# GPT-6 Astra vs Sol vs Luna: which GPT-6 model to use By SeedRouter · Published 2026-09-28 · Updated 2026-09-28 Start with GPT-6 Sol. It is OpenAI's model for complex coding and agent work, it costs a fifth of GPT-6 Astra per token, and on OpenAI's own business-workflow benchmark, Sol at xhigh effort beats Astra at low effort. Move a task to GPT-6 Astra when it is hard, long and expensive to get wrong. Use GPT-6 Luna for classification, extraction and other jobs you run thousands of times, where cost per call matters most. All three are sold on SeedRouter under one key, and switching is a change to `model`. ## How do GPT-6 Astra, Sol and Luna compare on specs? | | GPT-6 Astra | GPT-6 Sol | GPT-6 Luna | | -------------------- | --------------------------------------------- | ------------------------------------ | --------------------------------------------- | | Model ID | `gpt-6-astra` | `gpt-6-sol` | `gpt-6-luna` | | Released | September 3, 2026 | September 22, 2026 | September 22, 2026 | | OpenAI's description | Most capable, for the hardest end-to-end work | Complex coding and agentic workflows | Most efficient, for focused high-volume tasks | | Context window | 1,050,000 tokens | 1,050,000 tokens | 1,050,000 tokens | | Max input | 922,000 tokens | 922,000 tokens | 922,000 tokens | | Max output | 128,000 tokens | 128,000 tokens | 128,000 tokens | | Reasoning effort | `low` to `max` | `none` to `max` | `none` to `max` | | Default effort | Not documented | `medium` | `medium` | | Knowledge cutoff | April 30, 2026 | April 20, 2026 | May 18, 2026 | | Input / output | Text and images / text | Text and images / text | Text and images / text | The figures come from OpenAI's model pages for [GPT-6 Astra](https://developers.openai.com/api/docs/models/gpt-6-astra), [GPT-6 Sol](https://developers.openai.com/api/docs/models/gpt-6-sol) and [GPT-6 Luna](https://developers.openai.com/api/docs/models/gpt-6-luna), and the release dates from the [API changelog](https://developers.openai.com/api/docs/changelog). The main difference in the API is the lowest effort. GPT-6 Sol and GPT-6 Luna accept `none`, which answers without reasoning and is the only setting that accepts `temperature` and `top_p`. GPT-6 Astra always reasons; `none` returns a 400 error. ## Which one costs less? ### GPT-6 Astra Current rates are temporarily unavailable. No price estimate is provided; unavailable rates must not be interpreted as free usage. ### GPT-6 Sol Current rates are temporarily unavailable. No price estimate is provided; unavailable rates must not be interpreted as free usage. ### GPT-6 Luna Current rates are temporarily unavailable. No price estimate is provided; unavailable rates must not be interpreted as free usage. GPT-6 Luna is the cheapest by a wide margin, and GPT-6 Astra the most expensive. On OpenAI's list prices, GPT-6 Sol costs a fifth of GPT-6 Astra and GPT-6 Luna a twentieth of GPT-6 Sol. All three read cached input at 10% of the input rate, and a prompt over 272K input tokens moves the whole request to the long-context row. The [GPT-6 API pricing guide](https://seedrouter.ai/blog/gpt-6-api-pricing) walks through how each line is billed. ## How do they score on OpenAI's benchmarks? OpenAI published results in two announcements, [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) and [GPT-6 Sol and Luna](https://openai.com/index/introducing-gpt-6-sol-and-luna/). They were run at different effort levels, so read each row with its setting. | Benchmark (OpenAI-reported) | GPT-6 Astra | GPT-6 Sol | GPT-6 Luna | | ------------------------------------ | ----------------------------- | ---------------------------------- | ----------------------------------------------- | | AutomationBench (business workflows) | 30.3% at low effort | 33.2% at xhigh effort | Beats GPT-5.6 Luna by 5.4 points at high effort | | DeepSWE v1.1 (software engineering) | 74.1% | 68.8% at max effort | 66.6% at max effort | | OSWorld 2.0 (computer use) | 72.6% in a latency simulation | 60.5% at xhigh effort, offline set | Beats GPT-5.6 Sol (medium) at max effort | Two points stand out. On AutomationBench, GPT-6 Sol at xhigh effort scored higher than GPT-6 Astra at low effort, and OpenAI puts Astra's cost per task at 3.9 times Sol's. On software engineering and computer use, GPT-6 Astra still leads; OpenAI calls it "the world's best model for computer use". On factuality, OpenAI reports that GPT-6 Sol makes about half as many mistakes as GPT-5.6 Sol, "approaching Astra-level reliability at much lower cost", and that GPT-6 Luna at higher effort matches GPT-5.6 Sol at about a hundredth of its cost. ## When is GPT-6 Astra worth the price? Pick GPT-6 Astra when the task is long, the steps depend on each other and a mistake is expensive: large refactors, design work, research you will act on, or agents that operate a computer for many steps. OpenAI's changelog says it combines its skills "to carry complex tasks from an initial request to a finished result". For everyday coding and agent loops, GPT-6 Sol usually gives you most of that quality at a fifth of the token price, and you can raise its effort to `xhigh` or `max` before you reach for Astra. ## When is GPT-6 Luna enough? GPT-6 Luna fits jobs with a clear, narrow answer: routing tickets, tagging content, pulling fields out of documents, short summaries. Run it at `none` or `low` effort for the lowest cost and latency, and raise the effort only if accuracy on your own test set is not good enough. ## Which GPT-6 model should I use? | If you need | Use | Why | | ----------------------------------- | ----------------------- | --------------------------------------------------- | | A default for coding and agent work | GPT-6 Sol | Strong results at a fifth of Astra's price | | The best result on hard, long tasks | GPT-6 Astra | OpenAI's most capable model, best at computer use | | The lowest cost per call | GPT-6 Luna | A twentieth of Sol's price, `none` effort available | | `temperature` or `top_p` | GPT-6 Sol or GPT-6 Luna | Only accepted at `none` effort, which Astra lacks | ## Frequently asked questions ### Is GPT-6 Sol better than GPT-6 Astra? Not overall. GPT-6 Astra scores higher on OpenAI's software-engineering and computer-use results. GPT-6 Sol at xhigh effort beat GPT-6 Astra at low effort on OpenAI's business-workflow benchmark, and it costs a fifth as much per token, which is why it is the better default for most work. ### What is GPT-6 Sol? GPT-6 Sol is OpenAI's GPT-6 model for complex coding and agentic workflows, released on September 22, 2026. It sits between GPT-6 Astra and GPT-6 Luna in capability and price, with a 1.05M-token context window. ### Can I use GPT-6 Astra, Sol and Luna with the same code? Yes. All three take the same Responses and Chat Completions requests; change `model` and, for Astra, avoid `none` effort and sampling fields. ### Can I switch models in the middle of a conversation? Yes. Send the next request with a different `model` and the same message history. Reasoning from earlier turns is optional context, so the conversation continues without it. ## Try all three on one key Run the same prompt on [GPT-6 Sol](https://seedrouter.ai/models/gpt-6-sol), [GPT-6 Astra](https://seedrouter.ai/models/gpt-6-astra) and [GPT-6 Luna](https://seedrouter.ai/models/gpt-6-luna) in the browser, compare the answers and the token counts, then call the winner through the API. The [GPT-6 API guide](https://seedrouter.ai/blog/gpt-6-api) shows the request.