What is GPT-5.6 Luna?

GPT-5.6 Luna is the lowest-cost model in OpenAI’s GPT-5.6 family, built for fast and affordable AI assistance across everyday and technical workflows. The GPT-5.6 lineup includes Sol as the flagship model, Terra as the balanced model for everyday work, and Luna as the efficient model for users who need strong capability at lower cost. Luna is intended for developers, businesses, and teams that need scalable AI for coding help, workflow automation, research support, analysis, customer-facing applications, and high-volume API usage. In the pasted preview text, Luna is presented as part of the same GPT-5.6 release process and benchmark set as Sol and Terra. It appears in evaluations for command-line coding workflows, long-horizon biology tasks, ExploitBench, and ExploitGym, indicating that it is designed to handle more than simple chat use cases. The model is priced at a lower per-token rate than Sol and Terra, making it more suitable for applications where cost efficiency is a major priority. GPT-5.6 Luna also supports the new GPT-5.6 prompt caching approach, including explicit cache breakpoints, a 30-minute minimum cache life, cache writes billed above the uncached input rate, and discounted cached-input reads. Like the rest of the GPT-5.6 family, Luna is developed with layered safeguards matched to model capability. These safeguards include trained refusals for prohibited cyber assistance, real-time misuse classifiers, paused generation for higher-risk cases, account-level review, monitoring, enforcement, automated red-teaming, and third-party human expert red-teaming. Luna is expected to support legitimate defensive and technical workflows such as code review, debugging, patch development, security education, and defensive testing while making prohibited misuse more difficult and detectable. GPT-5.6 Luna helps organizations deploy GPT-5.6-class AI where speed, affordability, scalability, and safe production use are the most important requirements.

Pricing

Price Starts At:
$0.20 per 1M tokens (input)
Price Overview:
$0.20 input / $1.20 output per 1 million tokens

Integrations

Offers API?:
Yes, GPT-5.6 Luna provides an API
All GPT-5.6 Luna Integrations
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Screenshots and Video

GPT-5.6 Luna Screenshot 1
GPT-5.6 Luna Screenshot 1 GPT-5.6 Luna Screenshot 2

Company Facts

Company Name:
OpenAI
Date Founded:
2015
Company Location:
United States
Company Website:
openai.com

Product Details

Deployment
SaaS
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

GPT-5.6 Luna Categories and Features

Large Language Models

Foundation Models

AI Vision Models

AI Reasoning Models

AI Models

AI Coding Models

GPT-5.6 Luna Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: Engineer
    Has used product for: Less than 6 months
    Uses the product: Daily
    Org Size (# of Employees): 500 - 999
    Feature Set
    Layout
    Ease Of Use
    Cost
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    Great fast model

    Date: Jul 09 2026
    Summary

    Five stars from me. GPT-5.6 Luna looks like a great fit for fast, affordable developer workflows where you need lots of useful AI assistance without paying flagship-model prices. It is the model I would use to make AI coding support feel always-on instead of expensive and occasional.

    Positive

    GPT-5.6 Luna is the one I would use when speed and cost matter most. As a developer, I can see it being really useful for lightweight coding help, quick explanations, small refactors, simple scripts, test generation, issue triage, and agent steps that do not need the most expensive model in the stack.

    I also like Luna for multi-agent systems where a lot of small tasks need to run in parallel. OpenAI describes Luna as the fastest and most cost-efficient GPT-5.6 model, and that is exactly the kind of model developers need for high-volume workflows.

    Negative

    Luna is not the model I would choose for the hardest reasoning tasks. If I were debugging a complicated distributed system, planning a major architecture change, or running a long autonomous coding agent, I would probably step up to Terra or Sol.

    It also depends heavily on good task routing. Luna can be extremely valuable when used for the right jobs, but expecting it to perform like the flagship model on every problem would be the wrong approach.

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