GPT-6 Astra in Practice: Tests, Use Cases, and Feedback

The author argues the cut from 20x to 10x is not justified by Sol's efficiency, and that OpenAI is halving usage and steering users from Astra to Sol without passing the savings back.

A reply argues things are still bad, but community pressure did push OpenAI to release Astra within a day or two, and urges users to keep speaking out.

The author edited an AI-glasses-themed short film with GPT-6 Astra, using Krea2 for stills, H3 for video, and Suno v6 for music.

theo responds to rumors: Opus 5.5 was indeed good, but the claim that Astra 6.1 was released as Sol 6.1 due to safety concerns does not hold up.

The author advises $20-tier users to use only GPT-6.1 Sol with reasoning set to at least High, noting Sol runs very slowly and suits long overnight tasks.

Someone had GPT-6 Astra teach itself to play Geometry Dash while playing The Nightmare, and it cleared the level for the first time after 42 hours of learning; the creator explains the process in a new video.

Gemini 4 Argon ranks first on AutomationBench-AA at 77.5%, while on Terminal Bench 4 GPT-6 Astra scores 59%, behind three Claude models.

ARC Prize released a replay showing GPT-6.1 Sol completed a level in 463 actions at max reasoning versus 1,078 at low reasoning, so stronger reasoning lowered total inference cost.

On Vibe Code Bench one model built all 30 apps perfectly, Claude Opus 5 came next with 25, and GPT-6 Astra placed third with 24.

The author argues that Astra writing code is not the point; what decides success is the repo skeleton of context, router, agents, memory, eval and safety.

The author says GPT-6.1 Sol is slow but cheap and durable for daily coding and agent tasks, leaving Astra for cases that need maximum intelligence and easing Plus quota anxiety.

A DevDay launch list: GPT-6.1 Sol nears Astra performance at low cost, Astra Ultrafast reaches 300 tokens/sec in Codex, plus a $500 Pro tier, Codex Cloud and an Agents API.