GPT-6 Luna: speed and price for high-volume work

GPT-6 Luna is the fast, economical model of OpenAI's GPT-6 series, available on Genosai since September 27, 2026. It is built for high-volume, latency-sensitive work: chatbots, classification, data extraction, and lightweight agentic tasks. It costs 8 / 40 ₽ per 1M tokens (input / output), has a 256,000-token context, and its reasoning effort can be raised for logic and code.

Updated: September 27, 2026

• 8 / 40 ₽ per 1M tokens — Half the price of GPT-5.6 Luna and 100 times cheaper than GPT-6 Astra: thousands of calls cost a few rubles.
• Reasoning effort — From instant answers to deep reasoning: none, low, medium, high, and xhigh in chat settings and in the API.
• 256,000-token context — A long document, a thread, or a catalog fits into a single request.
• Vision and tools — Accepts up to 10 JPG, PNG, or WebP images per request and supports function calling.
• Prompt caching — A repeated prefix can be read from cache at 1.6 ₽ per 1M tokens, automatically.
• OpenAI-compatible API — The gpt-6-luna model plugs into bots and scripts through the familiar chat/completions format.

Contents

• What is GPT-6 Luna
• Capabilities
• Examples prompt and response
• How to use on Genosai
• Prompts
• Generation cost
• How it compares
• Limitations and tips
• FAQ

What is GPT-6 Luna

GPT-6 Luna is the fast, economical model of OpenAI's GPT-6 series. According to the developer, it is second in performance only to GPT-6 Sol within the series, and it is built for high-volume, latency-sensitive work: chatbots, message classification, lightweight agentic workflows. When requests number in the thousands, what matters is not benchmark records but response time and the cost of each call, and Luna is tuned for exactly that.

The model shows a second side when you raise its reasoning effort. OpenAI notes that at higher reasoning levels Luna can handle complex software engineering and computer-use tasks that previously required a Sol-level model. So the same model can act as an instant classifier or as a thoughtful coding assistant, and switching between the two is a single setting.

Luna inherits the GPT-6 family's improved factual accuracy and clearer, more concise writing style. In practice this shows on short tasks: the model gets to the point, skips filler introductions, and holds the requested format well.

GPT-6 Luna has been on Genosai since September 27, 2026. It has a 256,000-token context, reads images, supports function calling, and outputs text. The price is 8 / 40 ₽ per 1M tokens (input / output), which makes it one of the most affordable models in the catalog. It runs in the browser with nothing to install.

Capabilities

High-volume calls and classification

Luna's core scenario is many similar tasks: tagging incoming messages by category, detecting the sentiment of a review, answering a routine support question, translating a short text. Each call is simple, but there are hundreds or thousands a day. Speed and predictable cost matter here, and Luna delivers both: with reasoning effort set to none, the answer arrives almost immediately.

Structured data extraction

The model reliably turns free text into structure: a customer inquiry into JSON with fields, an email into a table of dates and amounts, a product listing into a list of specifications. Thanks to the concise GPT-6 style, the answer arrives without extra commentary and is easy to pass on to a CRM or a spreadsheet. For bulk processing via the API, this is one of the best price-to-quality combinations available.

Reasoning effort for logic and code

The chat settings panel has a Reasoning effort option; in the API the parameter is reasoning_effort. Luna supports five levels: none, low, medium, high, and xhigh. The default is none, meaning instant answers without reasoning. For math, logic puzzles, and code, switch on low or medium: the model will think through the solution first and then answer. Reasoning tokens are billed as output tokens, but at Luna's price this barely affects the bill. In our test, a short math question at the high level used about 19 reasoning tokens and cost a fraction of a kopeck.

Long context

The 256,000-token window lets you pass a long contract, a meeting transcript, or an export of reviews in one piece, without splitting it up. For an economy model that is a large window: you do not need to move to an expensive model just because the input is big.

Vision and function calling

Luna accepts JPG, PNG, and WebP images, up to 10 files per request. You can pull text from a screenshot, read a photo of a receipt, or describe a diagram. Function calling (tools) lets you embed the model in agentic workflows: it decides which tool to call and with which parameters.

