The Price of Thinking Just Fell 80%. The Price of Judgment Didn't.
A managing partner asked me if he was locking in a price for something that gets cheap next year. It took three weeks.
Last month I sat across from a managing partner who’d just signed an enterprise ChatGPT agreement. He had one worry, and it wasn’t accuracy. “What if I’m locking in a price for something that gets cheap next year?”
It didn’t take a year. It took three weeks.
What actually happened
OpenAI launched its newest models, the GPT-5.6 family, on July 9. On July 30 it cut the price of Luna, the fastest and cheapest one, by 80%. Terra, the middle tier, got a 20% trim. If you were hoping the flagship, Sol, would follow, it didn’t. It stayed put.
AI pricing runs on tokens, a unit nobody asked for. A token is most of a word. That’s all the vocabulary you need. Luna now costs 20 cents per million tokens going in and $1.20 per million coming out. It was $1 and $6 three weeks ago. I read the announcement twice to make sure I had the decimal right.
And Luna isn’t a toy. OpenAI’s flagship from March scores a 51 on the Artificial Analysis index, a scoreboard the industry leans on to rank model intelligence. Luna, run at full effort, scores a 51 today. Same number. The March flagship cost $2.50 in and $15 out per million tokens. Luna, as of July 30: 20 cents and $1.20. OpenAI is now selling March’s flagship intelligence at about one-thirteenth the token price.
The subscription part
If your firm signed up for ChatGPT Enterprise or the Work plan, you never see a token bill. You see seats and usage limits. So here’s your version of the news: Terra and Luna now draw fewer credits from your plan. OpenAI confirmed the cuts flow through to subscriptions.
Your price didn’t move. Your capacity did.
The contract your firm signed in June now stretches several times further on routine work, and nobody had to negotiate a thing. I’d bet most firms won’t even notice. That’s the real waste here. Not overpaying. Under-using.
The math on one document review
Say you’ve got a matter with 500,000 documents and you want a first pass: issue tags plus a privilege screen. Call it 900 million tokens through the model, most of them going in. (Your e-discovery vendor will quote a different number. Use theirs.)
At Luna’s old prices, that run cost roughly $1,650. At the new prices, it’s about $330. That’s ballpark math, and your real number depends on document length. The direction is what matters. The cost of asking “should we run AI across this whole set?” just fell off the list of reasons to say no.
Why the price war reached your desk
This didn’t happen out of generosity. Chinese open-weight models like DeepSeek have been winning on price all year. By one industry count, they passed the American labs in traffic on OpenRouter, a big AI routing service, back in February. OpenAI needed an answer.
Luna is that answer. On input pricing, it now beats DeepSeek’s discounted rate. On output it’s still more, by about a third. Close enough to call it even.
Here’s why a lawyer should care about that comparison. Most firms I work with could never touch the Chinese models anyway. Client confidentiality rules and outside counsel guidelines shut that door before price ever came up. So cheap AI used to mean a governance headache. Now the vendor your firm already vetted charges about the same as the one it couldn’t use. The discount came to you.
The fine print
The flagship didn’t get cheaper. Hard legal reasoning, the kind you’d trust near a brief, still costs 25 times what Luna does. Cheap tokens don’t verify themselves either. A wrong answer at 20 cents is still a wrong answer, and now you can produce wrong answers at scale. Every workflow still needs a lawyer reading the output.
One more thing. A price that fell 80% three weeks after launch can move again, in either direction. Don’t build a three-year budget on this week’s rate card.
I’ll admit I keep going back and forth on whether cuts this deep can last. My old CFO instinct says margins this thin usually mean somebody’s buying market share. Maybe that’s wrong. It doesn’t change what you should do this quarter.
What to do Monday morning
Ask whoever owns your OpenAI relationship which models your plan actually routes to. If the answer is “the expensive one, for everything,” fix that first.
Re-run the numbers on any AI project your firm shelved over cost in the past year. The figure that killed it is probably wrong now.
Pick one high-volume, low-judgment task and pilot it on the cheap tier this quarter. Intake summaries are a good place to start.
The price of thinking just dropped 80%. The price of judgment didn’t. Make sure your clients are paying you for the second one.
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