OpenAI updated several evaluation benchmarks for its GPT-6 Astra model after publishing a blog post on September 3 that experienced deployment issues and a brief retraction. ^[1] Mengqi Yuan from the XLANG Lab at the University of Hong Kong presented OSWorld 2.0, a benchmark consisting of 108 long-horizon, real-world computer-use workflows spanning 31 self-hosted websites and professional desktop applications. ^[2] An OpenAI spokesperson stated that fixes were made to the launch blog to ensure numbers represented the best estimate of available model performance for meaningful user comparisons. ^[3] Researchers from the Stanford Intelligent Systems Laboratory and Stanford Trustworthy AI Lab suggested that the rapid changes in metrics indicate 'benchmaxxing,' a practice of maximizing scores by re-running evaluations with different conditions. ^[4] The reported hallucination rate for GPT-6 Astra changed from 4.2% in early snapshots to 2% in a later version before reverting back to 4.2%. ^[5] Claude Fable 5.1, released less than 24 hours before the presentation, pushed OSWorld 2.0 partial-credit scores above 60% and binary completion above 45%. ^[6] The average task in OSWorld 2.0 requires more than 300 agent steps, and 69.6% of tasks take a skilled human over an hour to complete. ^[7] The best system evaluated in the paper completes only 20.6% of OSWorld 2.0 tasks outright, with a partial-credit score of 54.8%. ^[8]