⚡ Stop treating intelligence and efficiency as separate. GPT-5.6 maximizes intelligence per token to deliver equal-or-better performance more cheaply and quickly. Title: How GPT-5.6 fuses frontier intelligence with frontier efficiency URL: ⚡ Overview GPT-5.6 is trained to optimize both task success and efficiency, taking a more direct path through tasks. OpenAI calls it their greatest intelligence-per-token efficiency yet. 🧩 Problem Solved Frontier models are smart, but reasoning tokens, latency, and cost are the wall in production. GPT-5.6 makes efficiency a first-class goal, pushing the performance-vs-cost tradeoff outward. 🛠 Methodology & Lineup ・Sol: flagship for frontier reasoning and long-horizon agentic work ・Terra: everyday balanced model, GPT-5.5-competitive at about half the cost ・Luna: fastest and cheapest (~80% less than Sol) On serving: improved speculative decoding gives 15%+ better token generation, and GPU kernel improvements cut serving cost 20%. 📊 Results On the Artificial Analysis Coding Agent Index, Sol (max reasoning) sets a new SOTA of 80, beating Fable 5 by +2.8 while using under half the output tokens, half the time, and ~1/3 less cost. On ExploitBench it matches Mythos Preview using ~1/3 of the output tokens. #GPT56# #OpenAI#

GPT6 Astra is pretty good — After a morning of intensive coding. Clear improvements over 5.6 Sol, much better frontend taste, much better engineering rigorous and much much more efficient thinking and answering.

GPT‑6 Astra - “models a house in Blender and turns it into a walkable scene in Unreal Engine 5, helping designers and clients explore the layout and experience the space before it’s built.”

Great work that measures GPT6 Astra as an Embodied Policy by Yu-Mool Shu and Lipxin Zheng. It further proves with numbers that VLA handles dexterous movement, while the General Model oversees supervision, error correction, and replanning. Interesting numbers: For the average performance across 10 tasks in RoboDojo, PI0.5 gets 24%, Astra gets 37% but together they achieves 62%. But for RoboLab, Astra itself gets 98%. As for the contribution, GPT 6 Astra is correcting only 14.4% of the executed control steps and the remaining 85.6% following the actions by PI0.5​. Link:

my takeaway from 3 days of GPT6/Astra research: it’s simultaneously far more capable and efficient than expected, yet still far from good enough. that combination should massively accelerate AI adoption, drive Jevons paradox, and expose how much work remains before these models are dependable across the full scope of real world use cases. hard to imagine a more bullish outcome for AI infrastructure.

🥒 Teaching a #reBot# Arm to slice cucumbers with #GPT6Astra—in# the real world! 🤖 Community developer @box2ai brings #LLM# agents to physical #robot# through #ROS#, In-Context Learning, and self-evolving loops—no hand-crafted control pipeline needed. 🦾 Open-source reBot Arm: