Openai just cut gpt-5.6 luna pricing by 80% to 45 cents per million tokens, and frontier token…
openai just cut gpt-5.6 luna pricing by 80% to 45 cents per million tokens, and frontier token prices have fallen about tenfold since the gpt-4 era. subscriptions meanwhile keep climbing toward a $200 a month ceiling.
compute deflates while access inflates, and the gap is where the margins moved.
Context
Verified date: OpenAI's post Advancing the price-performance frontier with GPT-5.6 and VentureBeat are both dated Jul 30, 2026. VentureBeat says OpenAI cut GPT-5.6 Luna, the smallest and fastest model in the series, by 80% and Terra by 20%. Yahoo Finance reports Luna's price dropped from $1 input and $6 output to $0.20 and $1.20 per million tokens.
On the long trend, Epoch AI measures how fast the price of reaching a given benchmark level has fallen, and Gravity's price history (Oct 4, 2026) says GPT-4-class tokens cost 99.8% less than in October 2023.
The 80% cut to Luna is dated Jul 30, 2026, not a new move. The reported price is $0.20 input and $1.20 output per million tokens, so the 45 cents per million figure was not seen in the sources read, so unsupported here, not refuted. The tenfold fall since the GPT-4 era is understated by Gravity, which puts GPT-4-class tokens about 652 times cheaper, though Epoch finds prices fall at different rates by capability. The $200 a month ceiling is consistent with ChatGPT Pro and Claude Max both listed at $200 a month on the Inference Price Index. 'Compute deflates while access inflates' is the author's opinion.
Related work
- Gravity: LLM API pricing history, every price drop since 2023 ↗Oct 4, 2026.
- Epoch AI: LLM inference prices have fallen rapidly but unequally ↗Mar 12, 2025.
- Inference Price Index: LLM pricing intelligence center ↗Subscription versus API break-even.
Watch next
- Read OpenAI's post for Luna's cached and batch prices.
Sources
Provenance
The note above is reproduced unedited from the original post, first published on Threads on 1 October 2026 at 12:14 IST. Sources are the papers and datasets the note draws on.
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