GPT-6 Is a Family, Not a Single Model
OpenAI released GPT-6 as three models aimed at different workloads rather than one flagship with a fallback. Astra sits at the capability ceiling, Sol is the balanced default for coding and agentic workflows, and Luna handles focused, high frequency tasks at minimal cost. All three share the same headline specifications, so the choice comes down to reasoning depth, price per token, and how much verification each job needs.
Quick Comparison at a Glance
GPT-6 Astra delivers the strongest complex reasoning and multi step execution. It fits deep research, demanding software work, solution design, computer use and deliverables that go to other people. Its trade off is the highest API price in the family.
GPT-6 Sol balances capability and cost. It covers everyday writing, code implementation, material analysis, reports and office tasks that need some judgment, making it a sensible starting point for most routine work.
GPT-6 Luna optimizes for throughput and price. Classification, field extraction, batch summarization, short rewrites and rule based automation are its natural habitat. Reasoning depth on open ended problems should be validated per task before relying on it.
GPT-6 Astra: For Ambiguous Problems and Complete Deliverables
Astra is designed for work where the problem boundaries are unclear, several information sources must be combined, or the model has to chain many steps without losing the thread: analyze a document set, draft an implementation plan, write code, then revise from feedback. OpenAI groups its strengths under complex reasoning, software engineering, web browsing, computer use, scientific research and professional output.
One nuance from official materials: on some evaluations Astra reached better results while producing fewer output tokens. A higher unit price therefore does not automatically mean a higher total bill; actual cost depends on how much the task consumes in practice. Reach for Astra when a decision matters, a code change is risky, or the output ships to someone else. Skip it when requirements are crisp and steps are few.
GPT-6 Sol: The Default for Writing, Coding and Analysis
Sol targets complex programming and agent workflows while remaining a capable general purpose model. Building a page from a written spec, reviewing code for defects, synthesizing several documents, or producing one revision pass on a report all sit comfortably in its range. If a task grows into multiple revision rounds or demands deeper reasoning, raising the reasoning effort or switching to Astra is the natural escalation path.
GPT-6 Luna: Fast, Cheap, and Repeatable
Luna is built for jobs with clear goals, stable steps and high volume. Route tagging, extracting structured fields from text, summarizing queues of documents, or rewriting short copy are cases where its cost profile pays for itself, especially when outputs are easy to check mechanically. For open ended research, messy debugging or trade off analysis, run real samples before trusting it to hold quality.
Shared Specifications and Real Limits
All three models list a context window of roughly 1,050,000 tokens and a maximum output of about 128,000 tokens, and all accept text plus image input. Identical context capacity does not mean identical comprehension: how well each model reasons over a long codebase or a dense report still varies.
Through the API they can combine web search, file retrieval, code execution and computer use, with the available toolset depending on the model and integration configuration. Native output is text; audio and video require companion tools or dedicated models. Reasoning effort is configurable across the family: Astra supports low through max, while Sol and Luna additionally support a none setting. Higher effort raises quality but also latency and token consumption.
API Pricing: The Gap Between Tiers
Standard text pricing per million tokens:
GPT-6 Astra: $10 input, $50 output.
GPT-6 Sol: $2 input, $10 output.
GPT-6 Luna: $0.10 input, $0.50 output.
At list price, Astra costs about five times Sol, and Sol costs about twenty times Luna. Real invoices also move with prompt caching, long inputs and tool calls. These API rates are separate from ChatGPT or Codex subscription entitlements and should not be treated as interchangeable.
How to Choose in One Pass
Match the model to task difficulty: complex, high stakes, multi step work points to Astra; ordinary writing, programming and office analysis starts at Sol; clear, repetitive, batch heavy work deserves a Luna evaluation. When unsure, run the same realistic task set through two candidates and pick the cheapest configuration that still meets the quality bar. A useful split inside one project: Astra clarifies fuzzy requirements and untangles hard bugs, Sol builds the well specified pages and features, and Luna handles bulk copy tweaks and field cleanup.
Frequent Questions
Is Astra always the best choice? It is the most capable, not the most appropriate; high volume simple jobs get better economics from lower tiers. Can all three read images? Yes, image input is listed for all three, though their output remains text. Does the long context guarantee perfect recall? No, fitting more material is not the same as extracting every detail, so important conclusions should cite evidence and be spot checked.
