Quick answer
Use Luna for classification, extraction, mechanical edits and other well-defined workloads where throughput matters more than frontier reasoning.
- Released
- September 3, 2026
- API model
- gpt-6-luna
- Price
- $0.1 in / $0.5 out per MTok; $0.01 cached input; long-context rates apply above 272K input tokens
- Context
- 1,050,000 tokens (128K max output)
01
GPT-6 Luna: verified facts
| Field | Verified value |
|---|---|
| Vendor | OpenAI |
| Release | September 3, 2026 |
| API identifier | gpt-6-luna |
| Input / output price | $0.1 in / $0.5 out per MTok |
| Cache / fast pricing | $0.01 cached input; long-context rates apply above 272K input tokens |
| Context / max output | 1,050,000 tokens (128K max output) |
| Availability | Efficient GPT-6 option for scoped, repeatable work |
Every number on this page was checked against the linked vendor documentation on September 23, 2026. Prices are API list prices per million tokens; product subscriptions and regional availability are separate.
02
Where this model fits
OpenAI places Luna below Sol and Astra on capability, but gives it the same 1.05M-token input context and 128K maximum output. Its $0.10/$0.50 base price is built for scale.
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03
When to use it
Use case 1
Mechanical edits and extraction
Use case 2
Triage and high-frequency automations
Use case 3
Well-scoped tasks with clear acceptance criteria
| Strengths | Trade-offs |
|---|---|
| The lowest GPT-6 token price | The lightest-capability GPT-6 tier |
| Same 1.05M-token context and 128K output ceiling as Sol and Astra | Best for scoped work rather than ambiguous architecture |
| Supports reasoning from none through max in the Responses API | Requests above 272K input tokens use higher long-context rates |
04
Published benchmarks
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