AIREITER
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GPT-6 Luna

Run GPT-6 Luna online and via API for summarization, extraction, and classification at 30% of OpenAI's official token rates. 1M-token context.

输入 Token输入输出缓存读取缓存创建
≤ 271,999$0.03 每 100 万 Tokens$0.15 每 100 万 Tokens$0.003 每 100 万 Tokens$0.0375 每 100 万 Tokens
> 271,999$0.06 每 100 万 Tokens$0.225 每 100 万 Tokens$0.006 每 100 万 Tokens$0.075 每 100 万 Tokens

价格按每百万 token 计。按包含缓存读写的总输入 token 数选择档位,整次请求按该档计费。

使用 API 运行

输入

输出

Example
Generated in
10.6 seconds
输入 Token
494
输出 Token
204
Tokens per second
19.25 tokens / second
Time to first token
-

模型详情

在 Playground、API 请求和内部工作流中使用相同的模型 key。

模型 ID
gpt-6-luna
供应商
openai
协议
OpenAI Chat Completions
上下文窗口
1,050,000 Token
最大输出
128,000 Token

What You Can Do with GPT-6 Luna

GPT-6 Luna is the fast, low-cost tier of the GPT-6 family. Four things about it change how you design around it, and none of them are in the marketing copy.

Output Costs 5x What Input Costs

$0.03 per 1M input against $0.15 per 1M output. Long prompts are close to free; long answers are not. Capping max completion tokens moves your bill far more than trimming the prompt does.

A 1M Window With a Cliff at 272K

The context window is 1,050,000 tokens, but crossing 272K input tokens reprices the entire request at 2x input and 1.5x output - not just the excess. A 280K-token prompt costs more than two 140K ones.

It Reasons Before It Answers

In our runs the first streamed token took 5.0 to 5.6 seconds of a 7.2 to 7.4 second response. Streaming does not hide that gap. If you need text on screen sooner, lower the reasoning effort rather than the output cap.

Structured Output Holds Without a Schema

Asked in plain prose for four named fields as JSON, it returned exactly those four keys in a fenced block, with no schema, no function definition, and no retry. The sample above is that unedited response.

GPT-6 Luna Pricing

AIReiter prices GPT-6 Luna at 30% of OpenAI's official API rates. Rates below verified against the OpenAI price list on 23 September 2026; live per-token rates are shown above the playground.
01

Input Tokens

$0.03 per 1M tokens, against OpenAI's official $0.10.

02

Output Tokens

$0.15 per 1M tokens, against OpenAI's official $0.50.

03

Cached Input Reads

$0.003 per 1M tokens, against OpenAI's official $0.01.

04

Long-Context Tier

Requests above 272K input tokens follow OpenAI's own surcharge: 2x on input and 1.5x on output.

GPT-6 Luna Use Cases

Luna earns its place wherever per-record cost is the constraint. The figures below come from the measured run shown in the playground: 494 input and 204 output tokens per ticket.
01

Support Triage at $0.05 per 1,000 Tickets

Summary plus severity, product area, affected version, and a priority label, in one call per ticket. A 50,000-ticket backlog reprocesses for about $2.30.

02

Batch Enrichment With a Cached Prefix

Batch jobs repeat the same instructions on every row. Keep that prefix byte-identical and the repeated part reads at $0.003 per 1M instead of $0.03 - a tenth of the input cost across the whole run.

03

Default Tier Behind a Router

Send everything to Luna and escalate only what fails a check. Because Sol costs roughly 20x, a router that keeps 95% of traffic on Luna pays about a tenth of an all-Sol pipeline - the 5% that escalates is half the remaining bill.

04

Whole Documents Under the 272K Line

Reports, transcripts, and contracts fit the 1M window, but stay under 272K input tokens per request to avoid the 2x repricing. Chunking below that line is usually cheaper than one large call.

How to Use GPT-6 Luna

Luna is a volume model, so test it the way you will run it: on a real record, with the settings you can afford at scale.

01

Paste One Real Record

Use an actual ticket, transcript, or document rather than a toy prompt. Luna's failure mode is messy input, not hard reasoning.

