvements helped deliver the efficiency gains behind the Luna and Terra reductions. OpenAI’s own testing also places Luna at the top of its intelligence index among the compared models, despite its much lower cost per task.
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