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

The author walks through how to recreate the exploding-object effect with GPT-6 Astra, using a Leica M11 as the example.

The Astra agent setup uses 8 specialist seats with just one able to commit changes, and going from 271K to 273K input tokens raises an example request from about $3.11 to $6.06.

Every part, the car, track and barriers came from the Hyper3D Rodin MCP, which GPT-6 Astra called through Codex and wired to real driving controls.

A tutorial uses codex-router to set DeepSeek V4.1 Flash as a subagent while Astra plans and reviews, claiming an around-the-clock AI team for about $50 a month.

The author suggests asking the model for its knowledge cutoff and having it draw a pelican on a bicycle, and complains that degradation has repeatedly broken project code.

Set reasoning effort to Low and ask for the knowledge cutoff date; a mismatched answer suggests the model has been swapped out.

A Chinese write-up of OpenAI's guidance, plus a prompt you can paste into Codex to have it review your AGENTS.md and skills.

The post says DeepSeek V4.1 Flash scores 98% of GPT-6 Astra at 1.4% of the cost, and outlines a two-model setup for about $50 a month.

A user reports that GPT-Astra usage looks normal again, with medium reasoning running steadily and quota no longer draining unusually fast.

Running GPT-6 Astra through Martin Fowler's 66-step refactoring checklist produced spaghetti code, 0 of 66 usable steps, 2,501 reported code smells and three resets in five days.

A Chinese post lists three checks for model downgrades: ask the knowledge cutoff date, request a penguin-on-a-bicycle SVG animation, or install an extension that shows model routing and quota.

A poster criticizes a report claiming parity without Claude Fable 5.1 or GPT-6 Astra, noting it never says which models are used and cites no third-party evaluation.