GPT-6 Astra vs. Sol vs. Luna: What Changes and Which Should You Choose?
GPT-6 Astra, Sol, and Luna share a broad technical foundation but target different levels of difficulty and cost. This comparison puts their official roles, API prices, context limits, and practical tradeoffs side by side so you can match the model to the work you actually do.
GPT-6 is a family of three models rather than one model with three names. Astra targets the hardest end-to-end work, Sol aims to balance strong reasoning with everyday cost, and Luna is designed for focused tasks at high volume. They share several headline specifications, so the decision mainly turns on task difficulty, quality needs, and how frequently you will use the model.
OpenAI’s current model catalog lists GPT-6 Astra, GPT-6 Sol, and GPT-6 Luna with text and image input, text output, multilingual capability, and vision. Each has a 1.05-million-token context window and a 128,000-token maximum output. The large context does not mean every task needs all that material or that all prompt lengths cost the same.
The three models at a glance
| Model | Official positioning | Standard API price per 1M tokens: input / output | A practical starting point |
|---|---|---|---|
| GPT-6 Astra | Most capable; hardest end-to-end work | $10 / $50 | Difficult analysis, coding, and multi-step projects |
| GPT-6 Sol | Complex coding and agentic workflows | $2 / $10 | Everyday work requiring judgment; coding and agents |
| GPT-6 Luna | Efficient for focused, high-volume tasks | $0.10 / $0.50 | Repeated, scoped, easy-to-check tasks |
The prices are current Standard rates for prompts up to 272K input tokens, shown as input/output dollars per million tokens. They exclude any applicable tool fees or regional processing premium. OpenAI charges higher rates for longer prompts, and Batch and Flex processing have their own pricing. Confirm the current details on the official API pricing page before estimating a production workload.
GPT-6 Astra: capability for the hardest work
Astra is the family’s top model for work where the problem is ambiguous, the reasoning spans several steps, or the result needs to coordinate a larger project. Examples include comparing conflicting research, planning a major code migration, diagnosing a failure across multiple systems, or building a polished deliverable from a loose brief.
The tradeoff is price: at the listed Standard rates, Astra costs five times as much per token as Sol and one hundred times as much as Luna. That can be justified if it reduces retries, oversight, or errors on difficult work. OpenAI’s model guidance says Astra can perform demanding work while using substantially fewer output tokens in some evaluations, but that statement should not be treated as a guarantee of lower cost for every user task. Measure your own outcome and total effort.
GPT-6 Sol: the balanced everyday model
Sol is positioned for complex coding and agentic workflows. It is a sensible everyday choice when the task needs judgment but is not so uncertain or consequential that you need to start with the flagship. Examples include feature work in a small codebase, a research summary with a clear question, drafting an article from a brief, reviewing a spreadsheet, or planning a project with known constraints.
At one-fifth of Astra’s token price, Sol is an appealing default for a mix of quality and cost. It also supports the reasoning effort setting none, which can suit simpler API workloads, as well as higher reasoning levels for harder requests. For personal use in ChatGPT or Codex, availability and limits depend on the product and account; API list pricing alone does not tell you what a subscription includes.
GPT-6 Luna: efficiency for well-defined work
Luna is the efficient choice when the request is narrow and success is easy to verify. Think classification, extraction, concise summaries, small edits, routine code transformations, and repeated steps in an automation. At ten cents per million input tokens and fifty cents per million output tokens, it is especially economical for workloads that make many calls.
The best test is whether errors stay cheap to detect. If a task’s instructions are vague, several requirements interact, or a mistake could propagate into important decisions, consider Sol or Astra. Luna’s low token price does not by itself make it the right answer if a person must repeatedly repair the result.
What the price cut means
The current GPT-6 release notes compare new rates against previous model pricing. GPT-6 Sol’s $2 input and $10 output rates are half the listed GPT-5.6 Sol rates of $4 and $20. GPT-6 Luna’s $0.10 input is half of GPT-5.6 Luna’s $0.20; its $0.50 output is about 58.3% lower than the previous $1.20. The comparison is per million tokens, not a blanket estimate of how much every bill will fall.
API cost depends on the mixture of input, cached input, output, reasoning, tool calls, prompt length, processing tier, and retries. In practice, a cheaper model can spend more turns reaching a usable result; a more capable model can be economical if it finishes faster or needs less supervision. Track cost per accepted task to compare fairly. Batch and Flex are also priced at half of Standard rates in applicable cases, which is a separate processing-mode discount. The release notes and pricing documentation explain these conditions.
How to choose for your own work
A useful default is Sol. Move down to Luna when a workflow is routine and easy to check; move up to Astra when the goal is unclear, the stakes are high, or several dependent decisions must be made together. For Codex, for example, Luna may handle a precisely specified small edit, Sol may implement a feature and run tests, and Astra may plan an unfamiliar multi-stage migration. For research, the same pattern applies: extract facts with Luna, synthesize a defined question with Sol, and tackle an open-ended analysis with Astra.
If you have recurring tasks, create a small comparison set from real examples. Give each model the same prompt and material, then compare accuracy, completeness, latency, tokens, retries, and time spent reviewing. OpenAI’s model selection guide recommends experimenting with the same inputs and keeping the lightest setting that meets your quality bar.
Final comparison
Think of the lineup as a practical range: Luna is the efficiency option, Sol is the everyday balance, and Astra is the capability option for the hardest work. They share the same headline context and output ceilings, but their intended use and prices differ substantially. Choose by how much reasoning the task needs and how expensive an error would be, then validate the choice on your own examples. For personal ChatGPT use, check the model picker and account limits; API prices apply to metered developer access.
