Five days after Claude Opus 5 landed near-frontier coding at half the price,
OpenAI reached for the other lever. On July 30 it cut GPT-5.6 prices hard: Luna,
its fastest model, is now 80% cheaper, and Terra dropped 20%.
The part worth reading twice: the model helped make itself that cheap.
TL;DR: GPT-5.6 Luna is now $0.20 / $1.20 per million tokens (80% off), Terra is $2 / $12 (20% off), and Sol is unchanged at $5 / $30. OpenAI says GPT-5.6 Sol rewrote its own inference kernels to cut serving costs, then passed the savings on.
What changed (July 30)
The GPT-5.6 family has three tiers. Only the two smaller ones dropped:
| Model | Role | Input / 1M | Output / 1M | Change |
|---|---|---|---|---|
| Sol | Flagship, frontier | $5 | $30 | unchanged |
| Terra | Balanced, everyday | $2 | $12 | 20% cheaper |
| Luna | Fastest, cheapest | $0.20 | $1.20 | 80% cheaper |
For context on Luna: OpenAI says it matches performance that was frontier-class a
year ago, at roughly 6 cents on the dollar per task and nearly nine times the
speed. Replit's president called it "the closest we've come to intelligence too
cheap to meter."
How they got there (the interesting part)
Most price cuts are a margin decision. This one is mostly an engineering one, and
the engineer was partly the model itself.
OpenAI says GPT-5.6 Sol, running inside Codex, autonomously rewrote and optimized
its own production GPU kernels (in Triton and Gluon), which cut the end-to-end cost
of serving the model by 20%. It also redesigned its own speculative-decoding draft
model across hundreds of experiments, improving token-generation efficiency by more
than 15%. Cheaper to run, so cheaper to buy.
Efficiency gains stack across the harness, orchestration, and inference: less network data, less CPU work, more useful GPU output. Diagram: OpenAI.
What Sol actually scores
The capability numbers come from independent evaluations, not just OpenAI's own:
| Benchmark (independent) | GPT-5.6 Sol result |
|---|---|
| Artificial Analysis Coding Agent Index | 80, a new state of the art (about 2.8 above Claude Fable 5), using less than half the output tokens |
| Agents' Last Exam | 53.6, beating Fable 5 by 13.1 points |
| OSWorld 2.0 (computer use) | 62.6%, a new SOTA; beats Opus 4.8 while using 85% fewer output tokens |
| BrowseComp | 92.2%, a new state of the art |
Why it matters for what you build
Cost per useful task is the number that decides what you can afford to put in a
loop. When the floor drops this fast, whole categories of "too expensive to run at
scale" quietly become practical: high-volume classification, document analysis,
agent steps that retry and self-check.
This is the same story as last week, from the other direction. Opus 5 pulled
frontier quality down to half the price; GPT-5.6 is pushing the cheap end down 80%.
(I wrote up the Opus 5 side here.)
Two labs, one week, same trend: the cost of intelligence is falling faster than
most roadmaps assume.
The honest caveat
Two things to keep in mind. First, those capability scores are on independent
indexes, but the cost and speed comparisons are OpenAI's own simulations, and their
footnote says real-world results may vary substantially. Second, Sol (the frontier
tier) did not get cheaper: this cut is about making the smaller, faster models much
more affordable, not the top end.
The quick version
Sol (flagship), Terra (balanced), Luna (fast and cheap), all available now in
ChatGPT, Codex, and the OpenAI API. There is also a new Fast mode for Sol: up to
2.5x faster at twice the price, with no change in intelligence.
Sources: OpenAI's price-performance update
and the engineering deep-dive.
Diagram from OpenAI, shown for commentary.
If a model optimizing its own serving stack becomes normal, price cuts like this
get faster and more regular. Are you already picking models by cost per task, or
still by raw benchmark? Curious how you are deciding.
Top comments (1)
Luna dropping to $0.20/$1.20 per million tokens while Sol stays at $5/$30 changes the architecture question more than the headline benchmark race does. The kernel work in Triton and Gluon, plus the reported 15% gain from speculative decoding, is a useful reminder that model economics are increasingly a systems problem. I'd instrument cost per successful workflow-including retries, latency, validation, and escalation to Sol-because the cheapest token can still produce the most expensive task when routing is weak.