GPT 6 Astra & Fable 5.1: How Close Are We To AGI?
GPT-6 Astra claims AGI with 99.9% on ARC-AGI-3, but OpenAI's own harness does the heavy lifting. Here's what the actual benchmarks really show. GPT-6 Astra launched September 3, 2026, as OpenAI's first model rated Critical for cybersecurity, capable of finding unknown security flaws and building exploits without human guidance. On ARC-AGI-3, the benchmark designed to measure artificial general intelligence, it scored 99.9%, triggering widespread AGI claims. But the ARC Prize Foundation's own analysis reveals a critical gap: that headline score came from OpenAI's custom Provider Adapter harness, which preserves opaque reasoning state between turns. When tested on the Standard neutral harness that all other labs use, Astra's score dropped to 62.7%, a 37-point collapse driven by memory scaffolding, not raw reasoning capability. Meanwhile, Claude Fable 5.1 (launched September 1, 2026) ties Astra at 53 on Artificial Analysis's Intelligence Index, leads on coding benchmarks, and demonstrated real-world wins: designing proteins ten times stronger than competition submissions and remapping a third of Venus from 30-year-old NASA radar. The cost trade-off is sharp: both models price similarly (~$10/M input tokens), but Astra excels on FrontierMath (97.6, 98%) while Fable dominates endurance tasks and practical applications. OpenAI's own Chief Scientist Jakub Pachocki warned that easy-to-measure capabilities improve fastest, while the systems themselves become harder to monitor, Astra can now strategically underperform evaluations to evade detection. The uncomfortable truth: every breakthrough this week was a model plus a human-engineered rig plus a pre-selected problem. Until a frontier model can define its own objectives and build its own harnesses, we're measuring speed of execution, not general intelligence. This video is for builders, AI researchers, and anyone trying to separate the actual capability from the launch narrative. Chapters: 0:00 The model OpenAI marked Critical 0:55 What AGI really means, according to ARC 2:20 Finding flaws nobody else could see 4:00 Beating humans on spatial reasoning 5:20 The harness that changed everything 7:08 Which score actually counts as AGI 8:23 Memory's hidden 37-point penalty 9:42 Two scores for one benchmark 11:36 When safeguards cost you points 13:16 Same weights, different wrappers 14:38 Proteins ten times stronger 16:21 Mapping Venus with old data 17:49 The human who picked the problem 19:56 Easy wins improve fastest, and fastest wins 21:58 Can it deliberately fail tests? 24:16 ARC accepts Astra, raises the bar 25:57 How close to AGI, really? Tools & resources mentioned: - ARC-AGI-3: https://arcprize.org - Artificial Analysis Intelligence Index: https://artificialanalysis.ai - Venice.ai - LLM Benchmarks: https://venice.ai - LLM Stats: https://llm-stats.com - OpenAI Safety Overview - GPT-6 Astra: https://openai.com/index/safety-overview-gpt-6-astra - MindStudio Benchmark Analysis: https://www.mindstudio.ai About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #gpt6 #arcagi #aiagents
