# OpenAI releases GPT-5.6 as three-model lineup with aggressive pricing

> OpenAI's GPT-5.6 arrived not as one model but as three, and the split says a lot about where the industry is heading. Sol is the heavyweight aimed at the hardest agentic, coding and science problems at $5 per million input tokens and $30 output. Terra sits at half that cost, and Luna drops to $1 in and $6 out for fast, high-volume work where speed and price matter more than raw reasoning. The logic is that most real workloads do not need the smartest possible model on every call. A support bot answering routine questions and a research agent planning a multi-step task have very different needs, and paying Sol rates for Luna-grade work is simply waste. By packaging tiers explicitly, OpenAI is nudging developers to route each request to the cheapest model that can handle it. The release also leans hard on prompt caching, with explicit cache breakpoints and a 30-minute minimum cache life, another sign that the competition has shifted from who has the biggest model to who can serve tokens most cheaply. For anyone building on these APIs, the calculus is now as much about cost engineering as capability.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: OpenAI · Published Saturday, July 18, 2026_

## Wortins' read

OpenAI's GPT-5.6 arrived not as one model but as three, and the split says a lot about where the industry is heading. Sol is the heavyweight aimed at the hardest agentic, coding and science problems at $5 per million input tokens and $30 output. Terra sits at half that cost, and Luna drops to $1 in and $6 out for fast, high-volume work where speed and price matter more than raw reasoning. The logic is that most real workloads do not need the smartest possible model on every call. A support bot answering routine questions and a research agent planning a multi-step task have very different needs, and paying Sol rates for Luna-grade work is simply waste. By packaging tiers explicitly, OpenAI is nudging developers to route each request to the cheapest model that can handle it. The release also leans hard on prompt caching, with explicit cache breakpoints and a 30-minute minimum cache life, another sign that the competition has shifted from who has the biggest model to who can serve tokens most cheaply. For anyone building on these APIs, the calculus is now as much about cost engineering as capability.

## Source

[Read the full story at OpenAI](https://openai.com/index/gpt-5-6/)

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