GPT-6 Astra vs Sol vs Luna

Which GPT-6 model fits your task?

Astra is OpenAI’s choice for its hardest end-to-end work. Sol targets coding and agent workflows. Luna targets focused, high-volume tasks. Start with the model whose role matches your workload, then run the same acceptance checks on representative inputs.

At 10,000 input tokens and 2,000 billable output tokens, Standard costs $0.20 for Astra, $0.04 for Sol and $0.002 for Luna. The comparison above applies your usage to the published rates. A cheaper request does not prove that a model will complete your task in fewer attempts; include retry volume in the monthly request count.

Compare capabilities before spending

All three accept text and images, generate text, and support structured outputs and function calling. Each publishes a 1,050,000-token context window and a 128,000-token maximum output. Input and output share the context window, so a larger output reserve leaves less space for instructions, documents and tool results.

For Sol and Luna, use the Responses API for built-in tools and function calling. Chat Completions supports function calling with reasoning_effort: "none". Image support in this table describes compatibility; the cost estimate covers text tokens.

Sol and Luna offer none, low, medium, high, xhigh and max reasoning effort. Astra starts at low. Effort changes how much reasoning a task can consume, so include reasoning tokens in the billable output number rather than applying a made-up effort multiplier.

How the price comparison works

The tool subtracts cached reads from total input, bills that subset at the cache-read rate, and adds billable output. Above 272,000 input tokens, the full request uses the long-context tier: input and cache rates double, while output rates rise by 50%. Batch and Flex cost half of Standard; Fast costs twice the applicable rates.

Use the OpenAI API pricing calculator for cache-write categories and historical models. The prompt caching calculator shows when repeated prefixes recover their first write cost. For a migration from the previous generation, use the separate GPT-6 vs GPT-5 comparison.

Try a complete workflow

Read the Astra model guide, Sol model guide or Luna model guide for access, examples and model-specific prompts. Keep your own input, expected output and pass/fail checks the same when comparing models. That gives the cost difference a useful task outcome to measure against.