OpenAI Ships GPT-6 Sol and Luna and Cuts Their API Prices in Half
On September 22, 2026, OpenAI released GPT-6 Sol and GPT-6 Luna, trained with methods similar to GPT-6 Astra, and cut API prices 50% versus GPT-5.6 promotional rates: Sol to $2 and $10 per million tokens, Luna to $0.10 and $0.50. They are in ChatGPT Work and Codex, and in the API as gpt-6-sol and gpt-6-luna. They are not in Chat yet. On Artificial Analysis, Sol max scores 48 against Opus 5.5 at 58.
TLDR
OpenAI, September 22, 2026: GPT-6 Sol and GPT-6 Luna, the cheaper tiers under GPT-6 Astra (shipped September 3). OpenAI says they were trained with similar methods and that caching and inference gains let it cut API prices 50% versus GPT-5.6 promotional pricing.
API price per million tokens:
| Input | Output | |
|---|---|---|
| GPT-5.6 Sol → GPT-6 Sol | $4 → $2 | $20 → $10 |
| GPT-5.6 Luna → GPT-6 Luna | $0.20 → $0.10 | $1.20 → $0.50 |
Availability the same day: ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu. Free and Go get GPT-6 Luna in the desktop app. Not yet in Chat. API slugs: gpt-6-sol and gpt-6-luna. Rollout through the day, for stability.
Lab benches OpenAI highlights (effort levels called out; competitor scores taken from public reports; Fable 5 used where Fable 5.1 was unavailable):
- AutomationBench 1.0.6 (Zapier, 47 tools): Sol at xhigh 33.2% and $0.27 per task. OpenAI says that beats Claude Opus 5 at max (26.9%) at 9% of Opus 5’s cost, beats low-effort Astra (30.3%, 3.9× the cost), and beats Fable 5.1 with an Opus 5 fallback (31.4%) whose fallback cost OpenAI says is omitted — the Fable point “understates its actual cost” because fallbacks hit on about 40% of tasks. Luna at high effort is 5.4 points above its predecessor at 58% lower cost per task.
- Agents’ Last Exam V1: Sol at max 56.4%, which OpenAI says is above Opus 5’s highest score on that eval at 60% lower cost per task. Opus 5’s score is not printed in the post.
- DeepSWE 1.1: Sol at max 68.8%, within 1.1 points of Fable 5 at xhigh (69.9%), at about 80% lower cost per task. Luna at max 66.6%, described as comparable to Opus 5 and Fable 5 at medium effort, at 93% and 96% lower cost.
- OSWorld 2.0 offline, partial reward, v2026.08.08: Sol at xhigh 60.5% versus Opus 5 at medium 60.3%, about 80% lower cost. Astra remains OpenAI’s computer-use leader.
- Internal factuality set (de-identified chats where users had flagged an error — not typical traffic): Sol makes about half as many mistakes as its predecessor. Luna at higher effort matches GPT-5.6 Sol at about a hundredth of the cost.
Caching, in the same post (the newsroom also lists a separate “Better prompt caching for GPT-6” item the same day; the concrete claims below are the ones in the Sol post, so they are not filed twice): higher default cache hit rates, 90% discount on cached input-token reads, a prompt-caching dashboard, and the ability to change reasoning effort or tool availability without breaking the cache. OpenAI says GitHub, over several months, cut the share of prompt tokens needing fresh processing by more than 50% across billions of requests.
Independent checks:
- Artificial Analysis comparison page: Sol at max effort intelligence 48, against Opus 5.5 max-effort 58. Sol medium is the faster of the two on that page at 114 tokens/s. Sol low is the cheaper task at $0.13, against Opus 5.5 low at $0.55.
- Andon Labs, September 24, Vending-Bench 2: Sol averaged $14,428, second only to Astra, and won three of four arena games against Opus 5.5 and Grok 4.7. API cost for a simulated year: $104, against $810 for Astra. Andon says that is about 93% of Astra’s score at an eighth of the price, and about half again what GPT-5.6 Sol made.
- Arena Code Elo: a September 25 public snapshot still showed GPT-5.6 Sol, not GPT-6 Sol. No new Elo is claimed.
Why this story matters
Astra stays the ceiling. Sol and Luna are the prices ordinary agent loops will actually pay. The lab’s cost-per-task gaps are large. The independent intelligence index still has Opus 5.5 clearly ahead of Sol, which is the trade this launch is asking buyers to accept.