GPT-6 Luna Pro Online | Pro Reasoning on NetRoom
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GPT-6 Luna Pro

Current version: 6.0 updated

Same GPT-6 Luna weights served with pro reasoning. 1M token context.

Category
Language
Modality
Text · Reasoning · Tool use
Context
1M
Released
Sep 2026
Run this model Estimate cost
Update

What's new in GPT-6 Luna Pro 6.0

  • 1.05M token context, up to 128K tokens in a single reply
  • Same weights as GPT-6 Luna, served with reasoning.mode = pro
  • More internal reasoning per step, so fewer dropped details on multi-step work
  • Text, image and file input, text-only output
  • Structured outputs against a JSON schema, tool calling, seed and reasoning_effort
Strengths

What it's the best tool for

  • Same weights as GPT-6 Luna, served with reasoning.mode = pro
  • 1.05M token context, up to 128K tokens in a single reply
  • More careful than base Luna when conditions chain together
  • Text, images and files in one request
  • Tool calling in three modes: auto, required and a named function
  • Structured outputs against a JSON schema, plus seed and reasoning_effort
Limitations

When to reach for something else

  • Slower than base GPT-6 Luna and burns noticeably more reasoning tokens
  • On short, unambiguous prompts it gains little over base Luna
  • Even in pro mode this is the volume tier: Sol and Astra are stronger on the hardest work
  • No temperature or top_p, so answer variety is shaped by the prompt alone
  • Text output only, and no built-in web search in chat
Sample output

How GPT-6 Luna Pro responds

Prompt
Here is the crash log from the nightly ETL run plus the pipeline config. Find the cause, explain why the retries did not help, and propose the smallest fix that closes the issue without rewriting the whole pipeline.
GPT-6 Luna Pro
The failure is not in the ETL itself but in the source extract step. 1) Three consecutive attempts in the log break on the same query: the step timeout is 30 seconds, and a full-day extract stopped fitting into it once the table grew. 2) The retries did not help because retry_delay is zero in the config: all three attempts fired within the same minute, while the source was still busy with the previous query. 3) The failure stayed silent: the task is marked failed, but it has no on_failure_callback, so no alert was sent. Smallest fix: raise the step timeout to a value that covers a full extract, set retry_delay with a growing pause between attempts, and add on_failure_callback. The pipeline does not need rewriting. Splitting the extract into intervals is worth a separate ticket if the table keeps growing.
Where teams use it

Four scenarios where it pays for itself

01
Code review
Non-obvious bugs and refactoring on a 1M-token context
02
Agent scenarios
Multi-step pipelines with reliable tool calling
03
Document work
Long reports, contracts and screenshots in one request
04
Reconciliation
Tasks where not a single condition may be dropped
About model

More about GPT-6 Luna Pro

GPT-6 Luna Pro Online — the Pro Reasoning Mode of GPT-6 Luna

GPT-6 Luna Pro is the same OpenAI model as GPT-6 Luna, served with reasoning.mode = pro. There are no separately trained Pro weights: the whole difference is how long the model thinks before it starts writing. On NetRoom both versions live in the same chat, with no VPN and no foreign card, billed in rubles.

How Pro differs from base Luna

The GPT-6 generation has three tiers: Astra as the flagship, Sol in the middle, and Luna as the volume tier built for streams of similar requests. Pro does not promote Luna a tier: it is the same Luna, with the same context window and the same feature set, simply spending more internal reasoning on every step. The difference shows where an answer depends on several linked conditions — reading unfamiliar code, a long calculation chain, checking a document against its annexes. On short, unambiguous prompts the difference is marginal: base Luna returns the same answer sooner.

Specifications

  • 1.05M token context, up to 128,000 tokens in a single reply
  • Up to 922,000 tokens go to the request itself; the rest of the window is left for reasoning and output
  • Input: text, images and files. Output is text only
  • Tool calling: auto, required, and calling a named function
  • Structured outputs against a JSON schema plus response_format
  • seed and reasoning_effort for reproducibility and depth control
  • No temperature or top_p: answer variety is shaped by the prompt

When Pro is worth it and when base Luna is enough

Pro pays off when a mistake costs more than the wait: reconstructing an incident from logs, refactoring a module, checking a contract against its annexes, running an agent that cannot go wrong halfway through the chain. For volume work — labelling thousands of tickets, tagging, field extraction, draft emails, routine support replies — take base Luna: it replies faster and does not burn tokens on long reasoning. The same task takes Pro noticeably more tokens simply because it thinks longer and writes in more detail.

Where Pro will not help

If a task keeps running into hard multi-step reasoning and non-trivial code, pro mode on the volume tier does not close that gap — that is the signal to move to Sol or GPT-6 Astra. The reply is always text: this model does not draw images or video, and the NetRoom catalogue has separate generative models for that. There is no built-in web search in chat, so fresh data has to arrive in the prompt.

Getting started

Pick GPT-6 Luna Pro from the NetRoom catalogue and type your prompt. Attach files and images directly in the dialog; long documents fit into the context in one piece. No subscription, pay-per-token billing, and no separate OpenAI account needed. If the task turns out simpler than expected, switch to base GPT-6 Luna in the same chat and keep the conversation history.

Recent changes

What changed GPT-6 Luna Pro

  • + Added text model GPT-6 Luna Pro (OpenAI).
Full changelog →
Versions

Version history of GPT-6 Luna Pro

Version Date What changed
6.0 current
  • 1.05M token context, up to 128K tokens in a single reply
  • Same weights as GPT-6 Luna, served with reasoning.mode = pro
  • More internal reasoning per step, so fewer dropped details on multi-step work
  • Text, image and file input, text-only output
  • Structured outputs against a JSON schema, tool calling, seed and reasoning_effort

Use GPT-6 Luna Pro via the API

The same engine, straight from your code: one key and one balance for text, images, video and sound. Pay only for the requests you make.

curl
curl https://netroom.ai/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/gpt-6-luna-pro", "messages": [{"role": "user", "content": "Hello"}]}'

The format is OpenAI SDK compatible — point base_url at https://netroom.ai/api/v1 and use your NetRoom key.

The model id is already in the example. The full parameter reference and prices live in GET /api/v1/models and in the docs.

API documentation Get an API key

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