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

The author argues GPT-6 Astra should be compared with Fable and Sol with Opus 5.5, admitting Anthropic's lower tier already pressures Astra.

OpenAI released MultiLangBench, reporting GPT-6.1 Sol lifts COBOL-to-Java migration from 5.4% to 86.6%, while GPT-6 Astra raises building a SQLite-compatible engine from scratch from 26.8% to 87.4%.

Blind-ranked design and review tasks show GPT-6.1-sol xhigh winning most at $1.59 versus Astra high at $5.03 and xhigh at $9.89; the author sees no reason for Astra outside 3D design.

New leaderboards show GPT-6 Astra (max) leading HealthBench Professional at 66.0% length-adjusted, just ahead of GPT-6.1 Sol, with OpenAI models filling the cost Pareto frontier.

Users report OpenAI is A/B testing forced model routing in Codex workflows, sending tasks picked for Astra or Sol to Luna due to compute constraints.

A poster complains OpenAI is still circling Astra and asks it to just ship the new model.

In a 1,423-vote poll on the most-wanted next model, Fable 5.5 took 50.8% and GPT-6.1 Astra 49.2%, a gap of just 23 votes.

A user asks why OpenAI's GPT-6 Astra and 6.1 Sol have much tighter usage limits than Opus 5.5 despite reportedly having more compute.

A user criticizes OpenAI Developers' event as underwhelming and hopes tomorrow brings GPT-6.1 Astra or similar models that can compete with Opus 5.5.

A new benchmark of 784 omics analysis tasks finds GPT-6 Astra at 49.3% on short-horizon work, close to Grok 4.7 and Opus 5, while GPT-5.6 Sol with Codex leads long-horizon tasks.

A question post asking which model, Opus 5.5 or GPT-6 Astra, gives users higher usage limits.

A benchmark pits Pi, OMP, Droid and Codex CLI against the same stained-glass fracture simulation prompt, with GPT-6 Astra Pro serving as the judge model.