What happened
OpenAI expanded its GPT-6 family with two new models, Sol and Luna, and cut their API rates by at least half compared to the previous generation’s promotional rates. Tokens are the small chunks of text a model reads and writes; API pricing is quoted per million of them.
GPT-6 Sol now costs $2 per million input tokens and $10 per million output tokens, down from GPT-5.6 Sol’s $4 and $20. GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens, down from GPT-5.6 Luna’s $0.20 and $1.20. OpenAI says both were trained with methods similar to GPT-6 Astra, the flagship model it introduced on September 5, but built to run cheaper and faster. Astra’s own pricing, $10 input and $50 output per million tokens, is unchanged.
The same day, GitHub added both models to GitHub Copilot’s model picker. GitHub describes Sol as “a balanced model for interactive and agentic coding” and Luna as “a lightweight, cost-efficient model for smaller, faster tasks.” Sol is available to Copilot Pro+, Max, Business, and Enterprise plans. Luna is also available to the base Pro plan. Both are billed at usage-based rates under GitHub’s model pricing, and the rollout is gradual, so an eligible account may not see the new models immediately.
Why it matters
Before this, choosing an OpenAI model in the GPT-6 family mostly meant choosing Astra, a model built for long, hard, multi-step work and priced accordingly. Sol and Luna add lower-priced options for routine edits, smaller bug fixes, and lookups. Whether they can handle a particular task with less total cost depends on token use, retries, and the corrections you need.
OpenAI positions Astra for its most demanding tasks and Sol and Luna as faster, lower-priced models. That positioning provides starting points for comparison, not a guarantee about first-attempt correctness. Check how each model performs on the work you repeat before making it a default.
Who should care
Anyone choosing a model inside GitHub Copilot’s model picker, and anyone whose coding agent or app builder connects to OpenAI’s API, the direct line software uses to send requests to OpenAI’s models instead of going through a chat app, and lets you pick which model handles the request. If your admin manages Copilot for a team, the same model policy setting that controlled Astra’s rollout controls Sol and Luna too: new models are enabled by default unless an administrator has turned that off or blocked a specific model.
What builders should do next
Don’t assume Astra is always worth the extra cost, and don’t assume Luna is always cheap enough. Pick one task you run often, such as fixing a failing test or updating a function to match a new interface. Run it three times: once each on Astra, Sol, and Luna. Use the same starting code and acceptance checks for each run. Compare follow-up corrections, failed tests, and actual usage charges, including caching and any tool costs. Treat one comparison as an initial result; repeat representative tasks before choosing a default. Higher per-token prices do not establish that an incorrect attempt will be less likely.
OpenAI’s cheaper models follow a wider industry pattern
OpenAI is following a pattern other model vendors already use: one flagship model for the hardest work, and lower-priced siblings using related training methods. The Copilot rollout puts that choice directly in front of builders who may never have compared per-token pricing before. Check the selected model and the billing rules in your tool; OpenAI’s API prices do not automatically equal Copilot’s charges.
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