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GPT-6 Luna

OpenAIOpenAI docs

gpt-6-luna

Run GPT-6 Luna in a hosted Python Task with structured output, web search and file inputs.

OpenAI's GPT-6 model for focused, high-volume tasks at scale, optimized for cost. Features a 1.05M token context window, 128K max output, image understanding, and the full reasoning effort range up to max, with a default of medium. Knowledge cutoff May 2026. OpenAI documents additional native tools for this family, but qbash currently exposes the verified web search path only.

What GPT-6 Luna supports in qbash

  • Structured outputReplies as JSON that matches a schema you pass.
  • Web searchSearches the web while it answers.
  • Reasoning effort6 levels, from none to max.
  • Image inputReads WEBP, PNG, JPEG and GIF images.
  • Document inputReads PDF, TEXT, DOC, XLS, RTF and 7 more files.
  • Prompt cachingRepeated input is billed at the cached rate.

What is qbash?

qbash is where you build Tasks: Python programs that run a business process on managed infrastructure. Your code runs the steps and calls a model like GPT-6 Luna only at the ones that need judgment, with integrations, memory and files available in the same script.

A developer writes the Task once, and anyone on the team runs it from a form, on a schedule, from a webhook or from chat. Each Task is versioned and every run is logged.

How Tasks work

Specifications

Context window
1.05Mtokens
Max output
128,000tokens

Structured output with GPT-6 Luna in a Python Task

qbash.ai.raw calls GPT-6 Luna from a Task. Pass a JSON Schema as output_schema and the reply comes back as a Python dict that matches it, ready to route to a connector.

main.py
ticket = qbash.inputs.get("ticket")

triage = qbash.ai.raw(
    f"Summarize this support ticket and rate its urgency:\n\n{ticket}",
    model="gpt-6-luna",
    output_schema={
        "type": "object",
        "properties": {
            "summary": {"type": "string"},
            "urgency": {"type": "string", "enum": ["low", "medium", "high"]},
        },
        "required": ["summary", "urgency"],
    },
)

# triage is a dict, e.g. {"summary": "...", "urgency": "high"}
if triage["urgency"] == "high":
    qbash.integrations.slack.create_message(
        channel="#support",
        text=triage["summary"],
    )

qbash.output(triage)
Structured output in Tasks

Use GPT-6 Luna with web search

Turn on Web Search in an inline prompt's model bar and GPT-6 Luna searches the web while it answers. Searches are billed at $10.00 per 1,000.

Provider web tools

Reasoning effort with GPT-6 Luna

Set Effort to none, low, medium, high, xhigh and max in the prompt's Settings. The default is medium. Higher effort spends more tokens on reasoning before the reply.

GPT-6 Luna pricing

Input$0.10 per 1M tokens
Output$0.50 per 1M tokens
Cached input$0.01 per 1M tokens
Cache write$0.125 per 1M tokens
Web search$10.00 per 1K searches
Input, above 272K input tokens$0.20 per 1M tokens
Output, above 272K input tokens$0.75 per 1M tokens

Standard rates in USD.

Supported file formats

Images

  • WEBP up to 20 MB
  • PNG up to 20 MB
  • JPEG up to 20 MB
  • GIF up to 20 MB

Documents

  • PDF
  • TEXT
  • DOC
  • XLS
  • RTF
  • DOCX
  • XLSX
  • PPTX
  • PPT
  • ODT
  • ODP
  • ODS

Automate GPT-6 Luna with qbash

Guides that use GPT-6 Luna