Everything about GPT-5.6 Luna

What you need to know

GPT-5.6 Luna is a language model from OpenAI, released on July 9, 2026. That makes it 3 months old as of October 22, 2026. It is a closed model: you access it through an API, from OpenAI or one of the 49 providers listed on models.dev. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under budget models (24 models in this category on the site).

What do independent benchmarks say?

According to results published by Epoch AI (checked on October 22, 2026), GPT-5.6 Luna has a capability index of 79.6 out of 100, computed on 5 benchmarks from our basket.

That puts it at #30 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 91.6% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 98.3% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 82.1% — the best tracked model, GPT-6 Astra, reaches 93.7%
• FrontierCode (advanced programming tasks): 39.8% — the best tracked model, Claude Opus 5.5, reaches 54.6%

What it costs

GPT-5.6 Luna costs $0.20 per million input tokens and $1.2 per million output tokens (developer's price, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.006; a thousand of those tasks, $6.4.

That is in line with budget models, whose median output price is $1.13.

Tokens read back from cache cost $0.02 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Technical specifications

Its context window reaches 1,050,000 tokens, roughly 1,575 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 128,000 tokens.

It accepts as input: text, image and PDF. Its knowledge stops at February 16, 2026: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

When to choose it

Given its category, GPT-5.6 Luna is mainly aimed at high volumes where every cent counts: sorting, information extraction, summaries, simple assistants.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

What could replace it?

In the same category, the models closest to GPT-5.6 Luna are:

• GPT-6 Luna (OpenAI, index 80.3) — see the comparison
• Muse Spark 1.3 (Meta, index 82.7) — see the comparison
• GPT-5.4 nano (OpenAI, index 71.7) — see the comparison
• Qwen3.6 Flash (Alibaba Cloud (Qwen), index 64.5) — see the comparison