Everything about GPT-6 Luna

GPT-6 Luna at a glance

GPT-6 Luna is a language model from OpenAI, released on September 22, 2026. It is a very recent release. It is a closed model: you access it through an API, from OpenAI or one of the 37 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 it costs

GPT-6 Luna costs $0.10 per million input tokens and $0.50 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 $0.003; a thousand of those tasks, $3.

That is 2.3 times cheaper than the median output price of budget models ($1.13).

Tokens read back from cache cost $0.01 per million, which helps an agent that keeps resending the same context.

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

Measured performance

According to results published by Epoch AI (checked on October 22, 2026), GPT-6 Luna has a capability index of 80.3 out of 100, computed on 5 benchmarks from our basket.

That puts it at #26 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 90.5% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 98.9% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 78.9% — the best tracked model, GPT-6 Astra, reaches 93.7%
• FrontierCode (advanced programming tasks): 42.4% — the best tracked model, Claude Opus 5.5, reaches 54.6%

What it can do

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 May 18, 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.

Who is it for?

Given its category, GPT-6 Luna is mainly aimed at high volumes where every cent counts: sorting, information extraction, summaries, simple assistants.

Its FrontierCode result (42.4%, above the median of tracked models) makes it a serious candidate for coding agents.

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

Its very low cost (under half a cent for our sample task) means it can be called thousands of times a day.

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-6 Luna are:

• GPT-5.6 Luna (OpenAI, index 79.6) — 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