GPT-5.6 Luna is a chat model from OpenAI, tracked here with measured throughput of about 118 output tokens per second and roughly 1.6 seconds to first token.
| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $0.100 | $0.600 | $0.010 | |
| $0.100 | $0.600 | $0.010 | |
| $0.200 | $1.20 | $0.020 | |
| $0.200 | $1.20 | $0.020 | |
| $0.200 | $1.20 | $0.020 |
Prices updated daily. Last check: Sep 5, 2026
Input, output, and batch rates, plus alternatives, for one provider at a time.
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
GPT-5.6 Luna fits general-purpose conversational workloads: assistants and chatbots, drafting and rewriting text, summarization, question answering over supplied context, and instruction-following tasks embedded in application backends. Its ~118 tokens per second generation rate suits streamed interfaces where users watch responses render in real time, and the sub-two-second time to first token is workable for interactive sessions. It is a weaker fit for latency-sensitive batch systems that make large numbers of very short calls, where the ~1.6-second startup cost dominates total runtime, and for workloads that depend on a confirmed very large context window or image input — capabilities we do not have verified data for. Before committing it to long-context retrieval or agentic tool-calling pipelines, confirm the relevant specifications directly with the serving provider.
Pricing depends on which provider is serving the model and on the pricing type — input tokens, output tokens, cached input, and batch rates are typically billed differently, and providers change their rates over time. Check the pricing table on this page for current per-provider figures rather than relying on any fixed number.
It is a chat model, so it targets conversational assistants, text drafting and editing, summarization, and instruction-following tasks in application backends. Its streaming rate of roughly 118 tokens per second makes it suitable for interfaces where responses render progressively for the user.
Artificial Analysis measurements record approximately 118.1 output tokens per second and a time to first token of about 1,582 milliseconds. In practice that means a short delay before the response starts, followed by generation faster than typical reading speed. Actual figures vary by provider, prompt length, and load.
We do not have a confirmed context window figure for this model in our database. Check the documentation of the specific provider you plan to use, since serving providers sometimes cap context below a model's maximum.
OpenAI models in this series are accessed through hosted APIs rather than distributed as downloadable weights, so usage is via provider endpoints. The pricing table on this page lists the providers we track for this model.