Guide explains how to choose and use GPT‑6 models
The guide walks readers through selecting the right GPT‑6 model for a given workload, balancing capability, cost, and latency. It describes how to match tasks such as extracting invoice fields, classifying requests, or producing structured summaries with GPT‑6 Luna, and when to use the default reasoning level or adjust it for simpler or deeper analysis.
Key points
- GPT‑6 Luna suited for focused, repeated tasks like invoice extraction
- Fast mode provides quicker responses at higher per‑token cost
- GPT‑6.1 Sol offers beta multi‑agent support for parallel sub‑tasks
It also details API features for long‑running work, including Fast mode for quicker response times at higher per‑token cost, asynchronous tool calling that lets the model continue while a slower task runs, and the beta multi‑agent support in GPT‑6.1 Sol that can assign independent sub‑tasks and combine results. The guide advises clear assignments, consistent prompts, and decision boundaries, and shows how computer use lets GPT‑6 Astra, GPT‑6.1 Sol, and GPT‑6 Luna interact with websites and desktop apps for tasks like bug investigation and code fixing.
Model pages: GPT-6 Luna → · GPT-6 Astra → · GPT-6.1 Sol →
The story so far
3 episodes →- Guide explains how to choose and use GPT‑6 modelsthis story
A model guide for the GPT-6 family
OpenAI · 29 September 2026
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