Seeing what others have already created using GPT-6, it seem to be a new stepping stone in capabilities and overall "intelligence". However, some other details

Seeing what others have already created using GPT-6, it seem to be a new stepping stone in capabilities and overall "intelligence". However, some other details I think is worth bringing up is that this model is 70% more token efficient than GPT-5.6 Sol and consuming 1/3 of the tokens compared to Sol (max) in the Codex [1]. I am just thinking loudly here but, it seems like even though Astra is pricier than Sol, you might actually get more usage out of it? I did some digging myself and looking at FrontierCode and DeepSWE, Astra (low) seem to perform better than Sol (medium) and on par with Luna (max) while being somewhat on the same price range to Sol? [2][3]. And now we have four models to chose from, each with their varied reasoning efforts: Astra, Sol, Terra and Luna. Personally, I feel like Terra have turned into this middle child in a weird spot that's neither the option as cheap model because Luna is, yet it is not an good option for complex tasks because Sol is already good at it. For background, I use Luna (xhigh) daily, I think its a fantastic and underrated model. Especially Luna (max). It is way more capable than what it looks like, I think people underestimate it because OpenAI described it as "roughly corresponds to the nano model tier used in earlier GPT-5 families" [4]. I also like Luna because it barely consumes my weekly usage. Last week, it only ate ~15% of my weekly usage. So usage is not an issue anymore. I never have to worry. It may not be the fastest model because, well, it reasons as max effort, but it does the job way better than I expect. Also, considering how much one saves on the weekly usage, one can probably turn on "fast mode". Haven't done it myself though. Also, another thing that caught my eyes is this: > Historically, models have used compaction to summarize work during long sessions, such as when debugging complex issues or tackling large refactors. Each compaction can leave out details about why a fix failed or how a component behaves. > In Codex, Astra can keep notes across context windows, preserving accumulated details without repeatedly compressing them into a single summary. Earlier context windows remain searchable, so Astra can find requirements or test results from previous messages and tool outputs—even if that information wasn’t captured in its notes. You can enable this experimental feature in your Codex config.toml [5]. I was curious about this, because I know Luna (max) spews out tokens which can trigger compaction quite often. If you go to the config reference [6] and search for "features.context_management.experimental_mode", you will find this: > Enable experimental context management. Rather than repeatedly compressing context into a single summary, it uses notes and searchable history to preserve accumulated details. This is a very interesting feature and perhaps very useful during long horizon work in a thread where the conversation context window grows and compacts often. 1. https://artificialanalysis.ai/articles/benchmarking-gpt-6-astra https://artificialanalysis.ai/articles/benchmarking-gpt-6-as... 2. https://deepswe.datacurve.ai https://deepswe.datacurve.ai, Astra (low) got 67% $2.19, Sol (medium) 61% $1.42 and Luna (max) 67% $0.61 3. https://cognition.com/frontiercode https://cognition.com/frontiercode, Astra (low) 45.3% $1.60, Sol (medium) 39.9% $3.12, Luna (max) 39.8% $0.36 4. https://developers.openai.com/api/docs/models/gpt-5.6-luna https://developers.openai.com/api/docs/models/gpt-5.6-luna 5. https://openai.com/index/gpt-6-astra https://openai.com/index/gpt-6-astra 6. https://learn.chatgpt.com/docs/config-file/config-reference https://learn.chatgpt.com/docs/config-file/config-reference