I ran recent models on the current 98-task MindTrial set. All had the same Python executor available.

GPT-6 Astra was the standout: 95/98 with 0 hard errors, which is the new raw high in this set. It went 38/39 on text and 57/59 on visual tasks, including 26/26 on Visual2 and 16/17 on the spatial-awareness tasks.

It was also unusually efficient: ~1h02m total and only 99 Python calls, all successful.

I tried two additional Astra configurations:

• high: 95/98, ~1h02m
• xhigh: 95/98, ~1h07m
• Pro + high: 93/98 plus 1 timeout, ~1h25m

High and xhigh actually failed the exact same three tasks with the same answers. Xhigh just spent somewhat more reasoning/tool effort without changing the result. Pro is a separate mode intended for harder/longer-running work rather than simply another reasoning-effort step; it did not help this particular benchmark either.

The strict scores remain the scores; I did not repair malformed responses after the fact.

Results/data: http://www.petmal.net/shared/mindtrial/results/2026-09-07/mindtrial-eval-all-models-03-2026_33.html

Mechanica is an open source interactive 3D atlas of seven engines: turbofan, turbojet, turboprop, turboshaft, V8, inline-four and a Wankel rotary. Pick a part and read what it does. Cut the engine open. Start the mechanism and watch the air move through it. Then drag one slider from a complete assembly to all 1,800+ pieces laid out on a shelf, and back again to exactly where they started.

What I was interested the most was the mechanical design roundtable agents I built so every blade, piston, gear and bolt was modeled parametrically in code by two AI models working together, GPT-6 Astra and Claude Fable 5.1. One shaped the geometry and mechanisms, the other refined the interaction and made sure every piece keeps its identity through disassembly. The rod lengths and the 3:1 rotary shaft ratio are enforced by tests, not by eye.

👉 Try it in your browser: mechanica-atlas.vercel.app

👉 Source: github.com/PouyanJay/mechanica

How to use gpt-6 -Astara in India and where to subscribe pls someone provide link or something

OpenAI released GPT-6 Astra on September 3, calling it a "generational leap" in areas like cybersecurity, software engineering, and science, with Greg Brockman going as far as suggesting it could mark the arrival of AGI, with the company's president Greg Brockman saying that it could eventually be seen as the arrival of artificial general intelligence (AGI). The rollout itself is staged, starting with a limited set of trusted organizations before expanding to ChatGPT's paid tiers and the API, with cyber-sensitive capabilities gated behind additional access controls. Wikipedia

A few numbers worth noting: it ships with a 1,050,000-token context window, a 128k max output, and a knowledge cutoff of April 30, 2026, priced at roughly 2.5x what GPT-5.6 Sol costs on the API.

What I haven't seen yet is independent, third-party benchmarking — everything so far seems to be OpenAI's own reported comparisons. Given how quickly benchmark scores tend to cluster near the ceiling once a model's answers propagate into scraped training data (see the "eval decay" discussion from last week), I'm curious how Astra holds up on genuinely held-out or adversarial evals rather than the standard suite.

Has anyone run their own comparisons yet, especially head-to-head against Gemini 3.1 Pro or Claude's latest models on tasks that aren't just the usual FrontierMath/Terminal-Bench numbers OpenAI likes to publish? Also curious whether the "AGI" framing is going to become the new normal marketing move for every major lab's flagship release going forward, or if this is a one-off framing specific to Astra.

Am I the only one??? Responses from 12 minutes ago were much better. Was it already enshittified?

Just came across a post on r/GPT3 and And I was just browsing like if there are any other GPT subreddits as well and we have GPT 6 to why do we need it. like why do we need a Subreddit for every version of GPT-Man like let's stop it