OpenAI launches GPT-6 Sol and Luna, pushing frontier capability into cheaper tiers
The notable shift is economic as much as technical: stronger models are becoming cheap enough for much higher-volume use.
OpenAI expanded the GPT-6 family with GPT-6 Sol and GPT-6 Luna, offering different tradeoffs between capability, speed and price.
The larger signal is continued inference-cost compression. As strong models become cheaper, workflows that once looked too expensive—long-running agents, repeated coding passes, research loops and high-volume automation—become more practical.
Why it matters
The competitive frontier is increasingly about capability per dollar, not simply who has the highest benchmark score. That can reshape software pricing, model routing and how much intelligence applications can afford to use per task.
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Jevons paradox: why cheaper often means more
After exploring Introducing GPT-6 Sol and Luna, you might wonder whether cheaper models mean less AI spending. An old economics idea says the opposite: Jevons paradox: why cheaper often means more.
The 1865 observation that more efficient coal engines increased total coal use. The same logic suggests cheaper tokens may raise total AI usage and spending, not lower it.
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