GPT 6 Luna

Supported Functionality

Item	Specification
Input	Text, images
Output	Text
Context	1,050,000 tokens
Max Output	128,000 tokens
Vision	✓ Supported
Function Calling	✓ Supported in full through the Responses API; available in Chat Completions only with reasoning_effort: none

Description

GPT-6 Luna is an efficient reasoning model released by OpenAI on September 22, 2026. Its API identifier is gpt-6-luna. It accepts text and image input, produces text, provides a 1,050,000-token context window with up to 128,000 output tokens, and has a knowledge cutoff of May 18, 2026.

The model is designed for focused, frequently repeated work at scale, including extraction, classification, triage, fine-grained editing, and routine automation. It offers six reasoning-effort levels from none through max and uses the Responses API for function calling, web search, file search, Code Interpreter, hosted shell, Computer Use, MCP, and other tools. The GPT-6 family also supports asynchronous tool calls and mid-turn steering.

Key Capabilities

• High-Volume Language Processing: Performs classification, extraction, summarization, normalization, and transformation across many items.
• Focused Problem Solving: Executes analysis, calculations, edits, and decision support within clear task boundaries.
• Adjustable Reasoning: Uses none, low, medium, high, xhigh, or max to control processing depth.
• Long-Context Understanding: Reads large reference sets, histories, code excerpts, or business documents in one request.
• Vision: Analyzes screenshots, table images, interfaces, and document pages to extract usable information.
• Structured Outputs and Function Calling: Produces constrained formats and connects to tools or business systems.
• Agent Execution: Combines search, files, code execution, Computer Use, and MCP for frequent automated workflows.

Technical Strengths

Feature	Benefit
Efficiency-Focused Design	Fits well-defined workloads that run frequently and require stable repeated execution.
1.05M Context Window	Processes large record sets, long documents, and cross-file information with less frequent slicing.
128K Output Limit	Supports long lists, batch transformations, code, and large structured responses.
Six Reasoning Levels	Keeps routine work direct while adding depth for harder but constrained problems.
Responses API Tooling	Connects natively to search, files, code execution, shell, Computer Use, MCP, and related capabilities.
Async Calls and Mid-Turn Steering	Advances independent tool tasks in parallel and accepts new priorities or constraints during execution.

Frequently Asked Questions

How should I set reasoning effort for GPT-6 Luna?

Start with none or low for classification, extraction, format conversion, and fine-grained edits. Use medium when a task coordinates several constraints or updates, then test high, xhigh, or max for harder bounded problems. A fixed evaluation set provides the best basis for selecting the level.

How do I improve consistency in high-volume GPT-6 Luna tasks?

Define every field, allowed value, counterexample, and output schema, then divide large batches into independently retryable units. Use Structured Outputs or strict function parameters and add automated checks for missing values, malformed inputs, and boundary cases.

How should I use GPT-6 Luna's long context?

Group material by source, time, or record type and distinguish instructions, evidence, and data to process. Even with a 1.05M-token window, retrieval, deduplication, and sample review remain useful because irrelevant context can reduce batch consistency.

Should I choose GPT-6 Luna or GPT-6 Sol?

Start with GPT-6 Luna for focused, frequently repeated extraction, classification, editing, and automation work. GPT-6 Sol is better aligned with complex coding, open-ended analysis, professional judgment, and demanding agent workflows; choose using production-like samples for quality, tool success, and latency.

How do I call GPT-6 Luna on LinkModel?

GPT-6 Luna is available on LinkModel. Open its live model page, copy the published model ID, and submit text or image input with the request structure currently shown there. Use the live page for the platform route, accepted parameters, and available tools instead of substituting the OpenAI API identifier.

How do I validate GPT-6 Luna on LinkModel?

Complete a basic text request with the model ID shown on LinkModel, then use a representative batch to test structured outputs, image input, long context, and tool calls. Check field consistency, truncation, errors, concurrent stability, and retry results, and confirm which options LinkModel currently exposes for reasoning_effort, asynchronous tool calls, and mid-turn steering.