GPT-6 Astra vs Sol vs Luna: Which Should You Use?

GPT-6 Sol is the sensible starting point for most demanding application work. Astra is the step up for the hardest tasks; Luna is the lower-cost choice for focused, high-volume work. That is how OpenAI positions the three models, and their API prices make the trade-off unusually clear.

How do the three GPT-6 models differ?

These are published API token prices. The output price includes tokens generated by the model, so an output-heavy workload can change the total bill more than a long input does. Each model also lists a 128,000-token maximum output. OpenAI lists text and image input and text output across its latest models, with functions, web search, file search, and computer use for this family.

When should you choose Astra?

Choose Astra when a difficult, multi-step task is the bottleneck: complex reasoning, demanding coding, or an agent that must plan and complete substantial work. OpenAI describes it as its most capable model for the hardest end-to-end tasks. The price is five times Sol’s and 100 times Luna’s per token, so routing every simple request to Astra is expensive.

A useful pattern is to reserve Astra for escalation: let a lower-cost model handle routine steps and send only the genuinely difficult cases to Astra. That is an architectural choice, not a claim that all tasks will perform equally well across models.

When should you choose Sol?

Choose Sol when the work is substantial but you need a practical cost ceiling. OpenAI says it is built for complex coding and agentic workflows and recommends it as a balance of intelligence and cost. Its listed API price is one fifth of Astra’s for both input and output tokens.

For a workflow that writes code, works through a document, or uses tools across several steps, Sol is the natural first model to evaluate. Move individual steps to Astra only where the stronger model’s output changes the outcome enough to pay for the difference.

When should you choose Luna?

Choose Luna for narrow, repeatable tasks with clear inputs and outputs: extraction, classification, formatting, short summaries, or routing work to another model. OpenAI describes it as its most efficient choice for focused, high-volume tasks. Its listed API price is one twentieth of Sol’s and one hundredth of Astra’s.

At scale, that gap matters. A workload with one million input tokens and one million output tokens costs $60 on Astra, $12 on Sol, or $0.60 on Luna at the listed rates. Real bills vary with the mix of input and output tokens, tool use, and any other applicable pricing.

A simple way to decide

Start with the problem, not the model name. If a task is well defined and repeated often, begin with Luna. If it involves complicated coding, tool use, or multi-step judgment, begin with Sol. If the hardest cases still need more capability, reserve Astra for those cases. Review the output quality against your own requirements before changing the routing rule.

You can also compare any two current models side by side, including options from other companies.

Frequently asked questions

Is Astra always the best GPT-6 choice?

No. Astra is OpenAI’s most capable model, but the right choice depends on the task and cost. Sol is the stronger default for complex work under a tighter budget; Luna is the efficient option for focused, repeated tasks.

Do Astra, Sol, and Luna have different context windows?

OpenAI currently lists the same 1.05-million-token context window and 128,000-token maximum output for all three.

How much more does Astra cost than Sol?

At the listed API token rates, Astra costs five times Sol for both input and output. Sol in turn costs 20 times Luna. See OpenAI’s model catalog for current pricing before committing to a production workload.