GPT-6 Astra

GPT-6 Astra is built for computer use, coding, research, and long tool-driven workflows. It can operate browsers, edit documents, run code, connect through MCP, use hosted shell tools, and work with image-generation systems.

OpenAI reports 72.6% on OSWorld 2.0, 57.9% on Terminal-Bench 4.0, and 97.6% on FrontierMath Tier 4. Its official ARC-AGI-3 result is 99.9%, while GPT-5.6 Sol is listed at 7.8% on the same benchmark. OpenAI also reports a 1.9× faster task-completion result than GPT-5.6 Sol on Mind2Web.

The practical results are more uneven.

A long creative test produced a ray-traced water-balloon simulation after about 32 minutes. A procedural Unreal project continued for roughly four hours. It created a playable environment, but some building floors and lanterns were disconnected. A separate commercial-production task ran for 14 minutes and still required another critic pass because the first version was not coherent enough.

The model also failed at simple visual tasks. It could not correctly mark a frog in an image. In a six-image brain-scan test, it identified only one case correctly. The same session produced a detailed Alzheimer’s research layout with tables, a flowchart, and a coded figure, showing the difference between structured research work and narrow visual judgment.

Benchmark leadership is not uniform either. One hands-on comparison repeated the official coding results but noted that Astra did not rank first on every coding test. Gemini 3.8 Flash scored slightly higher on one coding benchmark. The tester said that result reduced confidence in treating any single leaderboard as a complete measure of engineering ability.

Tool choice changes the experience. Direct computer control was described as slow and token-heavy in one workflow. An MCP setup connected Astra to Blender and external image, video, and 3D-generation tools more efficiently, although those services consumed separate account credits. The model could build a playblast before generating a finished animation, but the workflow still depended on the connected tools and their limits.

Cost differences also appeared in practical work. One comparison reported a task costing about $12 with Astra versus roughly $50 for another model. In another media task, Astra took about 30 minutes while the competing system took about 50 minutes. Astra was faster and cheaper there, but the finished video assigned the wrong name to a person in one scene.

Astra works best when the goal is concrete and the output can be checked in stages. It is a strong choice for browser automation, software engineering, data transformation, structured research, and document production. It is a weaker choice for unsupervised medical interpretation, precise visual labeling, or open-ended creative work where a small error can invalidate an otherwise impressive result.

Best operating pattern: define the target, connect only the tools required, ask for intermediate checks, and review names, labels, numbers, and visual classifications before delivery.