02

Tune Down, Not Up

Start at low reasoning effort and a tight max completion tokens. Output is 5x the price of input, so the output cap is the lever that decides your bill.

03

Multiply the Token Count

Take the usage reported under the response, multiply by your daily volume, and you have the monthly cost before you write any integration code.

Build with the GPT-6 Luna API

What matters in a batch integration is not the first call but the ten-thousandth: cost per record, cache behaviour, and what happens when one row fails.

Drop-In Model ID

Point an existing OpenAI-compatible client at gpt-6-luna and change nothing else. Streaming, system prompts, and JSON-shaped output all behave as they do upstream.

Cached Prefixes Pay Off Here

Batch jobs repeat the same instructions on every row. Cached reads cost $0.003 per 1M against $0.03 for fresh input, so a stable prompt prefix is worth structuring for.

Per-Response Cost Accounting

Every response reports input, output, cached reads, and credits consumed, so a pipeline can log unit economics per record instead of discovering them on the invoice.

Escalate to GPT-6 Sol Without Re-Plumbing

Sol and Luna share one endpoint, one key, and one balance, so routing a hard row to the flagship tier is a model-ID swap, not a second integration.

GPT-6 Luna vs GPT-6 Sol vs GPT-5.6 Luna

The same support-ticket task - summarize plus extract four fields - measured on AIReiter on 23 September 2026 at 494 input and 204 output tokens, finishing in 10.6 seconds on Luna. The tiers differ by roughly a factor of twenty in price, not by whether they can do this job.
01

GPT-6 Luna - about $0.05 per 1,000 tickets

$0.03 input and $0.15 output per 1M tokens on AIReiter, against OpenAI's official $0.10 and $0.50. The cheapest GPT-6 tier, and the one that makes per-record automation arithmetic work.

02

GPT-6 Sol - about $0.91 per 1,000 tickets

$0.60 input and $3.00 output per 1M tokens, roughly 20x Luna. Worth it when a task needs flagship reasoning; wasted when the task is extraction that Luna already gets right.

03

GPT-5.6 Luna - the previous generation

Official $0.20 input and $1.20 output per 1M tokens. GPT-6 Luna halves the official input price and cuts output to under half, in the same cost-efficient slot.

04

How to choose

Run your own worst record through both tiers in the playground above. If Luna's answer holds up, the 20x gap means Sol has to earn the difference on that specific task rather than by reputation.

GPT-6 Luna FAQ

Common questions about the online playground, pricing, and API access.

/ 01

When should I choose GPT-6 Luna?

Choose Luna for high-volume, well-defined work such as summarization, extraction, and classification. Escalate to GPT-6 Sol only when a task needs flagship reasoning.

/ 02

How much does GPT-6 Luna cost on AIReiter?

AIReiter charges 30% of OpenAI's official rates: $0.03 per 1M input tokens and $0.15 per 1M output tokens, versus the official $0.10 and $0.50. Cached input reads are $0.003 per 1M.

/ 03

How does GPT-6 Luna differ from GPT-5.6 Luna?

GPT-6 Luna is the newer generation, at half the official input price and a fifth of the official output price of GPT-5.6 Luna, in the same cost-efficient tier.

/ 04

What is the context window?

GPT-6 Luna supports roughly 1M input tokens and up to 128K output tokens. Requests above 272K input tokens are billed at 2x input and 1.5x output, matching OpenAI's own policy.

/ 05

How fast is GPT-6 Luna?

On AIReiter, a summarize-and-extract request over a 494-token support ticket returned 204 output tokens in 10.6 seconds without streaming, and 7.2 to 7.4 seconds end to end with streaming. Note that Luna reasons before it answers: the first streamed token took 5.0 to 5.6 seconds across runs, so most of the wait happens before any text appears. Lower the reasoning effort if you need the response to start sooner.

/ 06

What does GPT-6 Luna cost per 1,000 records?

Using that same measured request - 494 input and 204 output tokens - about $0.05 per 1,000 tickets at AIReiter rates. The same workload on GPT-6 Sol costs roughly $0.91 per 1,000.

/ 07

Can I call GPT-6 Luna through an API?

Yes. Follow the linked API documentation and use model ID gpt-6-luna against the OpenAI-compatible endpoint.