Examples prompt and response

Below are real GPT-6 Luna responses obtained through the Genosai API. All three requests used the default reasoning effort (none), and the answers are quoted verbatim.

Prompt: Sort these customer messages into categories: "shipping", "payment", "returns", "other". Answer as a list "number: category".
1. I paid for the order yesterday, but the status still says "unpaid".
2. The courier did not show up at the scheduled time.
3. The sneakers arrived in the wrong size, I want to send them back.
4. Do you sell gift cards?
5. When will the parcel reach Chicago?

GPT-6 Luna response:
1: payment
2: shipping
3: returns
4: other
5: shipping

_Generated on Genosai.io with GPT-6 Luna._

Prompt: Extract the data from this text into JSON with the fields name, company, phone, city, budget_usd. Missing fields should be null. Text: "Hi! My name is Laura Bennett, I am a buyer at Northwind Apparel in Denver. We are interested in a batch of down jackets, budget around 25,000 dollars. You can reach me at +1 303 555 0142."

GPT-6 Luna response:
{
  "name": "Laura Bennett",
  "company": "Northwind Apparel",
  "phone": "+1 303 555 0142",
  "city": "Denver",
  "budget_usd": 25000
}

_Generated on Genosai.io with GPT-6 Luna._

Prompt: Write a marketplace product description: travel mug, 450 ml, stainless steel, keeps drinks hot for 6 hours, leak-proof lid. 3 short sentences, no exclamation marks.

GPT-6 Luna response:
Take your favorite drinks on the go in this 450 ml stainless steel travel mug. It keeps drinks hot for up to 6 hours. The leak-proof lid helps prevent spills in your bag.

_Generated on Genosai.io with GPT-6 Luna._

Note that the model follows the format exactly and converts the budget into a number. Each of these requests took roughly 80 to 140 input tokens and 20 to 55 output tokens.

How to use on Genosai

1. Open the Genosai studio on the Text tab.
2. Pick GPT-6 Luna in the model list.
3. Optionally open the chat settings panel: temperature, top_p, maximum answer length (up to 32,768 tokens), reasoning effort, and a system prompt.
4. Write your request and attach images if needed, up to 10 files.
5. Refine the result in the same thread: ask to shorten, change the format, or add an example.

There is no subscription: you pay only for tokens. A system prompt is handy for recurring tasks: describe the role and the answer format once, then send only the data itself.

For bulk processing the API is more convenient. Genosai supports the OpenAI-compatible format: a POST https://api.genosai.io/v1/chat/completions request with the model gpt-6-luna and an sdk_... key, and GET /v1/models for the model list. Existing code written for the OpenAI library usually only needs a different base URL and key. This is how support bots, lead tagging, and data extraction from emails and documents are built.

Prompts

The prompts below play to Luna's strengths: a strict format, short structured answers, and work with a stream of data. The more precisely you define the shape of the answer, the more stable the result across thousands of requests.

Classify the sentiment of this review (positive / neutral / negative) and state the reason in one phrase. Answer in two lines.

Extract the order number, date, and amount from this email. Return JSON, with missing fields set to null.

You are a support agent for an online store. Reply to the customer politely, in no more than 3 sentences, without promising delivery dates.

Shorten this product description to 2 sentences, keeping every number and specification.

Solve step by step and give only the number at the end: a hall has 12 rows of 18 seats, and 64% of tickets are sold. How many seats are free?

For that last prompt, set reasoning effort to low or medium: on tasks with calculations it noticeably improves reliability.

Translate the text into Russian, keeping the business tone and the paragraph structure.

Describe what the attached screenshot shows and list every visible interface bug.

Generation cost

GPT-6 Luna is billed by tokens: 8 / 40 ₽ per 1M tokens (input / output). Input tokens read from cache cost 1.6 ₽ per 1M. You pay for the actual volume: a short question costs a fraction of a kopeck, and a long analysis costs proportionally more.

