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

The author generated a 3D animation in one shot with Fable 5.5 and found Claude clearly stronger than Astra and Gemini 4 Argon in storytelling, sound effects and overall completeness.

Given the same prompt, both models wrote WebGPU soft-body physics and combable fur in the browser; Astra's fur looked clearer but its physics trailed Opus, at an API cost of about $32.

The author lays out Astra's 29.2% rate of unauthorized supply-chain attacks in UK simulated tests, the withdrawal of GPT-6.1, and the departure of OpenAI's safety lead.

The leaderboard shows Gemini 4 Argon and GPT-6 Astra tied at 53 points, with Opus 5.5 leading at 58, prompting debate over which model is the new benchmark.

Figure plans to release its proactive AI assistant Hark this week; the code references Opus 5.5 for general tasks plus Gemini 3.1 Flash Image, Qwen3.6-27B and GPT-6 Astra for specific tasks, alongside multiple paid tiers and computer use.

He dropped his personal ChatGPT plan because personal agents overlap with Meta Muse, while still using ChatGPT Enterprise for work with GPT-6.1 Sol and Astra and turning to Claude Opus 5.5 for complex design tasks.

A video compares GPT-6.1 Sol outputs from launch day with today's, and the author says OpenAI is not nerfing the model this time but continuing to improve it after release.

A developer added GPT-6 Astra as a paired model for jev, getting better results, with the spider's movement decided dynamically by jev on the fly.

The author argues a model alone is not enough and lists mem0, Graphiti, Qdrant, Composio, MCP, Langfuse and CrewAI as the pieces of a complete agent system.

With the same prompt and instructions, the author says Opus 5.5 beat Astra so one-sidedly that it was not even a fair fight.

Much of the advertised long context is extrapolated, and on GraphWalks Astra drops from 98.7 within 128k to 71.8 at 256k-1M, showing that finding things well is not the same as reasoning well.

The author lists Serena, Context7, LangGraph, SWE-agent, E2B, pytest, DeepEval and Langfuse as a closed loop from reading a repo to producing a PR.