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OpenAI

GPT-5.6 Luna

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.

Input from
$0.100 / 1M tokens
across 2 providers

API Pricing

Cheapest on OpenAI 33% below avg
ProviderInput / 1MOutput / 1MCached / 1M
OpenAI logo
OpenAIBatch
$0.100$0.600$0.010
$0.100$0.600$0.010
$0.200$1.20$0.020
$0.200$1.20$0.020

Prices updated daily. Last check: Aug 26, 2026

GPT-5.6 Luna pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Performance & Benchmarks

Source: Artificial Analysis →
Intelligence
52.3 / 100
Coding
71.4 / 100
Output Speed
130 t/s
Latency (TTFT)
74.2s

Reasoning & Knowledge

  • GPQA Diamond91.1%
  • Humanity's Last Exam39.5%

Coding

  • SciCode52.5%

Agentic & Tool Use

  • Terminal-Bench v2.180.9%
  • τ-bench Banking31.1%

Instruction & Long Context

  • Long-Context Reasoning78.3%

Benchmarks measured Aug 2026. Scores are independent evaluations, not vendor-reported.

Model Details

General

Creator
OpenAI
Modalities
Text

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

Strengths

  • Measured output speed of approximately 118.1 tokens per second, fast enough for streaming chat interfaces where text appears faster than most users read
  • Time to first token of roughly 1.58 seconds keeps interactive turn-taking under the two-second mark
  • Built by OpenAI, so it inherits the widely supported OpenAI-style chat completions request format used by most SDKs, frameworks, and agent libraries
  • Independent performance figures sourced from Artificial Analysis rather than vendor-published claims
  • Part of the GPT-5 series line, making it straightforward to slot into existing OpenAI-targeted application code
  • Served through multiple API providers whose rates can be compared side by side in the pricing table on this page

Limitations

  • Context window size is not confirmed in our data, so maximum prompt length must be verified with the serving provider before building long-document workflows
  • We do not track confirmed multimodal input support for this model — assume text-only unless the provider documents otherwise
  • Time to first token above 1.5 seconds is a meaningful fixed cost for high-volume pipelines that issue many short, sequential requests
  • No published benchmark scores (reasoning, coding, math) are recorded in our database, making capability comparison against peers difficult without independent testing
  • As an OpenAI model, weights are not distributed for self-hosting, so deployment is limited to API access

Key Features

Chat completion API compatible with OpenAI-style request formats
Measured throughput of ~118.1 output tokens per second (Artificial Analysis)
Measured time to first token of ~1,582 ms (Artificial Analysis)
Token streaming for progressive response rendering
Part of OpenAI's GPT-5.6 generation, Luna variant
Available through hosted API providers listed in the pricing table on this page
Text generation for conversational and instruction-following workloads

About GPT-5.6 Luna

GPT-5.6 Luna is a chat-oriented large language model created by OpenAI and listed in the computeprices.com catalog as a text-generation endpoint served through API providers. It carries the GPT-5.6 version designation with the "Luna" variant name, indicating a specific configuration within OpenAI's GPT-5 series rather than a standalone architecture. The performance data we track for GPT-5.6 Luna comes from Artificial Analysis measurements: approximately 118.1 output tokens per second in sustained generation, and a time to first token of roughly 1,582 milliseconds. That combination describes a model that streams at a moderate-to-fast rate once generation begins, but has a noticeable initial latency of over a second and a half before the first token arrives — a profile common to larger models and to deployments that perform additional pre-generation work. For interactive chat, the sub-two-second first-token figure is generally acceptable; for latency-critical pipelines that fire many short requests, the fixed startup cost is the more relevant number than the streaming rate. Beyond the creator, model type, and these throughput measurements, we do not currently track confirmed details for GPT-5.6 Luna such as context window size, input modalities, tool-calling support, or published benchmark scores. Readers evaluating it against other OpenAI models or third-party alternatives should check the serving provider's own documentation for those specifications, and use the pricing table on this page to compare what individual providers charge to serve it.

Common Use Cases

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.

Frequently Asked Questions

How much does GPT-5.6 Luna cost to use?

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.

What is GPT-5.6 Luna best used for?

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.

How fast is GPT-5.6 Luna?

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.

What context window does GPT-5.6 Luna support?

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.

Can I run GPT-5.6 Luna on my own hardware?

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.