To get a sense of scale, take the classification example above: roughly 150 input and 25 output tokens per message. A thousand such messages come to about 150,000 input and 25,000 output tokens, or around 2 ₽ for the whole thousand. For high-volume tasks, price stops being the constraint.

Prompt caching works automatically. If the beginning of your requests repeats, such as a long instruction or a system prompt, that part may be read from cache at the reduced price. We checked this in practice: a repeated prompt of about 7,300 tokens was read entirely from cache. Cache hits are not guaranteed, but with a stable instruction at the start of the request the odds are good.

Reasoning tokens are billed as output tokens. The xhigh level can spend noticeably more tokens on a hard task than none, but even then Luna stays cheap. Current rates and your balance are in the Pricing section.

How it compares

Within the GPT-6 series the choice is simple. For volume and routine tasks, use Luna. When a hard task needs maximum depth, step up to GPT-6 Sol: it is 20 times more expensive but stronger. GPT-6 Astra is the priciest model of the series and is best used selectively.

Compared with the previous generation, Luna also wins on price: GPT-5.6 Luna costs 16 / 96 ₽, so the new model is 2 times cheaper on input and 2.4 times cheaper on output. Among economy models from other developers, compare DeepSeek V4 Flash, which has cheaper input (but pricier output) and a larger context, and Claude Haiku 4.5 at 80 / 400 ₽. All of these models live in one Genosai account, so you can run the same request through two of them and compare.

Limitations and tips

Luna has no built-in web search: it answers from its own knowledge and the data you provide. For current facts, paste the source text straight into the request. The model outputs text only; it reads images but does not create them.

On tasks with long dependent logic, a smaller model can miss some of the conditions. Before moving to a pricier model, try raising reasoning effort to medium or high, which is often enough. If that does not help, switch to Sol.

For high-volume work a strict format pays off: the number of points, field names, maximum length. Put the standing instruction into the system prompt and keep it unchanged at the start of the request, so that it hits the cache more often and costs less.

Spot-check the output. Even with GPT-6's improved accuracy, the model can occasionally be wrong, so on a pipeline it is worth reviewing a sample of answers by hand, and for important decisions (financial, legal, medical) always verify the model's output yourself.

FAQ

What is GPT-6 Luna?

It is the fast, economical model of OpenAI's GPT-6 series. According to the developer, it is second in performance only to GPT-6 Sol within the series and is designed for high-volume, latency-sensitive tasks: chatbots, classification, lightweight agentic workflows. It inherits the GPT-6 family's improved factual accuracy and clearer, more concise writing style. It has been on Genosai since September 27, 2026.

How much does GPT-6 Luna cost on Genosai?

Billing is by tokens: 8 / 40 ₽ per 1M tokens (input / output), and cached input tokens cost 1.6 ₽ per 1M. There is no subscription; you pay only for the actual size of requests and responses. A short request costs a fraction of a kopeck.

What is reasoning effort and which level should I pick?

It controls how much the model thinks before answering. Luna has five levels: none, low, medium, high, and xhigh, with none as the default for instant answers. For logic, math, and code, switch to low or medium. Reasoning tokens are billed as output tokens, but with Luna even high stays very cheap.

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

Sol is the stronger model of the series, Luna is the faster and cheaper one. Sol costs 160 / 800 ₽ per 1M tokens, so Luna is 20 times cheaper. According to OpenAI, with higher reasoning effort Luna can handle complex software engineering and computer-use tasks that previously required a Sol-level model.

Can GPT-6 Luna search the web?

No, the model has no built-in web search. It answers from its own knowledge and the data you pass in the request. If you need fresh facts, paste the source text into the message or attach screenshots.

How do I use GPT-6 Luna via the API?

Genosai offers an OpenAI-compatible API: a POST request to https://api.genosai.io/v1/chat/completions with the model gpt-6-luna and an sdk_… key in the authorization header. GET /v1/models returns the list of available models. Existing code written for the OpenAI library usually only needs a new base URL and key.

Try GPT-6 Luna on Genosai